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Journal articles on the topic 'Land use / land cover classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Nganro, Sudirman, Slamet Trisutomo, Roland Barkey, et al. "Prediction of Future Land Use and Land Cover (LULC) in Makassar City." TATALOKA 23, no. 2 (2021): 183–89. http://dx.doi.org/10.14710/tataloka.23.2.183-189.

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Migration from rural area to urban area increases urban population. It increases and needs for settlements, leading to conversion of agricultural lands into settlement areas. Inconsistent land use compared with spatial planning causes change in land use. Spatial land use expansion can be monitored and predicted by modeling. NetLogo application is a software integrated with Agent-Based Modeling (ABM), which can be used to predict change of land use with various complex parameters. The present study used population growth as a parameter to predict change of land use of Makassar in 2050 based on

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Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.

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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop p

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3

Heikkonen, Jukka, and Aristide Varfis. "Land Cover/Land Use Classification of Urban Areas." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 04 (1998): 475–89. http://dx.doi.org/10.1142/s0218001498000300.

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This paper proposes a method for remote sensing based land cover/land use classification of urban areas. The method consists of the following four main stages: feature extraction, feature coding, feature selection and classification. In the feature extraction stage, statistical, textural and Gabor features are computed within local image windows of different sizes and orientations to provide a wide variety of potential features for the classification. Then the features are encoded and normalized by means of the Self-Organizing Map algorithm. For feature selection a CART (Classification and Reg

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Vijayan, D., G. Ravi Shankar, and T. Ravi Shankar. "Hyperspectral Data for Land use/Land cover classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 991–95. http://dx.doi.org/10.5194/isprsarchives-xl-8-991-2014.

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An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic ve

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Bektas Balcik, F., and A. Karakacan Kuzucu. "DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 143–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-143-2016.

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Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul

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Tang, Yanbing, and Clifton W. Pannell. "A Hybrid Approach for Land Use/Land Cover Classification." GIScience & Remote Sensing 46, no. 4 (2009): 365–87. http://dx.doi.org/10.2747/1548-1603.46.4.365.

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Jayanth, J., V. S. Shalini, T. Ashok Kumar, and Shivaprakash Koliwad. "Land-Use/Land-Cover Classification Using Elephant Herding Algorithm." Journal of the Indian Society of Remote Sensing 47, no. 2 (2019): 223–32. http://dx.doi.org/10.1007/s12524-018-00935-x.

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Hashim, Haslina, Zulkiflee Abd Latif, and Nor Aizam Adnan. "Land use land cover analysis with pixel-based classification approach." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (2019): 1327. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1327-1333.

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<p>Rapid development in certain urban area will affect its natural features. Therefore, it is important to identify and determine the changes occur for further analysis and future development planning. This process will influence several factors such as area development, environmental issues and human social activities. The selection of remote sensing data and method will derive the accurate land use land cover maps. This research study accessed the classification accuracy of different classifier approach for land use land cover classification in urban area. The objective of this paper i

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Nguyen, Lan H., and Geoffrey M. Henebry. "Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology." Remote Sensing 11, no. 14 (2019): 1677. http://dx.doi.org/10.3390/rs11141677.

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Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First

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Zhang, Ce, Isabel Sargent, Xin Pan, et al. "Joint Deep Learning for land cover and land use classification." Remote Sensing of Environment 221 (February 2019): 173–87. http://dx.doi.org/10.1016/j.rse.2018.11.014.

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Rawal, D., A. Chhabra, M. Pandya, and A. Vyas. "LAND USE AND LAND COVER MAPPING – A CASE STUDY OF AHMEDABAD DISTRICT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 189–93. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-189-2020.

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Abstract. Land cover mapping using remote-sensing imagery has attracted significant attention in recent years. Classification of land use and land cover is an advantage of remote sensing technology which provides all information about land surface. Numerous studies have investigated land cover classification using different broad array of sensors, resolution, feature selection, classifiers, Classification Techniques and other features of interest from over the past decade. One, Pixel based image classification technique is widely used in the world which works on their per pixel spectral reflec

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Dinka, Megersa Olumana, and Degefa Dhuga Chaka. "Analysis of land use/land cover change in Adei watershed, Central Highlands of Ethiopia." Journal of Water and Land Development 41, no. 1 (2019): 146–53. http://dx.doi.org/10.2478/jwld-2019-0038.

