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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Estimation of Forest Parameters Combining Multisensor High Resolution Remote Sensing Data
摘要： Forest monitoring is a major issue to carry out energetic and environmental policies. Actual context in spaceborne remote sensing data is very promising. Our study aims to test the ability of SAR, optical and textural data to estimate forest parameters (biomass, height, diameter and density), and to evaluate the improvement of combining these remote sensing data. We worked on monospecific pine forest stands. The first results highlighted the synergy between SAR and spatial texture informations. Sentinel-1 C-band SAR data is very promising for the estimation of forest parameters in monospecifics stands. Biomass was estimated with 29.4% relative error (20.7 tons/ha) and height with 14.6% (2.1m) combining four SAR and optical sensors.
关键词： Forest,biomass,texture,SAR,optical
更新于2025-09-23 15:22:29
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Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data
摘要： Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present above-ground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and space-borne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the “best” estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha?1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62 % of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.
关键词： random forests,remote sensing,lidar,forest biomass,carbon storage,Alberta
更新于2025-09-23 15:21:01
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Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010
摘要： Forests with high above-ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using synthetic aperture radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR from the ALOS and lidar from the ICESat Earth-Observing satellites with 56 field plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there was 274 ± 68 Tg AGB remaining in natural forest (≥ 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4 % over the subsequent 3 years. A total of 137.4 kha of the study area was deforested between 2007 and 2010, an average rate of 3.8 % yr?1.
关键词： SAR,lidar,Sumatra,forest biomass,deforestation
更新于2025-09-10 09:29:36
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An approach to estimating forest biomass change over a coniferous forest landscape based on tree-level analysis from repeated lidar surveys
摘要： Forests represent a significant opportunity for carbon sequestration, but quantifying biomass change at the landscape scale and larger remains a challenge. Here we develop an approach based on repeated tree-level analysis using high-resolution airborne lidar (around 8 pulses/m2). The study area was 53 km2 of actively managed coniferous forestland in the Coast Range Mountains in western Oregon. The study interval was 2006–2012. Tree heights and crown areas were determined from the lidar data using point cloud clustering. Biomass per tree was estimated with allometry. Tree-level data (N = 14,709) from local USDA Forest Service Forest Inventory and Analysis plots provided the basis for the allometry. Estimated biomass change over the 6-year interval averaged ?1.3 kg m?2 year?1, with the average gain in undisturbed areas of 1.0 kg m?2 year?1. Full harvest occurred on 3% of the area per year. For surviving trees, the mean change in height was 0.5 m year?1 (SD = 0.3) and the mean change in biomass was 45.3 kg year?1 (SD = 6.7). The maximum bin-average increase in biomass per tree (57.3 kg year?1) was observed in trees of intermediate height (35–40 m). In addition to high spatially resolved forest biomass change, potential applications of tracking of repeated tree-level surveys include analysis of mortality. In this relatively productive forest landscape, an interval of 6 years between lidar acquisitions was adequate to resolve significant changes in tree height and area-wide biomass.
关键词： carbon sequestration,forest biomass,lidar,tree-level analysis,coniferous forest
更新于2025-09-04 15:30:14
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Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei
摘要： The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called “Volumetric pixel (voxel)” has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R2, adjusted R2, and standard deviation values of 0.92, 0.87, and 35.13 Mg·ha?1, respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg·ha?1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.
关键词： LiDAR,voxel,REDD+,volumetric pixel,forest biomass,forest carbon stock,climate change
更新于2025-09-04 15:30:14