- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Detection of land use/cover change in Egyptian Nile Delta using remote sensing
摘要： The present study aims to assess the changes of different land use/land cover classes for Nile Delta of Egypt during the period from 1987 to 2015, to evaluate the impact of land cover change and urban sprawl, before, during and after the 25th of January 2011 using remote sensing and GIS techniques, as a result to unplanned urban sprawl which was done by people during the lack of general security of Egyptian revolution. The results indicated that there was a regular trend characterized in most classes and that the change in different land use/land cover classes ranged between increase and decrease areas. A continuous increase in agricultural, urban, ?sh farms and natural vegetation areas and a continuous decrease in water bodies and sand areas were detected in the studied area. The agricultural area recorded the highest increase during the period from 1987 to 2000 (305296.1 ha.) while it increased by 170578.1 ha., during the period from 2000 to 2015. However, in urban area, the highest increase was recorded during the period from 2000 to 2015 followed by the period 1987–2000 with mean values of 97940.8 and 53112.6 ha., respectively. The analysis of the results showed that most of Egyptian Delta governorates have been signi?cantly affected by the different classes of land use/land cover change due to agriculture activities, urban growth as a result of human activities dynamic impact.
关键词： Change detection,Remote sensing,Nile Delta governorates,Land use/land cover
更新于2025-09-23 15:23:52
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Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images
摘要： To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
关键词： remote sensing application,detection algorithm,land use and land cover,histogram distance
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - The Earth Obsevation Data Ecosystem Monitoring (Eodesm) System
摘要： Through the EU FP7 Horizon 2020 ECOPOTENTIAL project, a novel and innovative approach to classification has been developed, which is termed the Earth Observation Data for Ecosystem Monitoring (EODESM), and has been built on concepts behind an implementation of the Earth Observation Data for Habitat Monitoring (EODHaM) system generated as part of the EU FP7 BIOSOS project, applied to Very High Resolution (VHR) Worldview data. The EODESM system facilitates routine classification of land covers according to the Food and Agricultural Organisations Land Cover Classification System (FAO-LCCS), translates these to other taxonomies (including General Habitat Classifications; GHCs) and facilitates routine detection of change and the generation of maps indicating the causes and consequences of such change.
关键词： change detection,earth observation,environmental variables,Land cover classification
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Towards Joint Land Cover and Crop Type Mapping with Numerous Classes
摘要： The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type maps using a highly analytic nomenclature of more than 40 classes. An intensive manual annotation procedure was carried out for the production of reference data. A class nomenclature based on CORINE land cover Level-3 was employed along with several additional crop-type classes. Multitemporal surface reflectance Landsat-8 data for the year of 2016 were used for all classification experiments with a linear SVM classifier. Quantitative and qualitative evaluation highlighted the efficiency of the proposed approach achieving high accuracy rates. Further analysis on individual classes’ performance highlighted the challenges in the proposed classification scheme as well as important outcomes regarding the spectral behavior of the considered categories.
关键词： support vector machines,CORINE Land Cover,Landsat-8,classification
更新于2025-09-23 15:23:52
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On the Synergistic Use of Optical and SAR Time-Series Satellite Data for Small Mammal Disease Host Mapping
摘要： (1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas. A comparison of models derived from the three land cover maps are made, assessing their potential for use in cloud-prone areas. (3) Results: Classification accuracies demonstrated the combined OLI-SAR classification to be of highest accuracy, with the single-date OLI and time-series SAR derived classifications of equivalent quality. Random forest analysis identified statistically significant positive relationships between E. tancrei density and agricultural land, and between M. gregalis density and water and bushes. Predictive application of random forest models identified hotspots of high relative density of E. tancrei and M. gregalis across the broader study area. (4) Conclusions: This offers valuable information to improve the targeting of limited-resource disease control activities to disrupt disease transmission in this area. Time-series SAR derived land cover maps are shown to be of equivalent quality to those generated from single-date optical imagery, which enables application of these methods in cloud-affected areas where, previously, this was not possible due to the sparsity of cloud-free optical imagery.
关键词： Echinococcus multilocularis,random forests,spatial epidemiology,SAR,land cover,Ellobius tancrei,Microtus gregalis,time-series,Sentinel
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Living WALES — National Level Mapping and Monitoring Though Earth Observations, Ground Data and Models
摘要： Living Wales is new initiative that aims to capture the state and dynamics of Wales’s landscape, in near real time, historically and into the future (over the long term) through integration of earth observation (EO) data from multiple sensors, supportive ground measurements and process models. Living Wales is building on existing capability, working with relevant national and international organisations in the field, and strengthening research capacity in Wales as well as internationally. Data layers representing land cover, land cover change and a diverse range of environmental variables are being generated and made available to provide better support for government policies and initiatives but also for societal, economic and environmental benefit. Ultimately, Living Wales is anticipated to contribute to more sustainable use and planning of the Welsh landscape and its resources over short to long time (50 year +) scales. The concepts and approach will also be transferable to other countries and regions.
关键词： ground data,environmental variables,change detection,Land cover classification,earth observation,process models
更新于2025-09-23 15:22:29
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PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme
摘要： In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant co-polar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.
关键词： Coherency matrix,polarimetric synthetic aperture radar (PolSAR),cross-polarization,unitary matrix rotation,land-cover classification
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 - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.
关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification
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 - Texture and Intensity Based Land Cover Classification in Germany from Multi-Orbit & Multi-Temporal Sentinel-L Images
摘要： Land cover information is vital for ecosystem management, to find biodiversity indicators and for sustainable development. The launch of the Sentinel-1 satellites provide large amounts of Synthetic Aperture Radar (SAR) data that can be used for the extraction and classification of land cover. This study presents a preliminary method for land cover classification using SAR amplitude and textural features and by combining multi-temporal images from ascending and descending orbits. The texture parameters contrast, entropy, homogeneity and variance were investigated. The rules of the SAR-LC classifier, designed and implemented at the Federal Agency for Cartography and Geodesy in Frankfurt, were optimised to include textual information for processing the multi-temporal SAR images. The results for land cover classification of images from 2017 for the area around Berlin in Germany are reported along with their classification efficiencies.
关键词： Land cover classification,Germany,Copernicus,Sentinel-1,object based classification,texture
更新于2025-09-23 15:21:21
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[IEEE 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Lecco, Italy (2019.9.25-2019.9.27)] 2019 25th International Workshop on Thermal Investigations of ICs and Systems (THERMINIC) - Luminaire Digital Design Flow with Delphi4LED LEDs Multi-Domain Compact Model
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:21:01