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Abstract Land use/land cover changes (LULCC) at Adei watershed (Ethiopia) over a period of 23 years (1986–2009) has been analysed from LANDSAT imagery and ancillary data. The patterns (magnitude and direction) of LULCC were quantified and the final land use/land cover maps were produced after a supervised classification with appropriate post-processing. Image analysis results revealed that the study area has undergone substantial LULCC, primarily a shift from natural cover into managed agro-systems, which is apparently attributed to the increasing both human and livestock pressure. Over the 23

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Bergado, J. R., C. Persello, and A. Stein. "LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 17–21. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-17-2020.

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Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this stud

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Yang, Chun, Franz Rottensteiner, and Christian Heipke. "CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 251–58. http://dx.doi.org/10.5194/isprs-annals-iv-3-251-2018.

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Land cover describes the physical material of the earth’s surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image

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Y. Jamal, Assist Prof Dr Saleem. "Use of Remote Sensing and Geographic Information System for the Classification of Agricultural Land Uses and Land Cover in the Al-Sad Al-Adhim sub District – Iraq." ALUSTATH JOURNAL FOR HUMAN AND SOCIAL SCIENCES 225, no. 2 (2018): 245–73. http://dx.doi.org/10.36473/ujhss.v225i2.151.

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Land use refers to the human activity associated with a particular area of land. The land cover refers to the pattern of appearances located on the surface of the earth. Survey, inventory, monitoring and classification of land use and land cover are a fundamental step in the land use planning process, in evaluating and comparing alternatives and in choosing the best and sustainable use of land for development, accomplishment economic and social well-being. Remote sensing and Geographic Information System provided advantages that conventional methods could not provide for surveys and monitoring

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D. Payal Sandip, B. Varpe Shriniwas. "Land Use/Land Cover Mapping of Sambar Watershed by Using Remote Sensing and GIS." International Journal of Current Microbiology and Applied Sciences 10, no. 9 (2021): 578–85. http://dx.doi.org/10.20546/ijcmas.2021.1009.066.

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In the present study, an effort has been made to study in detail of Land Use/Land Cover Mapping for Sambar watershed by using Remote Sensing and GIS technique was carried out during the year of 2020-2021 in Parbhani district. In this research the Remote Sensing and Geographical Information system technique was used for identifying the land use/land cover classes with the help of ArcGIS 10.8 software. The Sambar watershed is located in 19º35ʹ78.78˝ N and 76º87ʹ88.44˝ E in the Parbhani district of Marathwada region in Maharashtra. It is covered a total area 97.01 km2. The land use/land cover map

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Feng, C. C., and D. M. Flewelling. "Assessment of semantic similarity between land use/land cover classification systems." Computers, Environment and Urban Systems 28, no. 3 (2004): 229–46. http://dx.doi.org/10.1016/s0198-9715(03)00020-6.

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Kitada, Keigo, and Kaoru Fukuyama. "Land-Use and Land-Cover Mapping Using a Gradable Classification Method." Remote Sensing 4, no. 6 (2012): 1544–58. http://dx.doi.org/10.3390/rs4061544.

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Tilahun, Abineh. "Accuracy Assessment of Land Use Land Cover Classification using Google Earth." American Journal of Environmental Protection 4, no. 4 (2015): 193. http://dx.doi.org/10.11648/j.ajep.20150404.14.

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Srivastava, Prashant K., Dawei Han, Miguel A. Rico-Ramirez, Michaela Bray, and Tanvir Islam. "Selection of classification techniques for land use/land cover change investigation." Advances in Space Research 50, no. 9 (2012): 1250–65. http://dx.doi.org/10.1016/j.asr.2012.06.032.

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John Hiew, Jacqueline, Amal Najihah M. Nor, Nur Hairunnisa Rafaai, et al. "Land use classification and land use change analysis using satellite images in Lojing, Kelantan." Journal of Tropical Resources and Sustainable Science (JTRSS) 7, no. 2 (2019): 53–60. http://dx.doi.org/10.47253/jtrss.v7i2.510.

