- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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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Spatial modeling for the optimum site selection of solar photovoltaics power plant in the northwest coast of Egypt
摘要： The unbalanced distribution of the Egyptian population causes serious social and economic problems. Redistributing population density to fully utilize the uninhabited areas like desert regions is very critical. This requires discovering renewable energy and water resources, to achieve an optimal goal of the sustainable national strategy. Therefore, this paper aims to define the most suitable locations for establishing the photovoltaic (PV) power plants considering the techno-economic and environmental conditions, assuring the maximum power achievement with minimizing project cost. To achieve this, the integration of Geographic Information System (GIS) tools, Remote Sensing technology (RS) with the Multi-Criteria Decision Making (MCDM) technique was performed. Among MCDM techniques, the Analytic Hierarchy Process (AHP) method has been used to determine the weights of the multi-criteria (techno-economic and environmental) as a more suitable tool to solve site selection problems. The obtained results showed that the entire region's surface receives a large amount of radiation, as the maximum and the minimum values of solar radiation for 2018 were 5.9 - 4.7 kWh/m2/day, respectively. The Land Suitability Index (LSI) map was created to evaluate the potentiality of the sites. LSI was classified into five categories: “most suitable,” “highly suitable,” “moderately suitable,” “marginally suitable,” and “least suitable”. As a result, 24.9 % (261.1747 km2) of the investigation area is more suitable and promising for deploying photovoltaic (PV) power plants.
关键词： Multi-Criteria Decision Making,Egypt,Photovoltaics power plants,GIS,Remote Sensing
更新于2025-09-23 15:21:01
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Case library construction method using high-resolution remote sensing land cover classification information
摘要： Using high-resolution remote sensing images, we propose a case classification method using a land cover classification case library. Case-based reasoning is applied to several aspects of this approach: case representation, case feature selection, case weight, case structure and a similarity measurement model. A detailed study and discussion of the construction of a land cover classification case are presented. Worldview-2 satellite images with 0.5-m spatial resolution are used and the case library construction process for land cover classification information is applied; the effectiveness of the proposed method is then verified using the library. Land cover classification information is thus successfully extracted.
关键词： remote sensing,land cover classification,High-resolution image,case-based reasoning,case library
更新于2025-09-23 15:21:01
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - An Elegant End-to-End Fully Convolutional Network (E3FCN) for Green Tide Detection Using MODIS Data
摘要： Using remote sensing (RS) data to monitor the onset, proliferation and decline of green tide (GT) has great significance for disaster warning, trend prediction and decision-making support. However, remote sensing images vary under different observing conditions, which bring big challenges to detection missions. This paper proposes an accurate green tide detection method based on an Elegant End-to-End Fully Convolutional Network (E3FCN) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In preprocessing, RS images are firstly separated into subimages by a sliding window. To detect GT pixels more efficiently, the original Fully Convolutional Neural Network (FCN) architecture is modified into E3FCN, which can be trained end-to-end. The E3FCN model can be divided into two parts, contracting path and expanding path. The contracting path aims to extract high-level features and the expanding path aims to provide a pixel-level prediction by using a skip technique. The prediction result of whole image is generated by merging the prediction results of subimages, which can also improve the final performance. Experiment results show that the average precision of E3FCN on the whole data sets is 98.06%, compared to 73.27% of Support Vector Regression (SVR), 71.75% of Normalized Difference Vegetation Index (NDVI), and 64.41% of Enhanced Vegetation Index (EVI).
关键词： green tide,Elegant End-to-End Fully Convolutional Network (E3FCN),deep learning,remote sensing,Moderate Resolution Imaging Spectroradiometer (MODIS)
更新于2025-09-23 15:21:01
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Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing
摘要： In order to promote the application of hyperspectral remote sensing in the quanti?cation of grassland areas’ physiological and biochemical parameters, based on the spectral characteristics of ground measurements, the dry AGB and multisensor satellite remote sensing data, including such methods as correlation analysis, scaling up, and regression analysis, were used to establish a multiscale remote sensing inversion model for the alpine grassland biomass. The feasibility and effectiveness of the model were veri?ed by the remote sensing estimation of a time-space sequence biomass of a plateau grassland in northern Tibet. The results showed that, in the ground spectral characteristic parameters of the grassland’s biomass, the original wave bands of 550, 680, 860, and 900 nm, as well as their combination form, had a good correlation with biomass. Also, the remote sensing biomass estimation model established on the basis of the two spectral characteristics (VI2 and Normalized Difference Vegetation Index [NDVI]) had a high inversion accuracy and was easy to realize, with a ?tting R2 of 0.869 and an F test value of 92.6. The biomass remote sensing estimate after scale transformation had a standard deviation of 53.9 kg/ha from the ?tting model established by MODIS NDVI, and the estimation accuracy was 89%. Therefore, it displayed the ability to realize the estimation of large-scale and long-time sequence remote sensing biomass. The veri?cation of the model’s accuracy, comparison of the existing research results of predecessors, and analysis of the regional development background demonstrated the effectiveness and feasibility of this method.
