20110723IGARSS_ZHAO-yang.ppt
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The document proposes an improved change vector analysis (ICVA) method to more accurately detect land cover changes using multi-temporal remote sensing data. ICVA combines traditional change vector analysis with a cross-correlogram spectral matching algorithm to 1) preliminarily detect changes, 2) identify and eliminate areas of vegetation variation rather than conversion using profile similarity analysis, and 3) determine actual land cover conversion types. The method is tested on MODIS EVI data for a region in China, achieving higher accuracy than traditional change vector analysis alone.
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![Improving change vector analysis in multitemporal space to detect land cover changes by using cross-correlogram spectral matching algorithm Yuanyuan Zhao, Chunyang He, Yang yang Beijing Normal University, Beijing, China, 100875 Email : [email_address] 2011 IEEE International Geoscience and Remote Sensing Symposium](https://image.slidesharecdn.com/20110723igarss-zhao-yang3-110726013119-phpapp02/85/20110723IGARSS_ZHAO-yang-ppt-1-320.jpg)
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This document presents a Bayesian methodology for retrieving soil parameters like moisture from SAR images. It begins by introducing the importance of soil moisture monitoring and the opportunity provided by Argentina's upcoming SAOCOM SAR satellite. It then discusses limitations of traditional retrieval models in accounting for speckle noise and terrain heterogeneity. The document proposes a Bayesian approach using a multiplicative speckle model within a likelihood function to estimate soil moisture and roughness from SAR backscatter measurements. Simulation results show the Bayesian method retrieves soil moisture across the full measurement space and provides error estimates, with improved precision at higher numbers of looks.
1. mohammed aslam, b. mahalingam
This study analyzed land use and land cover changes around a mined area in Kannur district, Kerala, India between 2000 and 2017 using satellite imagery. Support vector machine classification identified five land cover classes: vegetation, barren land, built up area, mining area, and waterbodies. In 2000, vegetation covered 51.34% of the area, followed by barren land at 31.75%. By 2017, vegetation increased to 58.46% while barren land decreased to 19.98%. The mining area saw little change, increasing vertically within the same area. Comparing land cover changes over time can help sustainable environmental management near mined regions.
Integration of geological and petrophysical constraints in geophysical joint ...
Integration of geological and petrophysical constraints in geophysical joint ...The University of Western Australia
Jeremie Giraud's PhD research being conducted at the Centre for Exploration Targeting, University of Western Australia is investigating the use of probabilistic geological models and statistical distributions of petrophysics to constrain joint potential field inversion.Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...The Statistical and Applied Mathematical Sciences Institute
In the first part of the talk, we will present a sensitivity analysis of a novel sea ice model. neXtSIM is a continuous Lagrangian numerical model that uses an elastobrittle rheology to simulate the ice response to external forces. The response of the model is evaluated in terms of simulated ice drift distances from its initial position and from the mean position of the ensemble. The simulated ice drift is decomposed into advective and diffusive parts that are characterized separately both spatially and temporally and compared to what is obtained with a free-drift model, i.e. when the ice rheology does not play any role. Overall the large-scale response of neXtSIM is correlated to the ice thickness and the wind velocity fields while the free-drift model response is mostly correlated to the wind velocity pattern only. The seasonal variability of the model sensitivity shows the role of the ice compactness and rheology at both local and Arctic scales. Indeed, the ice drift simulated by neXtSIM in summer is close to the free-drift model, while the more compact and solid ice pack is showing a significantly different mechanical and drift behavior in winter. In contrast of the free-drift model, neXtSIM reproduces the sea ice Lagrangian diffusion regimes as found from observed trajectories. The forecast capability of neXtSIM is also evaluated using a large set of real buoy’s trajectories. We found that neXtSIM performs better in simulating sea ice drift, both in terms of forecast error and as a tool to assist search-and-rescue operations. Adaptive meshes, as the one used in neXtSIM, are used to model a wide variety of physical phenomena. Some of these models, in particular those of sea ice movement, use a remeshing process to remove and insert mesh points at various points in their evolution. This represents a challenge in developing compatible data assimilation schemes, as the dimension of the state space we wish to estimate can change over time when these remeshings occur.
