2011_0728_IGARSS2011_Motohka.ppt
•Download as PPT, PDF•
0 likes•240 views
This document examines the relationship between PALSAR radar backscatter data and above-ground forest biomass measurements in Japan. The study found HV polarization data had a better correlation with biomass than HH polarization. Slope correction of the radar data was also important, especially in mountainous areas, to reduce error. The results were used to create a biomass map of Japan, though the author notes improving accuracy would require additional data sources and analysis.
Report
Share
Report
Share
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
MONITORING FOREST MANAGEMENT ACTIVTIES USING AIRBORNE LIDAR AND ALOS PALSAR.pptx
This document summarizes a study that used airborne LiDAR and ALOS PALSAR satellite data to monitor forest management activities. The study used LiDAR to identify individual tree locations and heights before and after thinning. Stem volume changes were then estimated and related to changes in PALSAR backscattering coefficients. The results showed that HV polarization was more sensitive than HH to biomass changes from thinning, and the HV/HH axis rotated towards increasing HV values after thinning. Future work could utilize full polarization data and interferometry to better monitor biomass changes over larger areas.
Identification of carbon stock changes at a national level using a combinatio...
Identification of carbon stock changes at a national level using a combinatio...Luis Alberto Vega Isuhuaylas
From the event:
COP20/CMP10 UN Climate Change Conference.
Organized by the UNEP and UNFCCC COP. Lima, Peru. (December 1st – 12th 2014).
http://www.mmechanisms.org/e/cop20_japanpavilion/Agu 2013 john_armston
1. Researchers from multiple institutions brought five terrestrial laser scanners to three forest plots in Australia to take coincident scans for instrument calibration and comparison.
2. The field campaign involved scanning plots with different scanner configurations to assess sensitivity of canopy structure retrieval to instrument specifications.
3. Preliminary analysis will focus on inter-comparing estimates of leaf area index, tree properties, and biomass from single and dual-wavelength lidar as well as discrete return and full waveform data.
Carbon Stocks Estimation in South East Sulawesi Tropical Forest, Indonesia, u...
This document summarizes a study that estimated carbon stocks in a tropical forest in Southeast Sulawesi, Indonesia using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR). Two ALOS PALSAR images with a 46 day temporal baseline were processed to form a Random Volume over Ground model and optimize coherence. Field measurements of tree heights and diameters were converted to carbon stock volumes using allometric equations. The resulting forest heights and carbon stocks from PolInSAR inversion correlated positively with ground measurements.
stickler_modeling_baselines
1) The document describes a modeling system called Amazon Scenarios that is used to simulate baseline deforestation rates and potential emissions reductions from REDD+ projects in the Brazilian Amazon.
2) The model takes into account factors like protected areas, indigenous lands, roads, soils and historical deforestation patterns to generate high and low estimates of business-as-usual deforestation under different policy scenarios.
3) Results from the Amazon Scenarios model have been used to estimate baseline deforestation and emissions reductions for several specific REDD+ projects in Brazil, including in the states of Amazonas, Rondonia, Acre, and Mato Grosso.
Moving ahead: What the climate convention can do to support conservation and ...
Presented by Louis Verchot at the Bonn Climate Change Conference on 11 May 2017, at a side event titled 'Re-discovering the magnificent carbon storage potential of wetlands and peatlands'.
LiDAR Technology & IT’s Application on Forestry
LiDAR technology and its application in forestry. Three papers are summarized that examine the use of airborne LiDAR data in characterizing forest structure. Paper 1 estimates indices of structural diversity like tree height and diameter variation across a study area in Italy. Paper 2 presents a method to characterize forest ecological structure in Tasmania using different LiDAR pulse returns. Paper 3 develops new methods to estimate tree-level biomass using metrics from LiDAR point clouds, improving accuracy of biomass estimates. Overall, the studies find LiDAR useful for quantifying forest structural complexity at multiple scales in an efficient manner.
Dozier UCLA 2017-04-10
This document discusses albedo, which is a measure of how much solar radiation is reflected by a surface. It begins by defining albedo and noting how it varies with factors like sun angle and snow grain size. It explains that small changes in albedo can have large impacts on the climate system by altering how much solar energy is absorbed. The document then discusses methods for measuring and modeling albedo remotely using imaging spectrometers and how light-absorbing impurities affect snow albedo.
Recommended
MONITORING FOREST MANAGEMENT ACTIVTIES USING AIRBORNE LIDAR AND ALOS PALSAR.pptx
This document summarizes a study that used airborne LiDAR and ALOS PALSAR satellite data to monitor forest management activities. The study used LiDAR to identify individual tree locations and heights before and after thinning. Stem volume changes were then estimated and related to changes in PALSAR backscattering coefficients. The results showed that HV polarization was more sensitive than HH to biomass changes from thinning, and the HV/HH axis rotated towards increasing HV values after thinning. Future work could utilize full polarization data and interferometry to better monitor biomass changes over larger areas.
Identification of carbon stock changes at a national level using a combinatio...
Identification of carbon stock changes at a national level using a combinatio...Luis Alberto Vega Isuhuaylas
From the event:
COP20/CMP10 UN Climate Change Conference.
Organized by the UNEP and UNFCCC COP. Lima, Peru. (December 1st – 12th 2014).
http://www.mmechanisms.org/e/cop20_japanpavilion/Agu 2013 john_armston
1. Researchers from multiple institutions brought five terrestrial laser scanners to three forest plots in Australia to take coincident scans for instrument calibration and comparison.
2. The field campaign involved scanning plots with different scanner configurations to assess sensitivity of canopy structure retrieval to instrument specifications.
3. Preliminary analysis will focus on inter-comparing estimates of leaf area index, tree properties, and biomass from single and dual-wavelength lidar as well as discrete return and full waveform data.
Carbon Stocks Estimation in South East Sulawesi Tropical Forest, Indonesia, u...
This document summarizes a study that estimated carbon stocks in a tropical forest in Southeast Sulawesi, Indonesia using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR). Two ALOS PALSAR images with a 46 day temporal baseline were processed to form a Random Volume over Ground model and optimize coherence. Field measurements of tree heights and diameters were converted to carbon stock volumes using allometric equations. The resulting forest heights and carbon stocks from PolInSAR inversion correlated positively with ground measurements.
stickler_modeling_baselines
1) The document describes a modeling system called Amazon Scenarios that is used to simulate baseline deforestation rates and potential emissions reductions from REDD+ projects in the Brazilian Amazon.
