Spectral Mixture Analysis and Geometric-Optical Reflectance Modeling of Boreal Forest Biophysical Structure

Peddle, Derek R.; Hall, Forrest G.; LeDrew, E.

doi:10.1016/s0034-4257(98)00090-x

Cited by 182 publications

(103 citation statements)

References 31 publications

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“…MODIS and Landsat have been able to effectively map 654 the 3D structure and characteristics of sparser forests: Landscape characteristics such as 655 areal extent, disturbance rates, landscape pattern metrics such as shape, size, contiguity, 656 edge density, and the vertical dimension, canopy crown dimensions and stem density 657 (Peddle et al 1996, Soenen et al 2009, Widlowski et al 2007) and biomass (Hall et al 658 1996). 659…”

Section: Fusion Of Pol-sar Measurements With Lidar Samplingmentioning

confidence: 99%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

187

118

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18Human and natural forces are rapidly modifying the global distribution and 19 structure of terrestrial ecosystems on which all of life depends, altering the global carbon 20 cycle, affecting our climate now and for the foreseeable future, causing steep reductions 21 in species diversity, and endangering Earth's sustainability. 22To understand changes and trends in terrestrial ecosystems and their functioning 23 as carbon sources and sinks, and to characterize the impact of their changes on climate, 24 habitat and biodiversity, new space assets are urgently needed to produce high spatial 25 resolution global maps of the three-dimensional (3D) structure of vegetation, its biomass 26 above ground, the carbon stored within and the implications for atmospheric green house 27 gas concentrations and climate. These needs were articulated in a 2007 National 28Research Council (NRC) report (NRC, 2007) recommending a new satellite mission, 29DESDynI, carrying an L-band Polarized Synthetic Aperture Radar (Pol-SAR) and a 30 multi-beam lidar (Light RAnging And Detection) operating at 1064 nm. The objectives 31 of this paper are to articulate the importance of these new, multi-year, 3D vegetation 32 structure and biomass measurements, to briefly review the feasibility of radar and lidar 33 remote sensing technology to meet these requirements, to define the data products and 34 measurement requirements, and to consider implications of mission durations. The paper 35 addresses these objectives by synthesizing research results and other input from a broad 36 community of terrestrial ecology, carbon cycle, and remote sensing scientists and 37 working groups. We conclude that: 38(1) current global biomass and 3-D vegetation structure information is unsuitable 39 for both science and management and policy. The only existing global datasets of 40 biomass are approximations based on combining land cover type and representative 41 carbon values, instead of measurements of actual biomass. Current measurement attempts 42 based on radar and multispectral data have low explanatory power outside low biomass 43areas. There is no current capability for repeatable disturbance and regrowth estimates. 44(2) The science and policy needs for information on vegetation 3D structure can 45 be successfully addressed by a mission capable of producing (i) a first global inventory of 46 forest biomass with a spatial resolution 1km or finer and unprecedented accuracy (ii) 47 annual global disturbance maps at a spatial resolution of 1 ha with subsequent biomass 48 accumulation rates at resolutions of 1km or finer, and (iii) transects of vertical and 49 horizontal forest structure with 30 m along-transect measurements globally at 25 m 50 spatial resolution, essential for habitat characterization. 51 We also show from the literature that lidar profile samples together with wall-to-52 wall L-band quad-pol-SAR imagery and ecosystem dynamics models can work together 53 to satisfy these vegetation 3D structure and biomass measurement requirements. ...

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Section: Fusion Of Pol-sar Measurements With Lidar Samplingmentioning

confidence: 99%

Characterizing 3D vegetation structure from space: Mission requirements

Hall¹,

Bergen²,

Blair³

et al. 2011

Remote Sensing of Environment

187

118

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“…Numerous algorithms have been tested for modeling forest structure, many of them employing simple or multiple linear regression models [20][21][22], or vegetation canopy models [11,[23][24][25]. The latter describe canopy architecture in both vertical and horizontal dimensions, but depend on a priori knowledge of canopy complexity, atmospheric conditions, illumination and viewing geometry, and topography [14].…”

Section: Objectives and Approachmentioning

confidence: 99%

Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians

et al. 2011

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Understanding the potential of forest ecosystems as global carbon sinks requires a thorough knowledge of forest carbon dynamics, including both sequestration and fluxes among multiple pools. The accurate quantification of biomass is important to better understand forest productivity and carbon cycling dynamics. Stand-based inventories (SBIs) are widely used for quantifying forest characteristics and for estimating biomass, but information may quickly become outdated in dynamic forest environments. Satellite remote sensing may provide a supplement or substitute. We tested the accuracy of aboveground biomass estimates modeled from a combination of Landsat Thematic Mapper (TM) imagery and topographic data, as well as SBI-derived variables in a Picea abies forest in the Western Carpathian Mountains. We employed Random Forests for non-parametric, regression tree-based modeling. Results indicated a difference in the importance of SBI-based and remote sensing-based predictors when estimating aboveground biomass. The most accurate models for biomass prediction ranged from a correlation coefficient of 0.52 for the TM-and topography-based model, to 0.98 for the inventory-based model. While Landsat-based biomass estimates were measurably less accurate than those derived from SBI, adding tree height or stand-volume as a field-based predictor to TM and topography-based models increased performance to 0.36 and 0.86, OPEN ACCESSRemote Sens. 2011, 3 1428 respectively. Our results illustrate the potential of spectral data to reveal spatial details in stand structure and ecological complexity.

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“…For these reasons TM imagery should be compared with AVHRR to investigate the potential for improving the temporal coverage. In addition, methodologies currently being developed for scaling of spectral imagery, such as spectral mixture analysis (Peddle et al 1999), and the use of semi-variograms (Friedl 1996) need to be tested over the subarctic landscape.…”

Section: Discussionmentioning

confidence: 99%

Radiation balance of wetland tundra at northern treeline estimated from remotely sensed data

Duguay

Wr²,

Lafleur³

et al. 1999

Clim. Res.

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Traditional methods of measuring surface net radiation involve point measurements that represent only a small area surrounding the instrumented sites. Remotely sensed spaceborne data offer the means by which to obtain estimates of the outgoing fluxes at the regional scale. The objective of this study was to estimate surface albedo, surface thermal exitance, and net radiation using Landsat Thematic Mapper (TM) data over wetland tundra at northern treeline near Churchill, Manitoba, Canada. Ground-based measurements of each component of the radiation balance were acquired at 5 locations coincident with 2 TM overpasses during summer 1991. Each location was representative of 1 of the major terrain types found in the Hudson Bay Lowlands (i.e. sedge-dominated wetland, upland lichenheath, tundra lakes and ponds, willow/birch wetland, and open spruce-tamarack forest). The mean absolute differences between remote sensing estimates and field measurements (all sites combined) are 0.01 for albedo, 25.7 W m -2 for thermal exitance, and 14.1 W m -2 for net radiation. The 2 summer 1991 TM images (June and August) were then used to examine within and between terrain type variations in surface net radiation during the growing season. TM imagery from August 1984 and August 1991 were also utilised to investigate differences in surface fluxes between a dry year (1984) and a wet year (1991). Results indicate that surface wetness and, to a lesser extent, phenology are the 2 main factors controlling the radiation balance during the summer period in this subarctic tundra-forest landscape.

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Spectral Mixture Analysis and Geometric-Optical Reflectance Modeling of Boreal Forest Biophysical Structure

Cited by 182 publications

References 31 publications

Characterizing 3D vegetation structure from space: Mission requirements

Characterizing 3D vegetation structure from space: Mission requirements

Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians

Radiation balance of wetland tundra at northern treeline estimated from remotely sensed data

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