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- 摘要
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- 实验方案
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Seasonal Variation in Spectral Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg (Germany)
摘要： Submerged macrophytes are important structural components of freshwater ecosystems that are widely used as long-term bioindicators for the trophic state of freshwater lakes. Climate change and related rising water temperatures are suspected to affect macrophyte growth and species composition as well as the length of the growing season. Alternative to the traditional ground-based monitoring methods, remote sensing is expected to provide fast and effective tools to map submerged macrophytes at short intervals and over large areas. This study analyses interrelations between spectral signature, plant phenology and the length of growing season as influenced by the variable water temperature. During the growing seasons of 2011 and 2015, remote sensing reflectance spectra of macrophytes and sediment were collected systematically in-situ with hyperspectral underwater spectroradiometer at Lake Starnberg, Germany. The established spectral libraries were used to develop reflectance models. The combination of spectral information and phenologic characteristics allows the development of a phenologic fingerprint for each macrophyte species. By inversion, the reflectance models deliver day and daytime specific spectral signatures of the macrophyte populations. The subsequent classification processing chain allowed distinguishing species-specific macrophyte growth at different phenologic stages. The analysis of spectral signatures within the phenologic development indicates that the invasive species Elodea nuttallii is less affected by water temperature oscillations than the native species Chara spp. and Potamogeton perfoliatus.
关键词： spectral library,bioindication,remote sensing reflectance modeling,submerged aquatic vegetation,phenologic variations
更新于2025-09-10 09:29:36
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[Sustainable Development Goals Series] Remote Sensing for Food Security || Monitoring Drought from Space and Food Security
摘要： Drought is a typical phenomenon of the Earth’s climate. Losses from droughts, especially in agriculture, are staggering. Even in the USA, a country of the most advanced technology, the average annual cost of drought is around $6 billion. However, in extreme drought years such as 1988, costs jumped to $60 billion. During 2001–2017, nearly 20% of global lands were drought-stricken, almost every year and in some years this number jumped up much higher. Developing countries of Africa and Asia have always been the most drought-affected, especially if drought reduced agricultural production and they are facing food security problem. In the twenty-first century, the Horn of Africa experienced several-year droughts, which affected 13 million people in 2011 causing very serious food shortages and hunger. Mongolia’s rangelands suffer from very intensive droughts resulting in a lack of feed for livestock every 2–4 years. Unusual summer dryness affected the main grain-producing countries of the Black and Caspian Seas regions in 2007, 2009, 2010, 2012, and 2013 (AMIS 2017; NOAA 2017; Kogan and Guo 2014; NCDC 2011).
关键词： Climate Change,Remote Sensing,Drought,Food Security,Vegetation Health
更新于2025-09-10 09:29:36
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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 - Development of Fusion Approach for Estimation of Vegetation Fraction Cover with Drone and Sentinel-2 Data
摘要： Fractional vegetation cover (FVC) is usually referred to as an important parameter for vegetation health monitoring and also used as control parameter in terrestrial ecosystem change detection. In recent year several models have been developed for FVC measurement using satellite data and digital images at regional and global scale. For the validation and modification in these models need to a precise ground truth information. FVC measured using digital camera act as an efficient ground truth information, but it is also lack in accuracy due to limited number of images and sampling points are possible to take with camera. Drone is the recent trend for precision agriculture monitoring and can be used as substitute to overcome these problems. In this paper an efficient method of ground truth FVC measurement using drone image is developed, which is further used for development of a sigmoid model to measure FVC using Sentinel-2 data for larger area. Results of the obtained model are compared with ground truth FVC and obtained value of RMSE is 0.10. FVC are also measured with dimidiate pixel model and obtained RMSE value with ground truth FVC is 0.17. Results show that developed model can be used for efficient measurement of fractional vegetation cover.
关键词： vegetation index,drone,NDVI,Dimidiate pixel model,FVC,Sentinel-2
更新于2025-09-10 09:29:36
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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 - Consistent Regression of Biophysical Parameters with Kernel Methods
摘要： This paper introduces a novel statistical regression framework that allows the incorporation of consistency constraints. A linear and nonlinear (kernel-based) formulation are introduced, and both imply closed-form analytical solutions. The models exploit all the information from a set of drivers while being maximally independent of a set of auxiliary, protected variables. We successfully illustrate the performance in the estimation of chlorophyll content.