2) The model takes into account factors like protected areas, indigenous lands, roads, soils and historical deforestation patterns to generate high and low estimates of business-as-usual deforestation under different policy scenarios.
3) Results from the Amazon Scenarios model have been used to estimate baseline deforestation and emissions reductions for several specific REDD+ projects in Brazil, including in the states of Amazonas, Rondonia, Acre, and Mato Grosso.
Moving ahead: What the climate convention can do to support conservation and ...
Presented by Louis Verchot at the Bonn Climate Change Conference on 11 May 2017, at a side event titled 'Re-discovering the magnificent carbon storage potential of wetlands and peatlands'.
LiDAR Technology & IT’s Application on Forestry
LiDAR technology and its application in forestry. Three papers are summarized that examine the use of airborne LiDAR data in characterizing forest structure. Paper 1 estimates indices of structural diversity like tree height and diameter variation across a study area in Italy. Paper 2 presents a method to characterize forest ecological structure in Tasmania using different LiDAR pulse returns. Paper 3 develops new methods to estimate tree-level biomass using metrics from LiDAR point clouds, improving accuracy of biomass estimates. Overall, the studies find LiDAR useful for quantifying forest structural complexity at multiple scales in an efficient manner.
Dozier UCLA 2017-04-10
This document discusses albedo, which is a measure of how much solar radiation is reflected by a surface. It begins by defining albedo and noting how it varies with factors like sun angle and snow grain size. It explains that small changes in albedo can have large impacts on the climate system by altering how much solar energy is absorbed. The document then discusses methods for measuring and modeling albedo remotely using imaging spectrometers and how light-absorbing impurities affect snow albedo.
Molinier - Feature Selection for Tree Species Identification in Very High res...
This document summarizes a study that used feature selection and classification methods to identify tree species in high-resolution satellite images. The researchers tested 35 features on over 1000 ground reference samples to rank their effectiveness for classification. They found that 6 spectral features performed best when used in a 5-nearest neighbor classifier, achieving over 80% accuracy for tree species identification. While species proportions were estimated accurately, stem numbers per species showed only moderate correlation with field data. Future work could explore more advanced classifiers, cross-validation, and improving stem number estimation.
TU4.L09 - RETRIEVAL OF SOIL MOISTURE UNDER VEGETATION USING POLARIMETRIC SCAT...
The document proposes a technique using polarimetric scattering cubes (PSC) to simultaneously invert soil moisture, surface roughness, and vegetation water content from polarimetric SAR data. The technique generates cubes of backscattering cross sections from a discrete scatterer model for different vegetation types. Measured data is transformed and compared to the cubes to determine the variables that minimize the distance. The technique has advantages over decomposition and allows inclusion of attenuation effects. Validation using experimental radar data is still needed to validate the underlying scattering models.
BioSAR2010-aSARcampaigninsupportofthebiomassmission.ppt
The document summarizes the BioSAR 2010 airborne SAR campaign in Sweden which collected P-band and L-band SAR data using the ONERA SETHI system. The objectives were to detect forest changes, cross-calibrate sensors, evaluate biomass retrieval algorithms, and assess long-term coherence. SAR data was collected over a 1200 hectare forest test site with stands of varying biomass. Helicopter lidar and extensive field data were also collected. Preliminary analysis showed the SAR data delineated clear-cuts and subtle changes due to thinning and growth compared to a 2007 dataset.
Stickler_modeling baselines_final
1) The document describes a modeling system used to simulate spatially explicit baselines of deforestation and forest degradation in the Brazilian Amazon to support REDD projects.
2) The model is calibrated using historical deforestation patterns and considers factors like protected areas, infrastructure, and soil quality. It generates baselines at different resolutions from 100m to 1km.
3) The model allows simulation of different policy scenarios like full enforcement of forest code or state-level zoning plans to estimate reduced emissions and carbon stocks compared to business-as-usual baselines.
CLASSIFICATION OF TYPHOON-DESTROYED FORESTS BASED ON TREE HEIGHT CHANGE DETEC...
1. The document analyzes data from an airborne SAR system called Pi-SAR to classify forests damaged by Typhoon Songda in 2004 using tree height change detection.
2. Amplitude analysis of Pi-SAR data before and after the typhoon achieved 64.1% accuracy in identifying damaged sites. Three-component decomposition analysis achieved 77.7% accuracy.
3. Interferometric SAR processing showed potential for quantitative height change detection between images, but phase unwrapping issues remain and coherence analysis had difficulty distinguishing damaged areas. Fusion with optical data is proposed for future work.
IGARSS2011_Gustavsson_v3.ppt
The document summarizes an experiment to detect wind-thrown trees in VHF and UHF SAR images. It finds that VHF SAR (CARABAS-II) could detect trees over 15cm in diameter if the trunk direction deviated less than 22 degrees from the flight heading, while UHF SAR (LORA) could detect all trees with a deviation under 22 degrees, regardless of size. LORA images provided more distinct detections of trees due to higher resolution. Comparing pre-and post-storm SAR images could likely improve detection of wind-thrown forests using change detection algorithms.
2013 ASPRS Track, Ozone Modeling for the Contiguous United States by Michael ...
This document summarizes modeling methods for ground-level ozone concentrations in the contiguous United States. It describes four modeling methods tested: inverse distance weighting (IDW), ordinary kriging, generalized linear models (GLM), and geographically weighted regression (GWR). IDW and kriging account for spatial autocorrelation in the data. GLM and GWR use solar radiation and relative humidity as predictor variables. Kriging and GWR had the lowest errors when validated against new data points, though all models have limitations due to the characteristics and amount of input data. The document emphasizes that statistical models are abstractions of reality and should adhere to principles like parsimony.
Compact Polarimetry Potentials.ppt
The document discusses compact polarimetry for SAR systems. It defines compact polarimetry as using one transmit polarization and two receive polarizations. This allows for larger swaths compared to full polarimetry. Calibration of a compact polarimetry system requires three reference targets and has fewer unknown parameters than a full polarimetry system. Simulations show compact polarimetry data can be accurately reconstructed from full polarimetry raw data. Analysis of biomass estimation using compact polarimetry data found errors similar to using actual full polarimetry polarization channels.
4 chenIGARSS_presentation.pptx
This study investigates the scale effect of the relationship between the normalized difference vegetation index (NDVI) and land surface temperature (T) and improves a thermal sharpening method called TsHARP. The study finds that the slope of the NDVI-T relationship increases more significantly with spatial extent than with spatial resolution alone. An improved TsHARP method is developed that establishes the NDVI-T regression relationship based on the spatial extent of individual thermal pixels, rather than the entire image extent. Testing shows the improved method produces a sharper and more accurate thermal sharpening result compared to the original TsHARP method.
