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Mark Chopping received the M.Phil. in Remote Sensing and Geographical Information Systems from the University of Cambridge in 1995 and the Ph.D. in Remote Sensing from the University of Nottingham in 1998. His research interests focus on the use of multi-angle data and bidirectional reflectance distribution function models with moderate resolution off-nadir data from space-borne sensors to mapping vegetation canopy structure and type. He has worked in arid and semi-arid environments in China and in the United States, and more recently in Arctic tundra.
In 1999 he joined the Agricultural Research Service of the United States Department of Agriculture as Physical Scientist, working on remote sensing of arid rangelands. He has also served as an investigator with the European Space Agency’s program for exploitation of multi-angle data from the Compact High Resolution Imaging Spectrometer flown on the PROBA mission. In 2002 he joined the Department of Earth and Environmental Studies as Assistant Professor at Montclair State University (NJ) and in 2003 became Principal Investigator in a NASA Earth Observing System project to map vegetation communities and carbon pools in arid environments using data from MISR and MODIS. Since then he has served as Principal Investigator in three further NASA-funded projects (see links).
BRDF and Canopy Reflectance Modeling
Terrestrial Ecology/Carbon Cycle Science
Geographical Information Systems
- 1:00 pm - 4:00 pm
A Decade of Changes in Aboveground Live Standing Dry Biomass, Canopy Cover, Height, and Understory Density in the Southwestern United States from EOS MISR and MODIS
The goal of this project is to use data from the Multiangle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) to map changes in aboveground standing live dry biomass in trees and shrubs in the southwestern United States for the period 2000-2009. It will leverage an innovative approach developed in previous Earth Observing System projects that exploits structural rather than spectral signals in bidirectional reflectance factor (BRF) data: this has demonstrated success in both estimating shrub abundance and primary forest canopy parameters at ~250 m with good accuracy. The method is predicated on the strong structural signal available in MISR and MODIS BRF data: both instruments view far from nadir, introducing reflectance anisotropy that can be exploited to predict understory density and invert geometric-optical (GO) models for crown cover and mean canopy height. Aboveground standing live dry biomass estimated using both parameters is likely to be more accurate than estimates based on spectral measures because canopy height is taken into account.
The project builds on advances in GO model inversion behavior stemming from recent efforts to assess retrievals against high resolution discrete return lidar data and orthophotography - rather than against moderate or medium resolution data products - and on a new and efficient automated method for obtaining high resolution validation data: the Canopy Analysis with Panchromatic Imagery (CANAPI) algorithm. This is able to delineate crowns using only high resolution panchromatic imagery and provide accurate measures of crown cover for the large (>250 m^2) areas needed for validation of moderate resolution products. Crown cover estimates are also needed to improve background sub-model calibration that is critical to the accuracy and precision of cover and height retrievals obtained via GO model inversion. Reference data will also include ground survey data for a number of well-characterized sites and lidar canopy heights where available. The approach has several advantages: MISR and MODIS cover large areas efficiently, allowing evaluation of trajectories through time from 2000; MISR views away from the solar principal plane allowing the background and upper canopy contributions to be isolated; explicitly acknowledging and exploiting structural effects provides potentially less ambiguous measures than spectral indices; and output maps include shrubs as well as trees. The ability to map understory density at large scales using multiangle imagery has been identified by other research groups and points towards the realization of synergies with data from lidar and radar sensors (e.g., between the DESDyNI and ACE missions that will fly active instruments and a multiangle polarimeter, respectively).
This research is important because disturbances that are likely to be related to changing climatic conditions - owing to global warming - appear to be playing increasingly prominent roles in defining aboveground carbon stocks in the southwestern US. These include unusually extensive and severe losses in forest cover from a modified fire regime; unusually extensive and severe insect outbreaks (pine and spruce beetle); and continued woody encroachment into former desert grasslands. Increasing shrub abundance in desert grasslands has already reduced the ability of the land to support a viable livestock economy in many places and is also important in terms of ecosystem structure and function and feedbacks to climate. As well as leveraging recent advances in exploiting EOS data using this novel modeling framework, this research also addresses imperatives within NASA's Carbon Cycle and Ecosystems and Terrestrial Ecology programs to further research quantifying changes in aboveground standing biomass from disturbance and recovery, and to address measurement uncertainty in low biomass regions.
NOTE: the website represents work from previous NASA-sponsored projects as well as this one.
Mapping Changes in Shrub Abundance & Biomass in Arctic Tundra using NASA Earth Observing System Data: A Structural Approach
The goal of this NASA-sponsored project is to map shrub abundance and estimate spatial distributions of woody biomass in Arctic tundra using NASA moderate resolution solar wavelength reflectance imagery over very large areas (i.e., the Arctic). Our primary aim is to map plant fractional crown cover but since we are exploiting structural metrics available through multi-angle approaches (e.g. surface roughness metrics, canopy height), we may also be able to provide estimates of height and biomass (Mg ha-1). Adapting and applying multi-angle remote sensing methods developed in earlier research projects, we aim to use these two information dimensions (horizontal and vertical) to inform efforts to map cover and aboveground woody biomass.