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Monitoring vegetation coverage in Tongren from 2000 to 2016 based on Landsat7 ETM+ and Landsat8
摘要： Vegetation coverage is an important indicator in regional ecological environment monitoring and plays a key role in its quality assessment. We consider Landsat7 ETM+ in 2000 and Landsat8 in 2016 as data sources using a different time phase partial image substitution method to eliminate cloud effects and an NDVI dimidiate pixel model to invert the vegetation coverage of the two time phases. We further classify them into five grades, provide statistics and analyse the areas of different grades at different time periods, while monitoring the spatial evolution of vegetation coverage over the past 16 years in Tongren. Experimental results showed that: (1) the different time phase partial image substitution method could reduce the influence of clouds on vegetation extraction; (2) in Tongren, the vegetation coverage area was decreased from 17,300.1 km2 to 17,224.8 km2 (i.e. decreased by 75.3 km2); (3) the areas of grade I and V increased by 0.42% and 15.08%, respectively, whereas the areas of grade II, III and IV decreased by 3.15%, 6.98% and 5.37%, respectively, which indicates that most of the area containing lower amount of vegetation gradually altered into an area containing a higher coverage of vegetation, whereas a few areas became bare land; and (4) the vegetation areas decreased due to expansion of cities and construction of dams, while vegetation increased due to the cultivation of crops and trees. Research shows that the overall evolution of vegetation coverage in Tongren is considerably good. However, while undertaking future development in the mountainous Karst region, one should be aware of the land’s intensive use and environmental protection.
关键词： Landsat8,change detection,China Tongren,Landsat7 ETM+,vegetation coverage
更新于2025-09-23 15:22:29
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FVI—A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0–1.24 μm Spectral Range for the Detection of Vegetation Floating over Water Surfaces
摘要： Through the analysis of hyperspectral imaging data collected over water surfaces covered by floating vegetation, such as Sargassum and algae, we observed that the spectra commonly contain a reflectance peak centered near 1.07 μm. This peak results from the competing effects between the well-known vegetation reflectance plateau in the 0.81–1.3 μm spectral range and the absorption effects above 0.75 μm by liquid water within the vegetation and in the surrounding water bodies. In this article, we propose a new index, namely the floating vegetation index (FVI), for the hyperspectral remote sensing of vegetation over surface layers of oceans and inland lakes. In the formulation of the FVI, one channel centered near 1.0 μm and another 1.24 μm are used to form a linear baseline. The reflectance value of the third channel centered at the 1.07-μm reflectance peak above the baseline is defined as the FVI. Hyperspectral imaging data acquired with the AVIRIS (Airborne Visible Infrared Imaging Spectrometer) instrument over the Gulf of Mexico and over salt ponds near Moffett Field in southern portions of the San Francisco Bay were used to demonstrate the success in detecting Sargassum and floating algae with this index. It is expected that the use of this index for the global detection of floating vegetation from hyperspectral imaging data to be acquired with future satellite sensors will result in improved detection and therefore enhanced capability in estimating primary production, a measure of how much carbon is fixed per unit area per day by oceans and inland lakes.
关键词： Sargassum,sensors,remote sensing,hyperspectrum,vegetation index,algae
更新于2025-09-23 15:22:29
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A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle
摘要： Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.
关键词： UAV,multispectral imagery,radiative transfer model,LNC,vegetation index,non-parametric regression
更新于2025-09-23 15:22:29
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Advances in Microclimate Ecology Arising from Remote Sensing
摘要： Microclimates at the land–air interface affect the physiological functioning of organisms which, in turn, influences the structure, composition, and functioning of ecosystems. We review how remote sensing technologies that deliver detailed data about the structure and thermal composition of environments are improving the assessment of microclimate over space and time. Mapping landscape-level heterogeneity of microclimate advances our ability to study how organisms respond to climate variation, which has important implications for understanding climate-change impacts on biodiversity and ecosystems. Interpolating microclimate measurements and downscaling macroclimate provides an organism-centered perspective for studying climate–species interactions and species distribution dynamics. We envisage that mapping of microclimate will soon become commonplace, enabling more reliable predictions of species and ecosystem responses to global change.