POLCAL_shimada.pptx
This document proposes a new polarimetric calibration method for SAR using forest and surface scattering targets. It models forest backscatter as consisting of volume, double bounce, and surface scattering. Two steps are used to determine channel imbalances and forest parameters: first using forest and a corner reflector, then estimating cross-talks. The method was evaluated using 26 Amazon datasets and showed stable parameters and good polarimetric signatures after calibration. Reflection symmetry was confirmed for forests.
Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR
This document summarizes a research project using LiDAR data and permanent sample plot (PSP) field data to generate models for estimating forest attributes. The following steps were completed: 1) LiDAR data was acquired and processed, 2) LiDAR metrics were extracted for PSP locations, 3) models were developed in R relating forest attributes (average height, basal area, biomass) to LiDAR metrics, 4) models were applied to a larger LiDAR dataset, and 5) results were assessed. Models achieved R-squared values from 0.73-0.80 and error margins from 10-28% depending on the attribute. The document discusses limitations and conclusions.
ASSESSING THE LIMITATIONS AND CAPABILITIES OF LIDAR AND LANDSAT 8 TO ESTIMATE...
Remote sensing based quantification of semiarid rangeland vegetation provides
the large scale observations required for monitoring native plant distribution, estimating
fuel loads, modeling climate and hydrological dynamics, and measuring carbon storage.
Fine scale 3-dimensional vertical structural information from airborne lidar and improved
signal to noise ratio and radiometric resolution of recent satellite imagery provide
opportunities for refined measurements of vegetation structure.
In this study, we leverage a large number of time series Landsat 8 vegetation
indices and lidar point cloud - based vegetation metrics with ground validation for scaling
aboveground shrub and herb biomass and cover from small scale plot to large, regional
scales in the Morley Nelson Snake River Birds of Prey National Conservation Area
(NCA), Idaho. The Landsat vegetation indices were trained and linked to in-situ
measurements (n = 141) with the random forest regression to impute vegetation biomass
and cover across the NCA. We also validated our model with an independent dataset (n =
44), explaining up to 63% and 53% of variation in shrub cover and biomass, respectively.
Forty six of the in-situ plots were used in a model to compare the performance of lidar
and Landsat data in estimating vegetation characteristics. Our results demonstrate that
Landsat performs better in estimating both herb (R2 ~ 0.60) and shrub cover (R2 ~ 0.75)
whereas lidar performs better in estimating shrub and total biomass (R2 ~ 0.75 and 0.68,
respectively). Using the lidar only model, we demonstrate that lidar metrics based on
shrub height have a strong correlation with field-measured shrub biomass (R2 ~ 0.76).
vii
We also compare processing the lidar data with raster-based and point cloud-based
approaches. The results are scale-dependent, with improved results of biomass estimation
at coarser scales with point cloud processing. Overall, the results of this study indicate
that Landsat and lidar can be efficiently utilized independently and together to estimate
biomass and cover of vegetation in this semi-arid rangeland environment.
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Matías Bosio, from PASCHACO - Argentina, in FAO Hq, Rome
061018 Sea Wi Fs Work
The document discusses the challenge of characterizing particulate matter using remote sensing data due to the complex and multidimensional nature of aerosols. It presents results from using SeaWiFS satellite data combined with surface observations to characterize aerosols over the US from 2000-2003. Specific cases studied include quantifying smoke emissions from agricultural fires in Kansas in 2003 by analyzing the optical thickness and shape of smoke plumes. Summer climatologies of aerosol optical thickness over the US from 2000-2004 are also shown.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
cartus_TH4.T02.3.ppt
1. The document evaluates data fusion strategies for measuring ecosystem structure in central Chile using small-footprint lidar, ALOS PALSAR radar, and Landsat data.
2. Lidar data allowed accurate retrieval of growing stock volume and canopy height for plantations, with errors of 23% and 1.7 meters respectively.
3. Fusion of multi-temporal ALOS and Landsat data with lidar measurements improved retrieval accuracy compared to using the radar and optical data alone. Combining data sources was found to be beneficial for measuring forest structure over large areas.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
This document analyzes the use of Lyapunov exponents to determine chaotic structure in hyperspectral satellite data. It investigates an EO-1 Hyperion hyperspectral image of a mixed forest site in Turkey. Lyapunov exponents are calculated from reconstructed phase spaces of spectral signals for different object classes. Positive and negative Lyapunov exponents indicate chaotic behavior is present. The results demonstrate Lyapunov exponents can be used as discriminative features to improve hyperspectral image classification accuracy by capturing the chaotic structures in the data.
Presentation 20220316 nandia
This document outlines a study to estimate above-ground biomass and carbon stock in boreal forests in Mongolia using satellite data and machine learning. Boreal forests cover about 9.2% of Mongolia but have been declining in recent decades. The study aims to develop a suitable machine learning model to map forest biomass and carbon stock. Random forest was the best performing model with an R2 of 0.24 and RMSE of 33 Mg/ha. Important input features included shortwave infrared band 1, green leaf index, and radar polarization data. The predicted forest biomass ranged from 32.5-122.5 Mg/ha and carbon stock ranged from 16.5-62.5 Mg C/ha. Some reference
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Journal.pone.0008267
1) A study compared carbon storage and wildlife diversity between a sustainably managed forest (Deramakot) and a conventionally logged forest (Tangkulap) in Malaysian Borneo.
2) Carbon storage was 54 Mg C ha-1 higher in the sustainably managed forest, which had less degraded forest stands.
3) Wildlife diversity, as measured by camera traps, was higher in the sustainably managed forest, with 6 threatened or near-threatened species showing higher frequencies.
4) The results suggest that sustainable forest management can provide co-benefits of increased carbon storage and wildlife diversity compared to conventional logging practices.
More Related Content
What's hot
Molinier - Feature Selection for Tree Species Identification in Very High res...
This document summarizes a study that used feature selection and classification methods to identify tree species in high-resolution satellite images. The researchers tested 35 features on over 1000 ground reference samples to rank their effectiveness for classification. They found that 6 spectral features performed best when used in a 5-nearest neighbor classifier, achieving over 80% accuracy for tree species identification. While species proportions were estimated accurately, stem numbers per species showed only moderate correlation with field data. Future work could explore more advanced classifiers, cross-validation, and improving stem number estimation.
