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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data
摘要： Changes in forest areas have great impact on a range of ecosystem functions, and monitoring forest change across di?erent spatial and temporal resolutions is a central task in forestry. At the spatial scales of municipalities, forest properties and stands, local inventories are carried out periodically to inform forest management, in which airborne laser scanner (ALS) data are often used to estimate forest attributes. As local forest inventories are repeated, the availability of bitemporal ?eld and ALS data is increasing. The aim of this study was to assess the utility of bitemporal ALS data for classi?cation of dominant height change, aboveground biomass change, forest disturbances, and forestry activities. We used data obtained from 558 ?eld plots and four repeated ALS-based forest inventories in southeastern Norway, with temporal resolutions ranging from 11 to 15 years. We applied the k-nearest neighbor method for classi?cation of: (i) increasing versus decreasing dominant height, (ii) increasing versus decreasing aboveground biomass, (iii) undisturbed versus disturbed forest, and (iv) forestry activities, namely untouched, partial harvest, and clearcut. Leave-one-out cross-validation revealed overall accuracies of 96%, 95%, 89%, and 88% across districts for the four change classi?cations, respectively. Thus, our results demonstrate that various changes in forest structure can be classi?ed with high accuracy at plot level using data from repeated ALS-based forest inventories.
关键词： classi?cation,dominant height,forest change,ALS,forestry activity,aboveground biomass,disturbance,forest
更新于2025-09-23 15:19:57
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Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass
摘要： Above ground biomass (AGB) is a parameter commonly used for assessment of grassland systems. Destructive AGB measurements, although accurate, are time consuming and are not easily undertaken on a repeat basis or over large areas. Structure-from-Motion (SfM) photogrammetry and Terrestrial Laser Scanning (TLS) are two technologies that have the potential to yield precise 3D structural measurements of vegetation quite rapidly. Recent advances have led to the successful application of TLS and SfM in woody biomass estimation, but application in natural grassland systems remains largely untested. The potential of these techniques for AGB estimation is examined considering 11 grass plots with a range of biomass in South Dakota, USA. Volume metrics extracted from the TLS and SfM 3D point clouds, and also conventional disc pasture meter settling heights, were compared to destructively harvested AGB total (grass and litter) and AGB grass plot measurements. Although the disc pasture meter was the most rapid method, it was less effective in AGB estimation (AGBgrass r2 = 0.42, AGBtotal r2 = 0.32) than the TLS (AGBgrass r2 = 0.46, AGBtotal r2 = 0.57) or SfM (AGBgrass r2 = 0.54, AGBtotal r2 = 0.72) which both demonstrated their utility for rapid AGB estimation of grass systems.
关键词： Structure-from-Motion (SfM) photogrammetry,disc pasture meter,grass,aboveground biomass (AGB),compact biomass LiDAR (CBL),terrestrial laser scanning (TLS),volume
更新于2025-09-16 10:30:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Monitoring the Spatio-Temporal Variations of C3/C4 Grass Species Using Multispectral Satellite Data
摘要： Grass that follows a C3 and C4 photosynthetic pathway represents a fundamental component of grass species functional type, with outstanding services in grassland ecosystems. These grasses differ in morphology, phenology and physiological features, over time, due to varying environmental requirements. These features determine the success of their discrimination using remotely sensed data and their response in Aboveground Biomass (AGB) over time. For decades, the lack of appropriate remote sensing data sources compromised C3 and C4 grasses monitoring over space and time. This has resulted in uncertainties in understanding their potential and contribution to the provision of services. The aim of this study was to determine the optimal period to discriminate C3 and C4 grass species. The study additionally estimated species AGB over space and time. This was achieved, using Sentinel 2 satellite data with Discriminant Analysis (DA) and sparse partial least squares regression (SPLSR) algorithms. The winter peak was shown to present the best temporal window for discriminating C3 and C4 grasses. This period was also associated with higher species AGB (±1.11kg/m2) for both species as they have reached their peak. Although summer period was associated with reasonably high classification accuracies, highest errors (±20%) were encountered and this period had higher AGB, as both species were in their early stages of growth. The discrimination of C3 and C4 and AGB variations were significantly (α = 0.05) contributed by red edge, NIR and SWIR portions of the electromagnetic spectrum. These variables managed to capture species phenological, physiological and morphological contrasts as well as spatial variations over time.
关键词： classification error,seasonal changes,temporal window,aboveground biomass
更新于2025-09-11 14:15:04
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - SMOS-IC Vegetation Optical Depth Index in Monitoring Aboveground Carbon Changes in the Tropical Continents During 2010–2016
摘要： Tropical aboveground carbon changes during 2010-2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010-2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010-2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.
关键词： carbon changes,SMOS-IC,aboveground biomass,vegetation optical depth,tropical region
更新于2025-09-10 09:29:36