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Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.Exploring DEM error with geographically weighted regression
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Statistical Methods
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segam2015-5925444%2E1
This summarizes a study that applies a technique of simultaneous full-waveform inversion (FWI) of baseline and monitor seismic data with total-variation (TV) regularization of the model differences to resolve production-induced subsurface changes using field data from the Gulf of Mexico Genesis Field. The technique inverts multiple survey vintages simultaneously with TV regularization, which promotes blocky model differences and reduces oscillatory artifacts. Application to the Genesis Field data resolves negative velocity changes in the overburden associated with compaction and dilation effects from nearby reservoir production, consistent with prior time-strain estimates.
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soil moisture retrieval.pptx
The document proposes a method for soil moisture retrieval over bare surfaces using time-series radar observations and a lookup table representation. It finds that:
1. Retrieving soil moisture and roughness using a time-series method that estimates time-invariant roughness first, then soil moisture, performs better than other empirical methods, with an error of 0.044 cm3/cm3 using 6-11 time observations.
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soil moisture retrieval.pptx
The document presents a method for retrieving soil moisture over bare surfaces using time-series radar observations and a lookup table representation. Key points:
1) It develops a non-empirical retrieval method that does not require ancillary data and inverts a forward model using a lookup table.
2) Monte Carlo simulations show the method can retrieve soil moisture to within 0.06 cm3/cm3 even with 17% radar measurement noise.
3) Validation with in situ data resulted in a root mean square error of 0.044 cm3/cm3 using 6-11 time-series inputs, outperforming other methods.
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
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2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
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- The system calculates sub-grid land states from CLM, simulates BT from these states, and calibrates model parameters and wetland emissivity to minimize differences from observed AMSR-E BT.
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20110723IGARSS_ZHAO-yang.ppt
- 1. Improving change vector analysis in multitemporal space to detect land cover changes by using cross-correlogram spectral matching algorithm Yuanyuan Zhao, Chunyang He, Yang yang Beijing Normal University, Beijing, China, 100875 Email : [email_address] 2011 IEEE International Geoscience and Remote Sensing Symposium
- 8. Traditional change vector analysis The VI time series data in the period R ： A greater M indicates a higher possibility of land cover change for pixel i . A specific threshold is used to distinguish change pixels from no-change pixels （ Lambin and Strahler, 1994a ） 。 The VI time series data in the period S ：
- 17. Study area Latitude: 38°28′ N - 41°05′ N Longitude: 115°25′ E -119°53′ E Total area: 55774.5 km 2 Climate: Sub-humid and temperate monsoon climate Main land cover type: cropland, built-up, forest Over the past several decades, significant land cover changes have taken place in the BTT-UAD, mainly driven by rapid economic development and unprecedented urbanization (Tan et al., 2005). Beijing–Tianjin–Tangshan urban agglomeration district (BTT-UAD), China
- 20. Extracting preliminary pixels of land cover change Change magnitude image of the study area, 2000-2008 Preliminary extraction of land cover change (2000-2008) in the study area EVI time series in 2000 EVI time series in 2008 TCVA Calculating Change magnitude Preliminary change information DFPS
- 21. Eliminating land cover modification in the study area using the CCSM algorithm The preliminary change information EVI time series in 2000 EVI time series in 2008 Calculating the shape similarity index R max Manual trial-and-error procedure land cover conversion Land cover conversion in the study area, 2000-2008 R max calculated by CCSM using the EVI profile curves in 2000 and 2008
- 31. Thank you very much!
Editor's Notes
- the land cover for an image pixel in a period is represented by a multi-dimensional vector of NDVI, while the number of dimensions is dependent on the number of NDVI observations in the period. The Euclidean distance between the NDVI vectors for two different periods (e.g., different years) is used to measure the change in magnitude. A threshold is then applied to the change magnitudes to separate the significant land cover changes from the rest (Chen et al . , 2003). TCVA has then been widely adopted in land cover change detection using VI data.
- 利用 TCVAM 方法计算研究区 2000-2008 年 土地覆盖变化强度影像 。 在辅助数据的支持下，利用双窗口变步长阈值搜寻算法确定变化区域的最佳阈值为 0.77 ， 初步提取京津唐城市群土地覆盖变化信息 。
- , because both the values and the profile shape of a yearly series of VI tend to change significantly in such case although the land cover type remains to be the same. Future efforts should be directed to further increase the accuracy of detecting land cover conversion.