TU4.L09 - RETRIEVAL OF SOIL MOISTURE UNDER VEGETATION USING POLARIMETRIC SCAT...
The document proposes a technique using polarimetric scattering cubes (PSC) to simultaneously invert soil moisture, surface roughness, and vegetation water content from polarimetric SAR data. The technique generates cubes of backscattering cross sections from a discrete scatterer model for different vegetation types. Measured data is transformed and compared to the cubes to determine the variables that minimize the distance. The technique has advantages over decomposition and allows inclusion of attenuation effects. Validation using experimental radar data is still needed to validate the underlying scattering models.
BioSAR2010-aSARcampaigninsupportofthebiomassmission.ppt
The document summarizes the BioSAR 2010 airborne SAR campaign in Sweden which collected P-band and L-band SAR data using the ONERA SETHI system. The objectives were to detect forest changes, cross-calibrate sensors, evaluate biomass retrieval algorithms, and assess long-term coherence. SAR data was collected over a 1200 hectare forest test site with stands of varying biomass. Helicopter lidar and extensive field data were also collected. Preliminary analysis showed the SAR data delineated clear-cuts and subtle changes due to thinning and growth compared to a 2007 dataset.
Stickler_modeling baselines_final
1) The document describes a modeling system used to simulate spatially explicit baselines of deforestation and forest degradation in the Brazilian Amazon to support REDD projects.
2) The model is calibrated using historical deforestation patterns and considers factors like protected areas, infrastructure, and soil quality. It generates baselines at different resolutions from 100m to 1km.
3) The model allows simulation of different policy scenarios like full enforcement of forest code or state-level zoning plans to estimate reduced emissions and carbon stocks compared to business-as-usual baselines.
CLASSIFICATION OF TYPHOON-DESTROYED FORESTS BASED ON TREE HEIGHT CHANGE DETEC...
1. The document analyzes data from an airborne SAR system called Pi-SAR to classify forests damaged by Typhoon Songda in 2004 using tree height change detection.
2. Amplitude analysis of Pi-SAR data before and after the typhoon achieved 64.1% accuracy in identifying damaged sites. Three-component decomposition analysis achieved 77.7% accuracy.
3. Interferometric SAR processing showed potential for quantitative height change detection between images, but phase unwrapping issues remain and coherence analysis had difficulty distinguishing damaged areas. Fusion with optical data is proposed for future work.
IGARSS2011_Gustavsson_v3.ppt
The document summarizes an experiment to detect wind-thrown trees in VHF and UHF SAR images. It finds that VHF SAR (CARABAS-II) could detect trees over 15cm in diameter if the trunk direction deviated less than 22 degrees from the flight heading, while UHF SAR (LORA) could detect all trees with a deviation under 22 degrees, regardless of size. LORA images provided more distinct detections of trees due to higher resolution. Comparing pre-and post-storm SAR images could likely improve detection of wind-thrown forests using change detection algorithms.
2013 ASPRS Track, Ozone Modeling for the Contiguous United States by Michael ...
This document summarizes modeling methods for ground-level ozone concentrations in the contiguous United States. It describes four modeling methods tested: inverse distance weighting (IDW), ordinary kriging, generalized linear models (GLM), and geographically weighted regression (GWR). IDW and kriging account for spatial autocorrelation in the data. GLM and GWR use solar radiation and relative humidity as predictor variables. Kriging and GWR had the lowest errors when validated against new data points, though all models have limitations due to the characteristics and amount of input data. The document emphasizes that statistical models are abstractions of reality and should adhere to principles like parsimony.
Compact Polarimetry Potentials.ppt
The document discusses compact polarimetry for SAR systems. It defines compact polarimetry as using one transmit polarization and two receive polarizations. This allows for larger swaths compared to full polarimetry. Calibration of a compact polarimetry system requires three reference targets and has fewer unknown parameters than a full polarimetry system. Simulations show compact polarimetry data can be accurately reconstructed from full polarimetry raw data. Analysis of biomass estimation using compact polarimetry data found errors similar to using actual full polarimetry polarization channels.
4 chenIGARSS_presentation.pptx
This study investigates the scale effect of the relationship between the normalized difference vegetation index (NDVI) and land surface temperature (T) and improves a thermal sharpening method called TsHARP. The study finds that the slope of the NDVI-T relationship increases more significantly with spatial extent than with spatial resolution alone. An improved TsHARP method is developed that establishes the NDVI-T regression relationship based on the spatial extent of individual thermal pixels, rather than the entire image extent. Testing shows the improved method produces a sharper and more accurate thermal sharpening result compared to the original TsHARP method.
POLCAL_shimada.pptx
This document proposes a new polarimetric calibration method for SAR using forest and surface scattering targets. It models forest backscatter as consisting of volume, double bounce, and surface scattering. Two steps are used to determine channel imbalances and forest parameters: first using forest and a corner reflector, then estimating cross-talks. The method was evaluated using 26 Amazon datasets and showed stable parameters and good polarimetric signatures after calibration. Reflection symmetry was confirmed for forests.
Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR
This document summarizes a research project using LiDAR data and permanent sample plot (PSP) field data to generate models for estimating forest attributes. The following steps were completed: 1) LiDAR data was acquired and processed, 2) LiDAR metrics were extracted for PSP locations, 3) models were developed in R relating forest attributes (average height, basal area, biomass) to LiDAR metrics, 4) models were applied to a larger LiDAR dataset, and 5) results were assessed. Models achieved R-squared values from 0.73-0.80 and error margins from 10-28% depending on the attribute. The document discusses limitations and conclusions.
ASSESSING THE LIMITATIONS AND CAPABILITIES OF LIDAR AND LANDSAT 8 TO ESTIMATE...
Remote sensing based quantification of semiarid rangeland vegetation provides
the large scale observations required for monitoring native plant distribution, estimating
fuel loads, modeling climate and hydrological dynamics, and measuring carbon storage.
Fine scale 3-dimensional vertical structural information from airborne lidar and improved
signal to noise ratio and radiometric resolution of recent satellite imagery provide
opportunities for refined measurements of vegetation structure.
In this study, we leverage a large number of time series Landsat 8 vegetation
indices and lidar point cloud - based vegetation metrics with ground validation for scaling
aboveground shrub and herb biomass and cover from small scale plot to large, regional
scales in the Morley Nelson Snake River Birds of Prey National Conservation Area
(NCA), Idaho. The Landsat vegetation indices were trained and linked to in-situ
measurements (n = 141) with the random forest regression to impute vegetation biomass
and cover across the NCA. We also validated our model with an independent dataset (n =
44), explaining up to 63% and 53% of variation in shrub cover and biomass, respectively.