关键词： microclimate,ecology,vegetation structure,climate change,remote sensing,biodiversity,thermal imaging,LiDAR
更新于2025-09-23 15:22:29
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - Coherent Scattering and PolinSAR Imaging Simulation of Fractal Trees
摘要： A 3-D deterministic spatial structure of forest was built and used to calculate the coherent electromagnetic scattering. The trees were generated based on the rule-based growth algorithm. The trunks, branches and leaves in the trees were simplified as a cluster of cylinders and disks, in which every scatterer has its deterministic location and orientation. The generalized Rayleigh-Gans (GRG) approximation and the infinite cylinder approximation are used to calculate the scattering matrix of cylinders and disks. A scatterer above a dielectric plane mainly contributes four scattering components, and the scattering of each component was added coherently. The effects of attenuation and phase change in each component were taking into account using Foldy’s approximation. The scattering coefficient of one tree and a forest were both calculated, which was used to simulate PolinSAR images.
关键词： fractal tree,vegetation scattering simulation,coherent scattering,multi-ray approach
更新于2025-09-23 15:22:29
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Rela??es empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5
摘要： The objective of this work was to adjust models to estimate dendrometric characteristics of the Brazilian dry tropical forest (Caatinga) from Landsat 5 TM sensor data. Measures for tree diameter and height were taken in 60 inventory plots (400 m2), in two municipalities of the state of Sergipe, Brazil. Basal area and wood volume were estimated using an allometric equation and form factor (f = 0.9). Explanatory variables were taken from the TM sensor, after radiometric and geometric correction, having considered, in the analysis, six spectral bands, with 30 m spatial resolution, besides the indexes of simple ratio (SR), of normalized difference vegetation (NDVI), and of soil-adjusted vegetation (Savi). To choose the best explanatory variables, the coefficient of determination (R2), the root mean square error (RMSE), and the Bayesian information criterion (BIC) were considered. The basal area per hectare did not show a significant correlation with any of the explanatory variables used. The best models were adjusted to tree mean height per plot (R2 = 0.4; RMSE = 13%) and to wood volume per hectare (R2 = 0.6; RMSE = 42%). The metrics derived from the Landsat 5 TM sensor have great potential to explain variation in the mean height of trees and in the wood volume per hectare, in remaining areas of the tropical dry forest in the Brazilian Northeast.
关键词： Savi,REDD,vegetation index,reducing emissions,remote sensing,NDVI
更新于2025-09-23 15:22:29
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Use of Sun-Induced Chlorophyll Fluorescence Obtained by OCO-2 and GOME-2 for GPP Estimates of the Heihe River Basin, China
摘要： Sun-induced chlorophyll ?uorescence (SIF) provides a new method for monitoring vegetation photosynthesis from space and has been widely used to estimate gross primary productivity (GPP). However, the ability of SIF obtained from the Orbital Carbon Observatory 2 (OCO-2 SIF) and Global Ozone Monitoring Experiment-2 (GOME-2) to estimate GPP in the cold and arid region of Heihe River Basin remains unclear because previous comparisons were insuf?cient. Here, we choose maize and alpine meadow to evaluate the performance of SIF obtained by OCO-2 and GOME-2 in GPP estimations. The results of this study show that daily SIF757 had stronger correlations with daily tower GPP than daily SIF771, and the correlation between daily SIF757 and daily tower GPP was stronger than the correlation between 16-d averaged SIF740 and 16-d averaged tower GPP. The 16-d averaged absorbed photosynthetically active radiation (APAR) and reconstructed sun-induced ?uorescence (RSIF) had the strongest linear correlations with 16-d averaged tower GPP. GPP_VPM and GPP_RSIF exhibited the best performance in GPP estimation, closely followed by GPP_SIF757, then GPP_SIF771 and GPP_ SIF740. We also found that the robustness of the correlation coef?cients of OCO-2 SIF with GOME-2 SIF was highly dependent on the size of their spatial footprint overlaps, indicating that the spatial differences between OCO-2 and GOME-2 footprints contribute to the differences in GPP estimates between OCO-2 and GOME-2. In addition, the differences of viewing zenith angle (VZA), cloud contamination, scale effects, and environmental scalars (Tscalar × Wscalar) can result in differences between OCO-2 SIF and GOME-2 SIF.