关键词： consistency,regression,model inversion,vegetation monitoring,kernel methods
更新于2025-09-10 09:29:36
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Linking Phenological Indices from Digital Cameras in Idaho and Montana to MODIS NDVI
摘要： Digital cameras can provide a consistent view of vegetation phenology at fine spatial and temporal scales that are impractical to collect manually and are currently unobtainable by satellite and most aerial based sensors. This study links greenness indices derived from digital images in a network of rangeland and forested sites in Montana and Idaho to 16-day normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). Multiple digital cameras were placed along a transect at each site to increase the observational footprint and correlation with the coarser MODIS NDVI. Digital camera phenology indices were averaged across cameras on a site to derive phenological curves. The phenology curves, as well as green-up dates, and maximum growth dates, were highly correlated to the satellite derived MODIS composite NDVI 16-day data at homogeneous rangeland vegetation sites. Forested and mixed canopy sites had lower correlation and variable significance. This result suggests the use of MODIS NDVI in forested sites to evaluate understory phenology may not be suitable. This study demonstrates that data from digital camera networks with multiple cameras per site can be used to reliably estimate measures of vegetation phenology in rangelands and that those data are highly correlated to MODIS 16-day NDVI.
关键词： phenology,growing season,NDVI,digital photography,RGB camera,rangeland,understory productivity,phenocam,vegetation
更新于2025-09-10 09:29:36
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Vegetation Optical Depth and Soil Moisture Retrieved from L-Band Radiometry over the Growth Cycle of a Winter Wheat
摘要： L-band radiometer measurements were performed at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat stand. L-band microwave observations were collected over two different footprints within a homogenous winter wheat stand in order to disentangle the emissions originating from the soil and from the vegetation. Based on brightness temperature (TB) measurements performed over an area consisting of a soil surface covered by a reflector (i.e., to block the radiation from the soil surface), vegetation optical depth (τ) information was retrieved using the tau-omega (τ-ω) radiative transfer model. The retrieved τ appeared to be clearly polarization dependent, with lower values for horizontal (H) and higher values for vertical (V) polarization. Additionally, a strong dependency of τ on incidence angle for the V polarization was observed. Furthermore, τ indicated a bell-shaped temporal evolution, with lowest values during the tillering and senescence stages, and highest values during flowering of the wheat plants. The latter corresponded to the highest amounts of vegetation water content (VWC) and largest leaf area index (LAI). To show that the time, polarization, and angle dependence is also highly dependent on the observed vegetation species, white mustard was grown during a short experiment, and radiometer measurements were performed using the same experimental setup. These results showed that the mustard canopy is more isotropic compared to the wheat vegetation (i.e., the τ parameter is less dependent on incidence angle and polarization). In a next step, the relationship between τ and in situ measured vegetation properties (VWC, LAI, total of aboveground vegetation biomass, and vegetation height) was investigated, showing a strong correlation between τ over the entire growing season and the VWC as well as between τ and LAI. Finally, the soil moisture was retrieved from TB observations over a second plot without a reflector on the ground. The retrievals were significantly improved compared to in situ measurements by using the time, polarization, and angle dependent τ as a priori information. This improvement can be explained by the better representation of the vegetation layer effect on the measured TB.