Forty six of the in-situ plots were used in a model to compare the performance of lidar
and Landsat data in estimating vegetation characteristics. Our results demonstrate that
Landsat performs better in estimating both herb (R2 ~ 0.60) and shrub cover (R2 ~ 0.75)
whereas lidar performs better in estimating shrub and total biomass (R2 ~ 0.75 and 0.68,
respectively). Using the lidar only model, we demonstrate that lidar metrics based on
shrub height have a strong correlation with field-measured shrub biomass (R2 ~ 0.76).
vii
We also compare processing the lidar data with raster-based and point cloud-based
approaches. The results are scale-dependent, with improved results of biomass estimation
at coarser scales with point cloud processing. Overall, the results of this study indicate
that Landsat and lidar can be efficiently utilized independently and together to estimate
biomass and cover of vegetation in this semi-arid rangeland environment.
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...
This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Matías Bosio, from PASCHACO - Argentina, in FAO Hq, Rome
061018 Sea Wi Fs Work
The document discusses the challenge of characterizing particulate matter using remote sensing data due to the complex and multidimensional nature of aerosols. It presents results from using SeaWiFS satellite data combined with surface observations to characterize aerosols over the US from 2000-2003. Specific cases studied include quantifying smoke emissions from agricultural fires in Kansas in 2003 by analyzing the optical thickness and shape of smoke plumes. Summer climatologies of aerosol optical thickness over the US from 2000-2004 are also shown.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
cartus_TH4.T02.3.ppt
1. The document evaluates data fusion strategies for measuring ecosystem structure in central Chile using small-footprint lidar, ALOS PALSAR radar, and Landsat data.
2. Lidar data allowed accurate retrieval of growing stock volume and canopy height for plantations, with errors of 23% and 1.7 meters respectively.
3. Fusion of multi-temporal ALOS and Landsat data with lidar measurements improved retrieval accuracy compared to using the radar and optical data alone. Combining data sources was found to be beneficial for measuring forest structure over large areas.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
This document analyzes the use of Lyapunov exponents to determine chaotic structure in hyperspectral satellite data. It investigates an EO-1 Hyperion hyperspectral image of a mixed forest site in Turkey. Lyapunov exponents are calculated from reconstructed phase spaces of spectral signals for different object classes. Positive and negative Lyapunov exponents indicate chaotic behavior is present. The results demonstrate Lyapunov exponents can be used as discriminative features to improve hyperspectral image classification accuracy by capturing the chaotic structures in the data.
Presentation 20220316 nandia
This document outlines a study to estimate above-ground biomass and carbon stock in boreal forests in Mongolia using satellite data and machine learning. Boreal forests cover about 9.2% of Mongolia but have been declining in recent decades. The study aims to develop a suitable machine learning model to map forest biomass and carbon stock. Random forest was the best performing model with an R2 of 0.24 and RMSE of 33 Mg/ha. Important input features included shortwave infrared band 1, green leaf index, and radar polarization data. The predicted forest biomass ranged from 32.5-122.5 Mg/ha and carbon stock ranged from 16.5-62.5 Mg C/ha. Some reference
What's hot (18)
Molinier - Feature Selection for Tree Species Identification in Very High res...
Molinier - Feature Selection for Tree Species Identification in Very High res...
TU4.L09 - RETRIEVAL OF SOIL MOISTURE UNDER VEGETATION USING POLARIMETRIC SCAT...
TU4.L09 - RETRIEVAL OF SOIL MOISTURE UNDER VEGETATION USING POLARIMETRIC SCAT...
BioSAR2010-aSARcampaigninsupportofthebiomassmission.ppt
BioSAR2010-aSARcampaigninsupportofthebiomassmission.ppt
CLASSIFICATION OF TYPHOON-DESTROYED FORESTS BASED ON TREE HEIGHT CHANGE DETEC...
CLASSIFICATION OF TYPHOON-DESTROYED FORESTS BASED ON TREE HEIGHT CHANGE DETEC...
2013 ASPRS Track, Ozone Modeling for the Contiguous United States by Michael ...
2013 ASPRS Track, Ozone Modeling for the Contiguous United States by Michael ...
Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR
Reeves: Modelling & Estimating Forest Structure Attributes Using LiDAR
ASSESSING THE LIMITATIONS AND CAPABILITIES OF LIDAR AND LANDSAT 8 TO ESTIMATE...
ASSESSING THE LIMITATIONS AND CAPABILITIES OF LIDAR AND LANDSAT 8 TO ESTIMATE...
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...
Measurement of Carbon content in plots under SFM and SLM in the Gran Chaco Am...
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
Investigation of Chaotic-Type Features in Hyperspectral Satellite Data
Similar to 2011_0728_IGARSS2011_Motohka.ppt
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...INFOGAIN PUBLICATION
Mangroves are unique ecosystems that provide valuable coastal area habitats, protection, and services. Access to observing mangrove forests is typically difficult on the ground. Therefore, it is of interest to develop and evaluate remote sensing methods that enable us to obtain accurate information on the structure of mangrove forests and to monitor their condition in time. The main objective of this study was to develop a methodology for processing airborne lidar data for measuring height and crown diameter for mangrove forests in the north-eastern coastal areas of Brazil. Specific objectives were to: (1) evaluate the most appropriate lidar data processing approach, such as area-based or individual tree methods, (2) investigate the most appropriate parameters for lidar-derived data products when estimating height and crown diameter, such as the spatial resolution of canopy height models and ground elevation models; and (3) compare the accuracy of lidar estimates to field measurements of height and crown diameter. The lidar dataset was acquired over mangrove forest of the northeast of Brazil. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Root mean square error, linear regression and the Nash-Sutcliffe coefficient were also used to compare lidar height and field height. The mean of lidar-estimated tree height was 9,48m and the mean of field tree height was 8.44m. The correlation between lidar tree height and field tree height was r= 0.60, E=-0.06 and RMSE= 2.8. The correlation between height and crown diameter needed to parameterized the individual tree identification software obtained for 32 trees was r= 0.83 and determination coefficient was r2 = 0.69. The results of the current study show that lidar data could be used to estimate height and average crown diameter of mangrove trees and to improve estimates of other mangrove forest biophysical parameters of interest by focusing at the individual tree level. The research presented in this study contributes to the overall knowledge of using lidar remote sensing to measure and monitor mangrove forests.
