K. Kirsch

Woodwell Climate Research Center

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Wayne Walker

University of Oxford

Josef Kellndorfer

Woodwell Climate Research Center

J. Bishop

University System of Georgia

Greg Fiske

Woodwell Climate Research Center

S. J. Goetz

Northern Arizona University

Elizabeth B. LaPoint

Northern Research Station

Tina Cormier

Indigo Information Services (United States)

Michael Hoppus

US Forest Service

Markus Rombach

Hahn-Schickard-Gesellschaft für angewandte Forschung

Claudia Stickler

Earth Island Institute

Cooperative Institutions

Woodwell Climate Research Center

US Forest Service

Northern Arizona University

Northern Research Station

Rocky Mountain Research Station

United States Department of Agriculture

Institut für Urheber- und Medienrecht

Earth Island Institute

Amazon (United States)

Instituto de Pesquisa Ambiental da Amazônia

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Forest Volume and Biomass estimation from SAR/LIDAR/Optical Fusion in Chile

AGUFM (2009)

Josef Kellndorfer Wayne Walker S. J. Goetz Tina Cormier K. Kirsch

Sensor Fusion

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Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: A regional‐scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets

Journal of Geophysical Research Atmospheres (2010)

Josef Kellndorfer Wayne Walker Elizabeth B. LaPoint K. Kirsch J. Bishop

A method is presented to characterize forest stand heights in a 110,000 km 2 region in the eastern United States surrounding the Chesapeake Bay area, driven by a statistical fusion model solely based on remote sensing data. The predicted map was tested against ground survey data from the Forest Inventory and Analysis (FIA) plot network. Input data to the model were 2003 medium footprint lidar data from the Laser Vegetation Imaging Sensor (LVIS) sensor, interferometric radar data from the 2000 Shuttle Radar Topography Mission (SRTM), 1999–2001 Landsat ETM+ data, and ancillary data sets of land cover and canopy density developed for the 2001 National Land Cover Database. In the presented approach, the interferometric synthetic aperture radar (InSAR), optical, and ancillary data sets were masked to the forested areas of the study region and used to segment the raster data stack. The generated image objects closely represented quasi‐homogenous forest stands. For a small region in the study area covered by an LVIS acquisition, LVIS lidar data were then used within the established segments to extract lidar‐based mean forest stand heights. Subsequently these LVIS mean stand heights were used as the response variable to the statistical prediction model (randomForest) which had segment‐based metrics like mean InSAR height (derived from SRTM minus ground digital elevation model data from the National Elevation Data set), mean optical reflectance (derived from Landsat ETM+ Tassled Cap Data), and ancillary metrics as predictive variables. The model developed over the area where LVIS data were available was then applied to map the entire study region. Independent validation of the model was performed in two ways. First, splitting of the model data stack into training and independent testing populations, i.e., testing on LVIS data. This test was deemed to describe the model performance within the LVIS swath. Second, predicted heights were compared to plot height metrics derived from FIA data in the entire study region, thus testing the validity of the model across the larger study area. Results, which are somewhat tampered by the time disconnect between the various data collections, showed the validity and usefulness of this approach. Independent LVIS testing resulted in a correlation coefficient r = 0.83 with an RMSE of 3.0 m (9% error), independent FIA data tested with r = 0.71 with an RMSE of 4.4 m (13% error).

Tree canopy

Elevation (ballistics)

Land Cover

Forest Inventory

Reference data

10.1029/2009jg000997

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Citations (83)

NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000

ORNL DAAC (2013)

Josef Kellndorfer Wayne Walker K. Kirsch Greg Fiske J. Bishop

Baseline (sea)

Carbon fibers

10.3334/ornldaac/1161

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Citations (37)

CO2-induced metal release from sandstones: Implications for geologic carbon sequestration

K. Kirsch

Carbon fibers

Source

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Citations (3)

Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS/PALSAR and Landsat Data Sources

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2010)

Wayne Walker Claudia Stickler Josef Kellndorfer K. Kirsch Daniel C. Nepstad

Information on the distribution of tropical forests is critical to decision-making on a host of globally significant issues ranging from climate stabilization and biodiversity conservation to poverty reduction and human health. The majority of tropical nations need high-resolution, satellite-based maps of their forests as the international community now works to craft an incentive-based mechanism to compensate tropical nations for maintaining their forests intact. The effectiveness of such a mechanism will depend in large part on the capacity of current and near-future Earth observation satellites to provide information that meets the requirements of international monitoring protocols now being discussed. Here we assess the ability of a state-of-the-art satellite radar sensor, the ALOS/PALSAR, to support large-area land cover classification as well as high-resolution baseline mapping of tropical forest cover. Through a comprehensive comparative analysis involving twenty separate PALSAR- and Landsat-based classifications, we confirm the potential of PALSAR as an accurate (>90%) source for spatially explicit estimates of forest cover based on data and analyses from a large and diverse region encompassing the Xingu River headwaters in southeastern Amazonia. Pair-wise spatial comparisons among maps derived from PALSAR, Landsat, and PRODES, the Brazilian Amazon deforestation monitoring program, revealed a high degree of spatial similarity. Given that a long-term data record consisting of current and future spaceborne radar sensors is now expected, our results point to the important role that spaceborne imaging radar can play in complementing optical remote sensing to enable the design of robust forest monitoring systems.

Land Cover

Deforestation

10.1109/jstars.2010.2076398

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Citations (130)