Introduce:Critical situation wrt global biodiversityRecognition of importance by decadal survey
Introduce:Critical situation wrt global biodiversityRecognition of importance by decadal survey – they specifically included biodiversity
The DESDynI mission summary from the Decadal Survey.They specifically mention habitat structure under the biodiversity and habitat component and identify both lidar and radar as the sensors to retrieve habitat structure for biodiversity science & conservation management
Introduce definitionsIntroduce how they relate to each otherThe Red lettering always denotes where a sensor other than radar or lidar is more likely to supply the needed information (e.g. multispectral optical or hyperspectral)
This slide talks about habitat
An example for habitatThese variables translate into DESDynI variablesExample for one bird speciesAgain – text in red may be better retrieved by passive optical (e.g. floristics, species…)
This slides about biodiversity (as opposed to habitat)
An example for biodiversity
Another example for biodiversityIllustrating relationship with key variable of vertical profile
Only room here for just two examples, one from each sensor, can note that there are many more.HOWEVER: there are no real examples of radar lidar fusion for biodiversity – yet.
Introduce how these definitions/concepts relate to vegetation 3-D structureMap on left was actually derived from multi-frequency polarimetric radar – landscape structureOn right is map of biomass derived from radar and canopy height profile derived from Lidar
Within the Teakettle Experimental Forest, 60% area identified as high biomass and low stress showed significantheight gain between 1999 and 2008. 40% area identified as high biomass and high stress showed significant height loss between 1999 and 2008.
May not need to use this slide.This would need to be updated if you use it, but there has been so much back and forth I’m not certain what the latest is!This is from the Fall meeting at Goddard with Paul Rosen.Feel free to modify.
Provide supporting evidence and illustrate examples of requirements for DESDynI for BD&HFocus on the 4 main variables
Provide supporting evidence and illustrate examples of requirements for DESDynI for BD&HFocus on the 4 main variablesYou may be able to improve some of my “Fusion” statements, feel free.
DESDYNI BIODIVERSITY AND HABITAT KEY VARIABLES AND IMPLICATIONS FOR LIDAR-RADAR FUSION 1 Kathleen Bergen, Ralph Dubayah, Scott Goetz Presented by Ralph Dubayah IGARSS 2010 Special Session on DESDynI Fusion
What precisions, temporal and spatial coverage of lidar and radar-derived measurements are needed for fusion?
What are the most important variables for lidar-radar fusion?
Science Objectives CHARACTERIZE THE EFFECTS OF CHANGING CLIMATE AND LAND USE ON TERRESTRIAL CARBON CYCLE, ATMOSPHERIC CO2, AND SPECIES HABITATS Characterize global distribution of aboveground vegetation biomass Quantify changes in terrestrial biomass resulting from disturbance and recovery Characterize habitat structure for biodiversity assessments
Science Objective 3: Habitat Structure Characterize habitat structure for biodiversity assessments Various forest structure products with specified accuracies (includes both gridded data and ungridded transect data) Desired Final Data Products Forest canopy structure including height, canopy profile, canopy cover, canopy roughness, biomass, vertical diversity Measurement Objectives Multi-beam lidar, polarimetric L-band SAR Instruments
Introduction: Identification by Decadal Survey 5
Key Concepts Biodiversity:combination of richness and abundance Habitat: the environmental conditions required by a species for survival and reproduction Floristics:The vegetation composition (flora) comprising habitat Landscape Structure: patches and the spatial heterogeneity of an area composed of interacting habitat patches Vertical Structure: the bottom to top configuration or complexity of above-ground vegetation Vertical Structure Floristics Landscape Structure Habitat Habitat Heterogeneity Biodiversity
Example: Total breeding bird density (pairs per 25 ha) as a function of total vegetation volume (TVV) for Arizona study sites ranging from desert-grasslands to woodlands to forests. Regression equation: y = 290x – 1.0. (Miller et al.)
Radar and Lidar Capabilities 18 Vegetation Type Upland conifer Lowland conifer Northern hardwoods Aspen/lowland deciduous Grassland Agriculture Wetlands Open water Urban/barren Lidar and Radar can Map and Measure Vertical Structure & Biomass Radar and Lidar Can Map and Measure Landscape Structure Vegetation 3D Structure & Biomass: Radar and Lidar Together High: 30 kg/m2 Biomass Low: 0 kg/m2 Low: 0 kg/m2
Space Mission: Current DESDynI Ecosystems Level 1 Requirements
The DESDynI Mission shall produce global estimates of aboveground woody biomass within the greater of 20 Mg/ha or 20% (errors not to exceed 50 Mg/ha), at a spatial resolution of 250 m globally at end of mission.
Provide transects of vegetation vertical canopy profiles over all biomes at 25 m spatial resolution, 30 m along-transect posting, with a maximum of500m across-transect posting at end of mission and 1 m vertical resolution up to conditions of 99% canopy cover.
* for areas disturbed at least 4 years prior to last observation and where the resulting biomass is less than 80 Mg/ha 25
Variables Important for Biodiversity and Habitat 27
Variables Important for Biodiversity and Habitat 28
Variables Important for Biodiversity and Habitat 29
Conclusions: Key Variables and Fusion Canopy height: a key habitat characteristic, forest height has been correlated with biodiversity, including plant and avian species richness. Fusion: Only fusion of lidar and radar together will provide canopy height wall-to-wall maps that are highly desired by conservation managers. Canopy height profile: provides observations on presence of different strata (e.g. overstory, understory) for vegetation vertical structure & diversity metrics. Correlated with biodiversity and with habitat suitability . Highly sought after by biodiversity scientists and conservation managers. Fusion: IFSAR? Biomass: is an indicator of the type of structure, age or maturity of a forest, and forest productive ability; the amount of total biomass in a patchhas been correlated with habitat use by species. Ranks high as desired by wildlife managers. Fusion: Only fusion of lidar and radar together will provide spatially continuous biomass maps that are highly desired by conservation managers and biodiversity scientists. Canopy cover:is also related to tree age and density and has been correlated with habitat suitability for species of birds, mammals, amphibians, and reptiles. Fusion: Lidar is the primary variable of the two, high spatial resolution radar would be needed for useful fusion. 30
Conclusions: Key Capabilities and Fusion Global coverage of forested ecosystems: landscape and forest structures are rapidly changing worldwide, and implications include extinctions and invasive species; data from all forested ecosystems will be required to assess the global extent of change. Fusion: will benefit from dense coverage of lidar transects, fusion with radar will provide wall-to-wall global coverage for all fusion variables. Contiguous along-track lidar footprints: Continuous profiles of vegetation along-track for calculating structure correlation lengths and other metrics, identification of edges, maximizing observation of ground to maximize precision of height estimates, identification of rare ecosystem features Fusion: this is a lidar variable, but the benefits of contiguous along-track lidar footprints will carry over into increased precisions of radar-lidar fusion for heights, biomass, texture, edge mapping, landscape pattern, surface roughness and potentially other fusion variables. Targeted response for events: Periodic or stochastic disturbance events such as hurricanes, fire, wind blow downs and insects have impacts on vegetation 3D structure and consequently on biodiversity and habitat of plants and animals Fusion: Wherever radar and lidar overlap in disturbance areas the lidar will be useful to increase the confidence and precision in radar observations and provide additional unique information on within-canopy structure where applicable. 31