Journal.pone.0008267
1) A study compared carbon storage and wildlife diversity between a sustainably managed forest (Deramakot) and a conventionally logged forest (Tangkulap) in Malaysian Borneo.
2) Carbon storage was 54 Mg C ha-1 higher in the sustainably managed forest, which had less degraded forest stands.
3) Wildlife diversity, as measured by camera traps, was higher in the sustainably managed forest, with 6 threatened or near-threatened species showing higher frequencies.
4) The results suggest that sustainable forest management can provide co-benefits of increased carbon storage and wildlife diversity compared to conventional logging practices.
Pisek_20110726_IGARSS2011_v2.ppt
The document discusses using MODIS BRDF data to map forest understory properties in European hemiboreal forests. It found that seasonal dynamics of understory reflectance could be retrieved at 500m resolution with mean absolute errors of 0.007-0.045 for red and 0.019-0.058 for NIR bands when validated against field measurements in Finland. Understory NDVI maps for Finland in 2010 were produced from the MODIS BRDF data with potential to characterize understory properties not possible before with mono-angle sensors.
Allometric equation and carbon sequestration of
This document discusses carbon sequestration through the growth of Acacia mangium trees planted in coal mining reclamation areas in Indonesia. It presents results from measuring the biomass accumulation and developing allometric equations for A. mangium stands of different ages. The key findings are:
1) Younger A. mangium stands in mined areas had lower volume, basal area, and mean annual increment compared to older stands in non-mined areas, likely due to degraded soil in mined areas.
2) Allometric equations were developed relating diameter at breast height to stem volume and biomass of different components. Stem biomass accounted for 67% of total biomass on average.
3
AT_MB_MM_IGARSS2011.ppt
The document presents research on using L-band radar to retrieve soil moisture and vegetation canopy parameters in boreal forests. A forward scattering model is developed to simulate radar backscatter from forest components. An inverse model using simulated annealing is formulated to estimate parameters by minimizing error between modeled and measured backscatter. The approach is tested on synthetic and real radar data from the CanEx-SM10 experiment, showing accurate retrieval of soil moisture for some forest types but larger errors for others due to model and data limitations.
Carbon Stock Estimation in Standing Tree of Chir Pine and Banj Oak Pure Fores...
A study was conducted to measure carbon stock in two Van Panchayat forest of Garhwal Himalaya. For comparative study, we selected the degraded and non-degraded site in Pine and Oak Forest and estimated total carbon stock (above and below ground).
Giacomo bertoldi seminar_30_padova_08
Seminar Given by Giacomo Bertoldi at the 2015 Workshop on Coupled Hydrological Modelling, held in Padua September 25
TERN Supersites and Carbon Monitoring_Mike Liddell
The document summarizes research from several TERN supersites on carbon monitoring across Australia. The supersites provide baseline carbon measures, link field data to carbon dynamics at different scales, connect high resolution data to remote sensing, and inform ecosystem modeling. Specific sites discussed include the Great Western Woodlands, FNQ Rainforest, and Warra Tall Eucalypt supersites. Research at these sites measures vegetation, biomass, carbon stocks, and contributes long-term data to understand carbon cycles and impacts of changes like forest management.
Estimating Carbon Stock of a Protected Tropical Forest in Cebu, Central Phili...
1) The study estimated the carbon stock of a protected tropical forest in Cebu, Philippines across three carbon pools: vegetation, soil, and litterfall.
2) A total of 20 plots were sampled across homogeneous and heterogeneous areas of the forest, which is dominated by Mahogany and Teak trees.
3) Results found an average of 653.9 tons of carbon stored per hectare in the forest's biomass. The homogeneous areas stored more carbon than heterogeneous areas.
4) Soil organic carbon and litterfall carbon were also quantified monthly from April to August 2010.
5) In total, the forest stores a significant amount of carbon across its three pools, helping to offset fossil fuel emissions
Assessing mangrove deforestation using pixel-based image: a machine learning ...
1. The document assesses mangrove deforestation using machine learning algorithms applied to pixel-based images. It examines random forest (RF), support vector machine (SVM), decision tree (DT), and object-based nearest neighbors algorithms to classify mangrove forests in seven provinces in Thailand's Gulf of Thailand.
2. SVM with a radial basis function resulted in an overall accuracy of 96.83%. RF performed better than other algorithms when orthophotography was not available.
3. The study aims to provide features of tree cover loss, above-ground carbon dioxide emissions, and above-ground biomass loss for 2001-2019 using the machine learning algorithms.
ChuePoh_IGARSS2011.ppt
This document discusses using radar polarimetry to estimate forest biomass in mountainous terrain. Fieldwork was conducted in Glen Affric, Scotland to collect biomass data and radar data was collected using ALOS PALSAR and TerraSAR-X. Polarimetric decompositions and regression analysis showed the volume to surface scattering ratio had the strongest relationship with above ground biomass, with an R2 of 0.74. The study found radar polarimetry can help estimate biomass in areas where traditional backscatter regression is problematic, but performance depends on forest characteristics and sensors.
ChuePoh_IGARSS2011.ppt
This document discusses using radar polarimetry to estimate forest biomass in mountainous terrain. Fieldwork was conducted and LIDAR, ALOS PALSAR, and TerraSAR-X data were collected for a study area in Glen Affric, Scotland. A biomass regression analysis found a correlation of R^2=0.74 between radar measurements and field measurements for dense forests, though performance was worse for sparse areas due to effects like shadows. The study demonstrates the potential of radar for large-scale biomass estimation but notes limitations for high biomass densities.
Geog presentation for_web
This document discusses a study that used a process-based vegetation model (LPJ-GUESS) to simulate the dynamics of forest composition on the Olympic Peninsula since the last glacial period (~15,000 years ago), and compare the simulations to paleoecological records. The study parameterized LPJ-GUESS for 9 tree species common to the region, considering each species' shade tolerance, temperature and moisture requirements. Simulation results showed good agreement with pollen-based reconstructions of forest changes over time for most sites and elevations. However, the model did not fully capture early Holocene dominance of Douglas-fir and alder at some mid-elevation sites. The study provides insights into how tree species life histories and climate
Assessment of biomass and carbon sequestration potentials of standing Pongami...