关键词： inverse modeling,SMOS,vegetation optical depth,microwave remote sensing,SMAP,soil moisture,winter wheat,tower-based experiment
更新于2025-09-10 09:29:36
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Underlying Topography Estimation Over Forest Areas Using Single-Baseline InSAR Data
摘要： In this paper, a method for digital elevation model (DEM) extraction over forest areas from single-baseline interferometric synthetic aperture radar (InSAR) data is proposed. The main idea of this method is that some backscattering variations which are linked to the geometrical structures of forest occur during the radar acquisition. The time–frequency analysis is used to retrieve these variations by dividing the synthesized SAR image into multiple SAR images in the Fourier domain called sublook images. Then, by interferometry, the sublook images characterized by the same Doppler bandwidth and acquired from spatially separated locations at either end of a baseline are used to estimate the sublook coherences and the above backscattering variations are converted into the variations the number of InSAR of sublook coherences. As a result, observations can be increased. The sublook coherences are then interpreted by the two-layer vegetation scattering model and are assumed to follow a near-linear relationship in the complex plane. The ground phase can then be estimated by linear regression of the sublook coherences. The performance of the proposed method was validated by E-SAR L- and P-band SAR data acquired over coniferous and tropical forests. For the coniferous scenario, the underlying DEM estimated by the proposed method has a root-mean-square error (RMSE) of 4.39 m, which is slightly less accurate than the DEM (with an RMSE of 4.07 m) derived by the polarimetric line-?t (LF) method, but represents a signi?cant improvement in DEM accuracy over the HH InSAR method. For the tropical scenario, the DEMs derived by the proposed method and the polarimetric LF method are closer to the ground surface than those derived by the HH InSAR method, and their mean ground height difference is 0.62 m. The two experiments con?rm that it is feasible to extract a DEM by the proposed method, which has a comparable performance in DEM inversion to the polarimetric LF method and only requires single-polarization InSAR data.
关键词： time–frequency (TF) analysis,underlying topography,two-layer vegetation scattering model,Interferometric synthetic aperture radar (InSAR)
更新于2025-09-10 09:29:36
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Detection of <i>Firmiana danxiaensis</i> Canopies by a Customized Imaging System Mounted on an UAV Platform
摘要： The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.
关键词： UAV,SVM,hierarchical classification,Firmiana danxiaensis,spectral analysis,remote sensing,image segmentation,vegetation indices
更新于2025-09-10 09:29:36
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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 - Semi-Physical Integration of Scattering Models for Microwaves and Optical Wavelengths
摘要： Various approaches exist to model scattering of a vegetation canopy above ground in terms of optical and radar wavelengths. Due to the different scattering properties these two spectral regions are modelled separately for visible/ infrared bands and for microwave regions. The newly developed RadOptics model (RO-M) integrates these two spectral regions semi-physically into one radiative transfer (RT)-based model framework, resting on the law of Beer-Bougert-Lambert. Due to the integrative nature of RO-M, it can calculate/simulate the canopy and soil reflectances for the optical and radar spectrum using a single unified model architecture. By Applying RO-M in radar domain (ROR-M) it is shown that the observed dependence of Backscattering coefficient on Leaf Area Index (LAI), soil moisture content and frequency can be simulated consistently with results in literature. The results of the RO-M within the optical domain (ROO-M) present an equivalent trend of reflectance and band ratio values with LAI compared to studies in literature.
关键词： physics,optics,vegetation,modeling,microwaves,soil
更新于2025-09-10 09:29:36
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[Advances in Intelligent Systems and Computing] Intelligent Systems and Applications Volume 868 (Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1) || Wheat Plots Segmentation for Experimental Agricultural Field from Visible and Multispectral UAV Imaging
摘要： The use of Unmanned Aerial Vehicles (UAV) in precision agriculture (PA) has increased recently. Most applications capture images from cameras installed in the UAVs and later create mosaics for human inspection. In order to further improve the quality of data o?ered by this technology, application speci?c image processing algorithms that enhance, segment and extract information from the raw images delivering information equivalent to in-situ measurements are still missing. The present study describes a method for image segmentation to assist the characterization of nitrogen content in wheat ?elds. The proposed methodology uses the UAV and Computer Vision algorithms that process visual (RGB) and multispectral agricultural images. Data is ?rst collected by the UAV that ?ies over an area of interest and collects high resolution RGB and multispectral images at a low altitude. Subsequently, a mosaic is created for each crop stage and the proposed algorithm segments the ROIs (regions where wheat crop is present) based on vegetation index. Using the proposed algorithm, the wheat plots are correctly segmented for two kinds of Brazilians wheat cultivates. The segmentation was validated by experts indicating that the proposed algorithm is suitable to be used as a ?rst step of a method that assist the analyses of nitrogen content speci?c to wheat crops.
关键词： Unmanned Aerial Vehicles (UAV),Segmentation,Vegetation index
更新于2025-09-10 09:29:36