- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features
摘要： The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.
关键词： Red Green Ratio Index (RGRI),Normalized Difference Flood Index (NDFI),COSMO-SkyMed,Random Forest,Enhanced Vegetation Index (EVI),multi-temporal,summer crops,Landsat 8 OLI,rule-based classification,agriculture
更新于2025-09-10 09:29:36
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Extending the SCOPE model to combine optical reflectance and soil moisture observations for remote sensing of ecosystem functioning under water stress conditions
摘要： A radiative transfer and process-based model, called Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), relates remote sensing signals with plant functioning (i.e., photosynthesis and evapotranspiration). Relying on optical remote sensing data, the SCOPE model estimates photosynthesis and evapotranspiration, but these ecosystem-level fluxes may be significantly overestimated if water availability is the primary limiting factor for vegetation. Remedying this shortcoming, additional information from extra sources is needed. In this study, we propose considering water stress in SCOPE by incorporating soil moisture data in the model, besides using satellite optical reflectance observations. A functional link between soil moisture, soil surface resistance, leaf water potential and carboxylation capacity is introduced as an extra element in SCOPE, resulting in a soil moisture integrated version of the model, SCOPE-SM. The modified model simulates additional state variables: (i) vapor pressure (ei), both in the soil pore space and leaf stomata in equilibrium with liquid water potential; (ii) the maximum carboxylation capacity (Vcmax) by a soil moisture dependent stress factor; and (iii) the soil surface resistance (rss) through approximation by a soil moisture dependent hydraulic conductivity. The new approach was evaluated at a Fluxnet site (US-Var) with dominant C3 grasses and covering a wet-to-dry episode from January to August 2004. By using the original SCOPE (version 1.61), we simulated half-hourly time steps of plant functioning via locally measured weather data and time series of Landsat (TM and ETM) imagery. Then, SCOPE-SM was similarly applied to simulate plant functioning for three cases using Landsat imagery: (i) with modeled ei; (ii) with modeled ei and Vcmax; and (iii) with modeled ei, Vcmax, and rss. The outputs of all four simulations were compared to flux tower plant functioning measurements. The results indicate a significant improvement proceeding from the first to the fourth case in which we used both Landsat optical imagery and soil moisture data through SCOPE-SM. Our results show that the combined use of optical reflectance and soil moisture observations has great potential to capture variations of photosynthesis and evapotranspiration during drought episodes. Further, we found that the information contained in soil moisture observations can describe more variations of measured evapotranspiration compared to the information contained in thermal observations.
关键词： SCOPE-SM model,Landsat,Evapotranspiration,Vegetation properties,Water stress,Remote sensing,Soil moisture,Vegetation functioning,Vapor pressure,Photosynthesis,Maximum carboxylation capacity,Soil surface resistance,Reflectance
更新于2025-09-09 09:28:46
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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 - Remote Sensing and Landscape Metrics for Evaluation of Secondary Vegetation Patterns in the Forest Fragmentation in an Area of the Brazilian Amazon
摘要： Deforestation in the Amazon biome has caused an intense process of fragmentation of its forests. This paper aims to evaluate secondary vegetation patterns in the forest fragmentation in an area of Brazilian Amazon, using remote sensing products and landscape metrics. The calculation of the metrics was performed using the Patch Analyst tool[1], a free extension of ArcGis?, which has the ability to quantify the composition and spatial configuration of a scenario, which can be presented by a remote sensing map or image. Preliminary results have shown that secondary vegetation contributed to improve the vegetation patches mean shape index (MSI), however, it is responsible for increasing the level of fragmentation in the study area.
关键词： Secondary Vegetation,Amazon,Landscape Metrics,Remote Sensing,Forest Fragmentation
更新于2025-09-09 09:28:46
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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 - Experimental Validation of Compact Tomosar for Vegetation Characterization
摘要： The study aims to explore the potentials of compact TomoSAR for vegetation characterization. The compact mode transmits either in linear or circular polarized waves and receives at horizontal and vertical polarization providing a different perspective to the understanding of the target. The goal of this study is to assess and evaluate the performance of compact polarimetric SAR modes to reconstruct the 3D reflectivity of forest volume and estimate the vertical structure in comparison with FP modes. Preliminary investigation of compact TomoSAR is conducted using L-band BIOSAR 2008 dataset consisting of six flight tracks acquired over Krycklan in northern Sweden.