The need of identifying the plant species having multitude value in present world are essential. Even in the Climate Change mitigation measures, the utilization of those plant species having economical value like bio-disel as well as can play their role of carbon sequestration.
pres_h2a_okconvert.ppt
1) The document proposes a method to jointly process dual polarized entropy/alpha decomposition and coherence optimization for improved forest height mapping from radar data.
2) Dual polarized entropy/alpha decomposition is used to classify forest/non-forest areas while coherence optimization estimates forest height by exploiting the relationship between coherence and height.
3) Initial results comparing optimized coherence to LiDAR height data show consistency, validating the potential of the integrated method. Further quantitative analysis is needed to fully evaluate accuracy.
Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux...
This document discusses upscaling flux tower data using remote sensing and modeling to produce global carbon and water flux maps. It summarizes work merging OzFlux tower data, AusCover remote sensing, eMAST modeling, and data integration to upscale measurements from plant to global scales. Two examples are provided of data-model integration at Tumbarumba and along the North Australian Tropical Transect, showing improved ecosystem process understanding through spatially resolved inputs to models from remote sensing. The work finds primary drivers of variability are land use and LAI rather than photosynthetic capacity, and scaling up through TERN facilitates integrated temporal and spatial information.
LBA - Prof. Paulo Artaxo
The document discusses the Large Scale Biosphere Atmosphere Experiment in Amazonia (LBA). It addresses key scientific questions about how the Amazon functions as a regional entity and how land use and climate changes affect its biological, chemical, and physical functions. Specific topics covered include the water, carbon, and nutrient cycles; aerosols; anthropogenic activities; susceptibility to stresses; and interactions between climate, land use, fire, hydrology, ecology, and human dimensions. It also provides conceptual diagrams of these interactions and the major uncertainties in quantifying the climate system.
kellndorfer_WE3.T05.4.pptx
This document describes research mapping forest height, biomass, and carbon across the contiguous United States from 2000-2007 using satellite and field data. A National Biomass and Carbon Dataset was created in 2000 by combining SRTM radar data, Landsat optical imagery, and field plot measurements. This provided 30m resolution estimates of height, biomass, and carbon. ALOS PALSAR dual-polarization SAR data from 2007 was later used to update biomass estimates. Both datasets were validated against field measurements and showed strong correlations at regional to national scales. The research demonstrated the ability to map important forest variables over large areas by combining different remote sensing and field data sources.
Similar to 2011_0728_IGARSS2011_Motohka.ppt (20)
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Measuring Individual Tree Height and Crown Diameter for Mangrove Trees with A...
Carbon Stock Estimation in Standing Tree of Chir Pine and Banj Oak Pure Fores...
Carbon Stock Estimation in Standing Tree of Chir Pine and Banj Oak Pure Fores...
TERN Supersites and Carbon Monitoring_Mike Liddell
TERN Supersites and Carbon Monitoring_Mike Liddell
Estimating Carbon Stock of a Protected Tropical Forest in Cebu, Central Phili...
Estimating Carbon Stock of a Protected Tropical Forest in Cebu, Central Phili...
Assessing mangrove deforestation using pixel-based image: a machine learning ...
Assessing mangrove deforestation using pixel-based image: a machine learning ...
Assessment of biomass and carbon sequestration potentials of standing Pongami...
Assessment of biomass and carbon sequestration potentials of standing Pongami...
Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux...
Eva van Gorsel_ From plant canopy to ecosystem to the globe: upscaling OzFlux...
More from grssieee
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
1) The document describes a segmentation algorithm for polarimetric SAR (PolSAR) data that can model both scalar-texture and multi-texture scattering.
2) The algorithm uses log-cumulants and hypothesis testing to determine whether a scalar-texture or dual-texture model best fits the data within each segment.
3) The algorithm is tested on simulated multi-texture PolSAR data and is shown to accurately segment the classes and estimate their texture parameters. However, when applied to real data sets, the algorithm only finds the simpler scalar-texture case.
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
This document discusses using wavelet transforms to analyze two-point statistics of polarimetric synthetic aperture radar (PolSAR) data. It introduces wavelet variance and kurtosis as metrics that can be applied to PolSAR data transformed using a wavelet frame. It then provides an example of applying this analysis to ALOS PALSAR data over Hawaii's Papau Seamount to characterize sea surface features.
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
The Sentinel-1 mission is part of the GMES program and consists of two satellites to provide C-band SAR data for emergency response, marine and land monitoring, and other applications. The satellites operate in a near-polar orbit with a 12 day repeat cycle. The main acquisition mode is an interferometric wide swath mode with 5m range and 20m azimuth resolution over a 250km swath. Sentinel-1 will support operational services and create a long-term SAR data archive.
GMES SPACE COMPONENT:PROGRAMMATIC STATUS
The document summarizes the status of the GMES Space Component program. It describes the Sentinel satellite missions for monitoring land, ocean, atmosphere and emergency situations. The Sentinels will provide long-term data continuity as well as improved coverage compared to existing missions. Sentinel data will be freely and openly available to both operational users and the science community. The program is on track, with the first Sentinel launches beginning in 2013.
PROGRESSES OF DEVELOPMENT OF CFOSAT SCATTEROMETER
The document describes the progress of the development of CFOSAT SCAT, a Ku-band scatterometer onboard the Chinese-French Oceanography Satellite (CFOSAT). CFOSAT will measure global ocean surface winds and waves to improve weather forecasting, ocean dynamics modeling, climate research, and understanding of surface processes. The SCAT instrument is a rotating fan-beam radar scatterometer that will retrieve wind vectors using measurements of backscatter at incidence angles from 26 to 46 degrees. It has a wide swath of over 1000km and specifications are designed to achieve high-precision wind measurements globally. System details including parameters and the operation mode are provided.
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
The document describes the SAP4PRISMA project which aims to develop algorithms and products to support the Italian hyperspectral PRISMA Earth observation mission. The project will focus on data processing, quality assessment, classification methods, and generating level 3 and 4 products for applications like land monitoring, agriculture, and hazard monitoring. It will include the generation of "PRISMA-like" synthetic test data to support algorithm development and validation. The research will be carried out across multiple work packages focusing on topics like data quality, classification methods, calibration/validation, and developing applicative products.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different sites, including a Zambian woodland and North Carolina forest sites.
3) Time series of Hyperion data at flux tower sites show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
1) The EO-1 Hyperion instrument has collected over 65,000 scenes over its 12-year mission to study land and coastal ecosystems using imaging spectroscopy.
2) Studies using Hyperion data have identified spectral indices related to chlorophyll that correlate with carbon flux measurements at different forest, grassland, and woodland sites globally.