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Remote sensing is widely used to capture the images of land use/land cover on earth. This paper studies on the land use changes in Lojing, Kelantan in 1989 dan 2006. The land use is then classified, and the classification scheme was adopted from United States Geological Survey (USGS) Land Use/ Land Cover Classification System. Supervised classification method has been used since it was proved by other research to be more accurate compared to unsupervised classification. Accuracy assessment was conducted to calculate the accuracy of the land use map produced so that at the end, a good quality o

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Abudu, Dan, Nigar Sultana Parvin, and Geoffrey Andogah. "Reviewing the Pertinence of Sentinel-1 SAR for Urban Land Use Land Cover Classification." International Journal of Scientific & Engineering Research 11, no. 5 (2020): 529–35. http://dx.doi.org/10.14299/ijser.2020.05.07.

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Conventional approaches for urban land use land cover classification and quantification of land use changes have often relied on the ground surveys and urban censuses of urban surface properties. Advent of Remote Sensing technology supporting metric to centimetric spatial resolutions with simultaneous wide coverage, significantly reduced huge operational costs previously encountered using ground surveys. Weather, sensor’s spatial resolution and the complex compositions of urban areas comprising concrete, metallic, water, bare- and vegetation-covers, limits Remote Sensing ability to accurately

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Singh, Babita. "Land Use Land Cover Analysis using Geospatial Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2561–66. http://dx.doi.org/10.22214/ijraset.2021.37339.

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Abstract: Remote sensing and Geographic information system (GIS) techniques can be used for the changing pattern of landscape. The study was conducted in Dehradun, Haridwar and Pauri Garhwal Districts of Uttarakhand State, India. In order to understand dynamics of landscape and to examine changes in the land use/cover due to anthropogenic activities, two satellite images (Landsat 5 and Landsat 8) for 1998 and 2020 were used. Google Earth Engine was used to perform supervised classification. Spectral indices (NDVI, MNDWI, SAVI, NDBI) were calculated in order to identify land cover classes. Both

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Udin, Wani Sofia, and Zuhaira Nadhila Zahuri. "Land Use and Land Cover Detection by Different Classification Systems using Remotely Sensed Data of Kuala Tiga, Tanah Merah Kelantan, Malaysia." Journal of Tropical Resources and Sustainable Science (JTRSS) 5, no. 3 (2017): 145–51. http://dx.doi.org/10.47253/jtrss.v5i3.660.

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Land use and land cover classification system has been used widely in many applications such as for baseline mapping for Geographic Information System (GIS) input and also target identification for identification of roads, clearings and also land and water interface. The research was conducted in Kuala Tiga, Tanah Merah, Kelantan and the study area covering about 25 km2. The main purpose of this research is to access the possibilities of using remote sensing for the detection of regional land-use change by developing a land cover classification system. Another goal is to compare the accuracy o

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Akay, A. E., B. Gencal, and İ. Taş. "SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 31–35. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-31-2017.

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This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant l

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Szarek-Iwaniuk, Patrycja. "A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland." Sustainability 13, no. 6 (2021): 3070. http://dx.doi.org/10.3390/su13063070.

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Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As

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Nguyen, H. T. T., Q. T. N. Chau, A. T. Pham, et al. "LAND USE/LAND COVER CHANGES USING MULTI-TEMPORAL SATELLITE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences VI-3/W1-2020 (November 17, 2020): 83–90. http://dx.doi.org/10.5194/isprs-annals-vi-3-w1-2020-83-2020.

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Abstract. Producing the map of land use land cover change (LULCC) at the local extent is fundamental for a variety of applications such as vegetation, forest covers, soil degradation, and global warming. Understanding the directions and spread trend of LULCC plays significant role in obtaining useful data for the local authorities in making land-use policies under the context of climate change. Dak Nong is located in the Central Highlands of Vietnam having the largest tropical forest. Over the past decades, the natural forest in the region has significantly declined due to the pressure of popu

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Sang, H., L. Zhai, J. Zhang та F. An. "An object-oriented approach for agrivultural land classiﬁcation using rapideye imagery". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (26 червня 2015): 145–48. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-145-2015.

