Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Chopping, Mark; Moisen, Gretchen G.; Su, Lihong; Laliberte, Andrea S.; Rango, A.; Martonchik, John V.; Peters, Debra P. C.

doi:10.1016/j.rse.2007.07.024

Cited by 132 publications

(76 citation statements)

References 21 publications

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“…Kalacska et al [65] use spectral remote sensing with EO-1 Hyperion hyperspectral satellite imagery to estimate forest structure in dry tropical forests. Chopping et al [66] measure canopy characteristics using NASA multi-angle imaging spectro-radiometer (MISR) data. They worked in a shrub and coniferous dominated region of southeastern Arizona and southern New Mexico, measuring woody biomass, canopy cover and mean canopy height.…”

Section: Discussionmentioning

confidence: 99%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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Section: Discussionmentioning

confidence: 99%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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“…The view angles include 0°, −20°, −44°, −5°, and 20°, 44°, 55°. These angles covered typical observation angles of multi-angle systems like MISR and CHRIS/PROBA [26,28]. The hot-spot and cold-spot angles were calculated according to the geo-location and observation date.…”

Section: Data Collection Using Lichy Systemmentioning

confidence: 99%

“…Forest parameters from LiDAR data are widely used as reference data [7,12], but the extrapolation of these limited LiDAR estimates to a broad regions or continuous mapping is typically based on the currently available satellite data like MODIS or MISR [26,40]. Multi-angular hyperspectral observations can help validate these satellite products while being beneficial for future joint LiDAR and optical sensor satellite mission for forest monitoring purposes [25,27,41].…”

Section: Potential Forest Applications Of Data From Lichy Systemmentioning

confidence: 99%

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

Pang

et al. 2016

Remote Sensing

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Abstract:We describe the design, implementation and performance of a novel airborne system, which integrates commercial waveform LiDAR, CCD (Charge-Coupled Device) camera and hyperspectral sensors into a common platform system. CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System is a unique system that permits simultaneous measurements of vegetation vertical structure, horizontal pattern, and foliar spectra from different view angles at very high spatial resolution (~1 m) on a wide range of airborne platforms. The horizontal geo-location accuracy of LiDAR and CCD is about 0.5 m, with LiDAR vertical resolution and accuracy 0.15 m and 0.3 m, respectively. The geo-location accuracy of hyperspectral image is within 2 pixels for nadir view observations and 5-7 pixels for large off-nadir observations of 55˝with multi-angle modular when comparing to LiDAR product. The complementary nature of LiCHy's sensors makes it an effective and comprehensive system for forest inventory, change detection, biodiversity monitoring, carbon accounting and ecosystem service evaluation. The LiCHy system has acquired more than 8000 km 2 of data over typical forests across China. These data are being used to investigate potential LiDAR and optical remote sensing applications in forest management, forest carbon accounting, biodiversity evaluation, and to aid in the development of similar satellite configurations. This paper describes the integration of the LiCHy system, the instrument performance and data processing workflow. We also demonstrate LiCHy's data characteristics, current coverage, and potential vegetation applications.

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“…Remote sensing is an efficient means of repeatedly monitoring the Earth in a consistent manner. However, forest biomass and height mapping using existing optical and thermal sensors is challenging because these sensors are primarily responsive to canopy cover, rather than vertical structure (Chopping et al, 2008). High resolution stereo-optical and radar images have been used to map forest structure, but these are of limited use in dense or structurally heterogeneous forests (Hyde et al, 2006;Boudreau et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

Wang

Ding

et al. 2016

Remote Sensing of Environment

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Mapping the global distribution of forest canopy height is important for estimating forest biomass and terrestrial carbon flux. In this study, we present a global map of mean forest canopy height at 500 m spatial resolution obtained by combining Geoscience Laser Altimeter System (GLAS) data acquired from 2005 to 2006 and 13 ancillary variables, including seven climatic variables and six remote sensing variables (nadir BRDF-adjusted reflectance at red and NIR bands, tree cover, anisotropic factor, accumulated Enhanced Vegetation Index, and elevation). The original contributions of this study include the following: (1) The wavelet method was applied to complement the GLA14 product to identify the ground peak and the top-canopy peak. We found that it was useful for dealing with waveforms with low reconstruction accuracy. (2) GLAS data from the leafless season were not used for nonevergreen forest because the height retrieval results exhibited underestimation and strong variations. (3) The anisotropic factor (ANIF), an indicator related to surface structure, was included as an ancillary variable for the first time and was determined to be important for height modeling in the Asian and North American regions. (4) The balanced random forest (BRF) algorithm was applied to register GLAS mean forest canopy height to a 500 m grid considering the small proportion of extreme height classes (tall and short trees), and it achieved good performance in terms of modeling accuracy (RMSE = 2.75 to 4.45 m) and preserving data variation. An inter-comparison among three global forest height maps [the present study, Lefsky (2010), andSimard et al. (2011)] was implemented in a pixel-by-pixel manner. High agreement (R 2 = 0.73, RMSE = 4.49 m) was determined between the present study and Simard et al., whereas the result from Lefsky was notably different from the other two results (R 2 = 0.14, RMSE = 8.92 m, compared with the present study; R 2 = 0.11, RMSE = 11.19 m, compared with Simard et al.). Large disparities were generally associated with evergreen broadleaf forests in South America, deciduous needleleaf forests in Europe and Russian North Asia, and evergreen needleleaf forests on the West Coast of North America. Differences in the height metric were a main factor affecting the disparities among the three results. Validation against field survey data acquired from the Distributed Active Archive Center indicated the accuracy of our mean forest canopy height map (R 2 = 0.63, RMSE = 4.68 m, n = 59).

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Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Cited by 132 publications

References 21 publications

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

LiCHy: The CAF’s LiDAR, CCD and Hyperspectral Integrated Airborne Observation System

A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height

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