3) Time series of Hyperion data at sites in Zambia, North Carolina, and Kansas show seasonal changes in these spectral indices that match patterns in ecosystem carbon uptake and release measured by flux towers.
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/Hyperion has been collecting hyperspectral imagery for over 12 years, acquiring over 65,000 scenes. Researchers have been using these data to develop and validate algorithms for estimating vegetation properties like fraction of absorbed photosynthetically active radiation (fAPAR) and photochemical reflectance index (PRI). Comparisons of Hyperion data to field measurements at flux tower sites show these algorithms can accurately track vegetation changes over time and relate spectral properties to productivity metrics like light use efficiency and gross ecosystem productivity. This work is helping prototype data products for the upcoming HyspIRI mission.
Test
This document is a return and exchange form for a wetsuit company. It provides instructions for customers to fill out when returning an undamaged item for a refund, exchange, or size change. The form requests information like the customer's order details, contact information, the suit being returned and its size, the reason for return, and if applicable, the new desired size. It also provides the return shipping address and notifies customers that the company is not responsible for lost or damaged return packages.
2011_Fox_Tax_Worksheets.pdf
This document provides instructions for clients of Fox Tax Planning and Preparation for preparing to have their taxes filed. It lists important income and deduction documentation to bring to an appointment, such as W-2s, 1099s, receipts for donations. It also includes an engagement letter detailing the services to be provided, responsibilities of both parties, fees, and electronic filing and signature procedures. Clients are asked to sign the letter agreeing to the terms and return it along with their tax information.
Tana_IGARSS2011.ppt
The document discusses mapping wetlands in North America using MODIS 500m imagery. It describes wetlands and existing global wetland databases. The methodology uses MODIS data from 2008, digital elevation models, and reference data to classify wetlands into three types - forest/shrub dominant wetlands, herbaceous dominant wetlands, and sea grass dominant wetlands. Training data is collected from existing land cover maps and Landsat imagery. A decision tree model and maximum likelihood classification are applied to extract wetlands from other land covers.
Solaro_IGARSS_2011.ppt
The document summarizes research using SBAS-DInSAR (Small BAseline Subset differential interferometric synthetic aperture radar) techniques to analyze ground deformation at Mt. Etna volcano in Italy over the last 18 years using ERS and ENVISAT satellite data. The analysis revealed three main deformation processes: inflation of the volcanic edifice, subsidence of sectors on the eastern flank due to gravitational spreading, and deflation-inflation cycles associated with eruptive and post-eruptive activity. More recent analysis using higher resolution COSMO-SkyMed data from 2009-2010 detected deformation related to faults and a 2010 earthquake more precisely than lower resolution ENVISAT data.
More from grssieee (20)
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
Tangent height accuracy of Superconducting Submillimeter-Wave Limb-Emission S...
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
SEGMENTATION OF POLARIMETRIC SAR DATA WITH A MULTI-TEXTURE PRODUCT MODEL
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
TWO-POINT STATISTIC OF POLARIMETRIC SAR DATA TWO-POINT STATISTIC OF POLARIMET...
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
THE SENTINEL-1 MISSION AND ITS APPLICATION CAPABILITIES
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
DEVELOPMENT OF ALGORITHMS AND PRODUCTS FOR SUPPORTING THE ITALIAN HYPERSPECTR...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
EO-1/HYPERION: NEARING TWELVE YEARS OF SUCCESSFUL MISSION SCIENCE OPERATION A...
Recently uploaded
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
dbms calicut university B. sc Cs 4th sem.pdf
Its a seminar ppt on database management system using sql
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Recently uploaded (20)
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdf
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
2011_0728_IGARSS2011_Motohka.ppt
- 1. Relationships between PALSAR backscattering data and forest above ground biomass in Japan ○ Takeshi Motohka (Japan Aerospace Exploration Agency) 2011/07/28: IGARSS 2011, Vancouver, Canada. Masanobu Shimada (Japan Aerospace Exploration Agency) Osamu Isoguchi (Remote Sensing Technology Center of Japan) Masae I. Ishihara (Japan Wildlife Research Center) Satoshi N. Suzuki (Japan Wildlife Research Center)
- 8. Deciduous broadleaf forest (Tomakomai, Hokkaido) Evergreen coniferous forest (Otanomousu-daira, Nagano) Deciduous broadleaf forest (Chichibu, Saitama) Evergreen broadleaf forest (Yona, Okinawa)
- 10. Tree diameter of breast height [cm] (DBH) Dry weight [kg] measured all trees in about 1 ha plot except for DBH < about 5 cm. Allometric equations for each specie Ʃ (dry weight) / stand-size Biomass [t/ha] Processing of tree census data
- 11. Statistics of the forest stands (n=44) DBH: Diameter at Breast Height
- 12. PALSAR yearly mosaic R: HH G: HV B: HH/HV Year: 2007, 2009 Mode ： Fine beam dual (HH, HV) Mosaicking period ： Jun. - Sep. Pixel sampling ： 10 m Orbit: Ascending
- 14. Saturation level : - dy/dx = 0.01 … 91 [t/ha] - dy/dx = 0.005 … 182 [t/ha] HV RMSE : 0.703 [dB] PALSAR γ 0 vs. biomass
- 15. Saturation level : - dy/dx = 0.01 … 68 [t/ha] - dy/dx = 0.005 … 136 [t/ha] PALSAR γ 0 vs. biomass RMSE : 1.053 [dB] HH
- 16. Red ●: After correction Green +: Before correction RMSE : 1.053 [dB] RMSE 0.703 [dB] RMSE : 2.312 [dB] RMSE 2.073 [dB] Effect of slope-correction
- 17. HV HH Precipitation vs. PALSAR gamma naught rainfall (over 10mm during 3 days before obs.) RMSE: HH : 0.670 dB HV : 0.402 dB Mean bias between rainy and non-rainy data: HH : +0.177 dB HV : +0.044 dB
- 18. Inversion of forest biomass RMSE: 106.23 t/ha %RMSE: 39.3 % (=RMSE/mean ) PALSAR HV γ 0 (15 x 15 pix average) Water, Lay-over, Shadowing mask Urban area mask Biomass map
- 19. 400 ～ 0 100 200 300 Above ground biomass (t/ha)
- 20. Cropland Cropland Wetland 400 ～ 0 100 200 300 Above ground biomass (t/ha)
- 21. 1 km (c) Google Earth 400 ～ 0 100 200 300 Above ground biomass (t/ha)