关键词： biomass,spectral characteristic parameters,alpine grassland,multiscale,hyperspectral remote sensing
更新于2025-09-23 15:21:01
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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 - Classification of Remote Sensing Images Using Attribute Profiles and Feature Profiles from Different Trees: A Comparative Study
摘要： The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image’s spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from component trees (max-tree and min-tree). In this work, we investigate their performance on the inclusion tree (tree of shapes) and partition trees (alpha tree and omega tree). Experimental results from both panchromatic and hyperspectral images again confirm the efficiency of FPs compared to APs.
关键词： tree representation,classification,Remote sensing images,attribute profiles,feature profiles,attribute filters
更新于2025-09-23 15:21:01
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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 - Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds
摘要： The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal LiDAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m2) to improve the single tree parameter estimation of low density data (up to 5 pts/m2) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.
关键词： Point Cloud,Tree Crown Parameters,Remote Sensing,Multitemporal LiDAR Data,Data Fusion
更新于2025-09-23 15:21:01
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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 - Soil Moisture Retrieval by Combining Using Active and Passive Microwave Data
摘要： Active and passive microwave remote sensing have their particular characteristics. Active microwave is more sensitive to vegetation cover and surface soil roughness, while passive microwave is more sensitive to the surface soil moisture. A new retrieval algorithm has been proposed by using Aquarius and SMAP satellites’ active and passive microwave observations to retrieve soil moisture products in different spatial scales. The retrieval results of soil moisture have been verified with the ground observations of soil moisture and temperature measurement (SMTM) stations in Naqu, China. The advantages and disadvantages of the algorithm have also been evaluated to analyze the practical value of the new soil moisture retrieval algorithm.
关键词： soil moisture retrieval algorithm,passive microwave,soil moisture,active microwave,active and passive microwave remote sensing,Aquarius satellite
更新于2025-09-23 15:21:01
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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 - High Resolution Soil Moisture Product Based on Smap Active-Passive Approach Using Copernicus Sentinel 1 Data
摘要： SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-1A and -1B Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program’s Sentinel-1A and -1B multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density (~< 3 kg m-2). The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.
关键词： active-passive algorithm,SMAP,Sentinel-1A and -1B,soil moisture,microwave remote sensing
更新于2025-09-23 15:21:01
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Ocean Dynamics Observed by VIIRS Day/Night Band Satellite Observations
摘要： Three cases of Day/Night Band (DNB) observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) are explored for applications to assess the ocean environment and monitor ocean dynamics. An approach to use the ratio between the target radiance and the reference radiance was developed in order to better assess the ocean diurnal and short-term environmental changes with VIIRS DNB observations. In the La Plata River Estuary, the sediment fronts showed 20–25 km diurnal inshore-offshore movements on 13 March 2017. In the waters off the coast of Argentina in the South Atlantic, VIIRS DNB measurements provided both daytime and nighttime observations and monitoring of the algal bloom development and migration between 24 and 26 March 2016. This algal bloom generally kept the same spatial patterns, but moved nearly 20 km eastward in the three-day period. In the Yangtze River Estuary and Hangzhou Bay region along China’s east coast, VIIRS DNB observations also revealed complicated coastal dynamic changes between 12 and 14 April 2017. Even though there are still some challenges and limitations for monitoring the ocean environment with VIIRS DNB observations, this study shows that satellite DNB observations can provide additional data sources for ocean observations, especially observations during the nighttime.
关键词： satellite remote sensing,VIIRS,DNB observation,ocean color,nocturnal study,ocean dynamics
更新于2025-09-23 15:21:01