10,398 research outputs found

    Land Cover Change Image Analysis for Assateague Island National Seashore Following Hurricane Sandy

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    The assessment of storm damages is critically important if resource managers are to understand the impacts of weather pattern changes and sea level rise on their lands and develop management strategies to mitigate its effects. This study was performed to detect land cover change on Assateague Island as a result of Hurricane Sandy. Several single-date classifications were performed on the pre and post hurricane imagery utilized using both a pixel-based and object-based approach with the Random Forest classifier. Univariate image differencing and a post classification comparison were used to conduct the change detection. This study found that the addition of the coastal blue band to the Landsat 8 sensor did not improve classification accuracy and there was also no statistically significant improvement in classification accuracy using Landsat 8 compared to Landsat 5. Furthermore, there was no significant difference found between object-based and pixel-based classification. Change totals were estimated on Assateague Island following Hurricane Sandy and were found to be minimal, occurring predominately in the most active sections of the island in terms of land cover change, however, the post classification detected significantly more change, mainly due to classification errors in the single-date maps used

    A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

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    A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region

    Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

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    Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

    Ayer Hitam Forest (AHFR) from space using satellite remote sensing

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    There is a high demand to map and monitor the land use and assess their condition for ecological and economic reasons. Information on existing land and cover and their spatial distribution is a pre-requisite for any planning, development and management programme. In this study, Landsat TM data of 1998 were acquired over the AHFR and it's vicinity which covers an area more than 1, 300 ha. The objective of this paper is to map AHFR and assess the land cover of AHFR in 1998 as well as its surrounding area using remote sensing technology. Digital data processing and analysis were carried out using PCI/EASI PACE software, version 6.2 available in Faculty of Forestry, UPM. A false Colour Composite (FCC) of Landsat TM band 4-5-3 (R-G-B) was used in supervised classification using Maximum Likelihood Classifier (MLC). From a visual interpretation, several features of AHFR could be identified such as federal road, forest road, cleared land, built-up area, oil palm, water bodies and rubber plantation etc. Meanwhile, digital classification showed that seven land use types surrounding AHFR such as forest, secondary forest/shrubs, oil palm, rubber, built-up area, cleared land and water bodies could a easily be mapped out. The mean overall classification accuracy obtained is 86.08 percent with an average accuracy o] 85.64 percent. Satellite map of AHFR is found to be useful for the macro planning and management purposes especially on the Environmental Impact Assessment (EIA) if further development on the area is to be politicized

    Satellite remote sensing for assessment of irrigation system performance: a case study in India

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    Irrigation management / Irrigated farming / Agricultural production / Irrigation systems / Food production / Rice / Cropping systems / Crop yield / Remote sensing / GIS / Models / Policy / Case studies / Satellite surveys / Performance evaluation / India / Bhadra Project

    Target-adaptive CNN-based pansharpening

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    We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

    User requirements and user acceptance of current and next-generation satellite mission and sensor complement, oriented toward the monitoring of water resources

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    Principal water resources users were surveyed to determine the applicability of remotely sensed data to their present and future requirements. Analysis of responses was used to assess the levels of adequacy of LANDSAT 1 and 2 in fulfilling hydrological functions, and to derive systems specifications for future water resources-oriented remote sensing satellite systems. The analysis indicates that water resources applications for all but the very large users require: (1) resolutions on the order of 15 meters, (2) a number of radiometric levels of the same order as currently used in LANDSAT 1 (64), (3) a number of spectral bands not in excess of those used in LANDSAT 1, and (4) a repetition frequency on the order of 2 weeks. The users had little feel for the value of new sensors (thermal IR, passive and active microwaves). What is needed in this area is to achieve specific demonstrations of the utility of these sensors and submit the results to the users to evince their judgement
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