关键词： SAR Tomography,Reflectivity,Vegetation Structure,Compact Polarimetry,PolInSAR
更新于2025-09-09 09:28:46
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Histogram-Based Autoadaptive Filter for Destriping NDVI Imagery Acquired by UAV-Loaded Multispectral Camera
摘要： Combinations of unmanned aerial vehicles (UAVs) and multispectral sensors provide low-cost approaches for detailed spatiotemporal vegetation studies. However, the resulting vegetation index images such as normalized difference vegetation index (NDVI) have unignorable stripe noise and seriously disturb the extraction of vegetation information. In this letter, the similar frequency phenomena caused by stripe noise were observed in the gray-scale histogram and the Fourier spectrum of a striped NDVI image. Thus, we tried establishing the empirical quantitative relationship between them, and then designed an autoadaptive ?lter for stripe noise removal in Fourier spectrum according to the characteristics including the dominant peak and troughs of the histogram curve of the raw NDVI image. Applying this autoadaptive ?lter to the corresponding Fourier spectrum image, we achieved automatic and effective stripe noise removal without any manual interference. Based on visual judgment and quantitative evaluation, the proposed autoadaptive ?lter demonstrated by far the better performance in retaining image ?delity and intelligibility than the improved high-pass ?lter and 2-D Weiner ?lter.
关键词： Agricultural multispectral camera (ADC) lite,auto-adaptive ?lter,stripe noise,image histogram,unmanned aerial vehicles (UAV),Fourier spectrum,normalized difference vegetation index (NDVI)
更新于2025-09-09 09:28:46
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Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation
摘要： To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, GRVI showed the most signi?cant correlations with NDVI among all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN ? RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.
关键词： phenology,passive sensor,greenness index,NDVI,RGB camera,active sensor,Svalbard,vegetation
更新于2025-09-09 09:28:46
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The ATL08 land and vegetation product for the ICESat-2 Mission
摘要： After being launched in September 2018, measurements from the ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) will be available to the science community starting in the Spring of 2019. These data offer new possibilities for the mapping of global terrain and vegetation as well as monitoring of the Earth's carbon stocks. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 will utilize photon counting technology for the altimetry observations. Photon counting technology is relatively new to the vegetation mapping community thus requires development of new algorithm approaches for terrain surface and canopy height retrievals. Photon counting systems provide range measurements for individual photons given the instrument's detection sensitivity. The sensitivity enables higher laser repetition rates for improved spatial coverage but is susceptible to solar background noise making the separation of signal and noise challenging and the data volume large. The algorithm developed specifically for the extraction of terrain and canopy heights from the ATLAS point clouds produces the ATL08 geophysical data product. This paper provides a detailed description of the ATL08 methodology, presents the data format, discusses many of the critical parameters likely to be of interest to future ICESat-2 data users, and describes the predicated uncertainties for terrain and canopy heights using two simulated ATLAS data sets. The first critical function in the ATL08 algorithm needs to accurately retrieve the surface is to identify the signal photons apart from the noise photons. Using a series of iterative filters, the ground and top of canopy surfaces are then identified in the signal. Next, individual photons are classified as either noise, canopy, or ground photons based on their distance above (or below) the estimated ground and top of canopy surfaces. The ATL08 algorithm has been tested on several simulated ATLAS data sets, and the results from two different ecosystems are described. The terrain extraction results saw sub-meter RMSE for the Alaska Tundra/Taiga ecotone and < 2 m RMSE in Sonoma County, California which is characterized by complex topography and dense vegetation. Although canopy heights on the ATL08 data product will underestimate the true canopy height within a segment, the ICESat-2 derived canopy height is found to be correlated with relative height metrics produced from airborne lidar (i.e. truth). For the sparse boreal forests of Alaska, the ATL08 canopy height was most correlated with the 95th percentile relative height (RH95). However, for the dense coniferous forests of Sonoma County, CA, ATL08 canopy height is correlated with 75th percentile relative height (RH75). Data from ICESat-2 will provide a new and exciting data set to the scientific community by providing global terrain and canopy height estimates as well as showing potential for estimation of forest biomass.
关键词： Lidar,Vegetation,Terrain,Land,ICESat-2
更新于2025-09-09 09:28:46
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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 Dimensional Reduction Methods on Urban Vegegation Classification Performance Using Hyperspectral Data
摘要： In the context of urban vegetation, hyperspectral imagery allows to discriminate biochemical properties of land surfaces. In this study, we test several dimension reductions to evaluate capacities of hyperspectral sensors to characterize tree families. The goal is to evaluate if a selection of differentiated and uncorrelated vegetation indices is an efficient method to reduce the dimension of hyperspectral images. This method is compared with conventional MNF and ACP approaches, and assessed on tree vegetation classifications performed using SVM classifier on two datasets at 4m and 8m spatial resolution. Results show that MNF combined with SVM classification is the better method to reduce hyperspectral dimension.
关键词： Urban vegetation,dimension reduction,SVM,hyperspectral data
更新于2025-09-09 09:28:46
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From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure
摘要： Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.
关键词： vegetation structure,HPC-based intensity surface,percentile height,tree segmentation,gridding,hyper point cloud (HPC),full waveform LiDAR
更新于2025-09-09 09:28:46
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Urban Land Use/Land Cover Discrimination Using Image-Based Reflectance Calibration Methods for Hyperspectral Data
摘要： Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods.
关键词： urban LULC classification,hyperspectral data,Vegetation-Impervious-Soil classes,EO-1 Hyperion,reflectance calibration
更新于2025-09-09 09:28:46