关键词： vegetation photosynthesis model,eddy covariance,sun-induced ?uorescence,gross primary productivity,carbon cycle
更新于2025-09-23 15:22:29
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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 - Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach
摘要： Estimating parameters for global climate models via combined active and passive microwave remote sensing data has been a subject of intensive research in recent years. A variety of retrieval algorithms has been proposed for the estimation of soil moisture, vegetation optical depth and other parameters. A novel attenuation-based retrieval approach is proposed here to globally estimate the gravimetric moisture of vegetation (????) and retrieve information about the amount of water [kg] per amount of wet vegetation [kg]. The parameter ???? is particularly interesting for agro-ecosystems, to assess the status of growing vegetation. The key feature of the proposed approach is that it relies on multi-sensor data from three sensor types (microwave radar, microwave radiometer, and lidar) to solve the physics equations and obtain ????-estimates. The comparability of these estimates to literature values as well as to results of a globally applied, retrieval approach of Grant [4], reveal the potential of the developed method.
关键词： lidar,radiometer,Multi-sensor,SMAP,vegetation water content,vegetation optical depth,radar
更新于2025-09-23 15:21:21
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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 - Evaluation of the Vegetation Optical Depth Index on Monitoring Fire Risk in the Mediterranean Region
摘要： Monitoring live fuel moisture content (LFMC) in Mediterranean area is of great importance for fire risk assessment. LFMC has extensively been estimated based on optical remote sensing data. But the latter can be affected by atmospheric effects. As a complementary data source, microwave data can be used as they are relatively insensitive to atmospheric effects. Yet further evaluations are needed to investigate the potential of microwave observations to monitor LFMC. In this study, we assess the capability of long-term microwave vegetation optical depth (VOD) to capture the temporal variability of in situ measured LFMC in 14 Mediterranean shrub species in southern France during 1996-2014. Microwave-derived VOD at X band (VODX-15) displayed a high sensitivity to LFMC with correlation coefficients of 0.56. Similar evaluations were made using four optical indices computed from the Moderate Resolution Imaging Spectrometer (MODIS) data including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), visible atmospheric resistant index (VARI), normalized difference water index (NDWI). The comparisons showed that VARI performs better than VODX-15 and other optical indices with highest median of correlation coefficients of 0.65. Overall, this study shows that passive microwave-derived VOD, are efficient proxies for LFMC of Mediterranean shrub species and could be used along with optical indices to evaluate fire risks in the Mediterranean region.
关键词： vegetation optical depth,fire risk,microwave remote sensing,live fuel moisture content
更新于2025-09-23 15:21:21
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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 - Assessment of the Ground Polarimetry in Crops Estimated Using MB Sar Interferometry at C-Band
摘要： In this paper, polarimetric multi-baseline (MB) Synthetic Aperture Radar (SAR) Interferometry data are used to estimate the polarimetric ground component under vegetation. However, the solution of the applied separation algorithm is not unique and depends on the constraints in the regularization. First, the effect of this non-uniqueness is analyzed and then exploited to isolate a ground component with minimized influence of depolarizing scattering mechanisms. Using experimental MB SAR data acquired by DLR’s airborne sensor F-SAR, the polarimetric entropy and mean alpha angle of the isolated ground component are compared to the original polarimetry of the full image. Finally, the ground polarimetry is interpreted for changing soil moisture vegetation conditions in corn. To this purpose, three dates are compared characterized by 1) a change in soil moisture, 2) a change in vegetation cover or 3) a simultaneous change of soil moisture and vegetation cover.
关键词： Agricultural Vegetation,SAR Interferometry,Soil moisture
更新于2025-09-23 15:21:21