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With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies. RapidEye high resolution imagery acquired on May 29 and August 9th of 2012 was used in this study to evaluate the potential

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López, N., A. Márquez Romance, and E. Guevara Pérez. "Change dynamics of land-use and land-cover for tropical wetland management." Water Practice and Technology 15, no. 3 (2020): 632–44. http://dx.doi.org/10.2166/wpt.2020.049.

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Abstract In hydrographic basins with wetlands, changes in land use (LU) and land cover (LC) impact the conservation of natural resources, leading to dynamics analysis for integral management. A method is proposed offering greater accuracy in determining the LU and LC bi-temporal and spatial change dynamics in tropical wetlands. LU and LC monitoring is based on Landsat images from 1986 to 2017. ‘Pre-classification’ and ‘post-classification’ methods are applied. In the former, reflectance image differencing and principal component N° 1 image differencing are analyzed to estimate the rate of chan

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Yang, C., F. Rottensteiner, and C. Heipke. "TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 139–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-139-2019.

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<p><strong>Abstract.</strong> Land use and land cover are two important variables in remote sensing. Commonly, the information of land use is stored in geospatial databases. In order to update such databases, we present a new approach to determine the land cover and to classify land use objects using convolutional neural networks (CNN). High-resolution aerial images and derived data such as digital surface models serve as input. An encoder-decoder based CNN is used for land cover classification. We found a composite including the infrared band and height data to outperform RG

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Shrestha, U. S. "Land Use and Land Cover Classification from ETM Sensor Data : A Case Study from Tamakoshi River Basin of Nepal." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 943–48. http://dx.doi.org/10.5194/isprsarchives-xl-8-943-2014.

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The mountain watershed of Nepal is highly rugged, inaccessible and difficult for acquiring field data. The application of ETM sensor Data Sat satellite image of 30 meter pixel resolutions has been used for land use and land cover classification of Tamakoshi River Basin (TRB) of Nepal. The paper tries to examine the strength of image classification methods in derivation of land use and land classification. Supervised digital image classification techniques was used for examination the thematic classification. Field verification, Google earth image, aerial photographs, topographical sheet and GP

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N, Sujathaa. "Change Detection Matrix in Land Use and Land Cover Classification using GIS." International Journal for Research in Applied Science and Engineering Technology 8, no. 10 (2020): 577–82. http://dx.doi.org/10.22214/ijraset.2020.31957.

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Xu, Bing, and Peng Gong. "Land-use/Land-cover Classification with Multispectral and Hyperspectral EO-1 Data." Photogrammetric Engineering & Remote Sensing 73, no. 8 (2007): 955–65. http://dx.doi.org/10.14358/pers.73.8.955.

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Pham, Lan Thi, Son Phi Nguyen, Nghia Viet Nguyen, et al. "Establishment of land cover map using object-oriented classification method for VNREDSat-1 data." Journal of Mining and Earth Sciences 61, no. 2 (2020): 134–44. http://dx.doi.org/10.46326/jmes.2020.61(2).15.

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Land cover/land use classification using high resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values, structures, shapes, and spatial elements. This paper reveals the object-oriented classification method to establish the land cover map using VNREDSat-1 data, with a spatial resolution of 10 m. Land cover/land use system is classified according to CORINE with level 3 with 14 types of land cover/land use. Extraction of 14 types of land cover/land use using object-oriented classification method based on reflectance spectral cha

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Nizalapur, V., and A. Vyas. "TEXTURE ANALYSIS FOR LAND USE LAND COVER (LULC) CLASSIFICATION IN PARTS OF AHMEDABAD, GUJARAT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 275–79. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-275-2020.

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Abstract. The present study addresses the potential of RADARSAT-2 data for Land Use Land Cover (LULC) Classification in parts of Ahmedabad, Gujarat, India. Texture measures of the original SAR data were obtained by the Gray Level Co-occurrence Matrix (GLCM). Results suggested False Colour Composite (FCC) of Mean, Homogeneity and Entropy showed a good discrimination of different land cover classes. Further, Principal Component Analysis (PCA) was also applied to the eight texture measures and FCC of Principal components is generated. Unsupervised classification is carried out for the above gener

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Sanou, Charles L., Nouhoun Zampaligré, Daniel N. Tsado, André Kiema, and Yssouf Sieza. "Impacts of Land Use and Cover Changes on Transhumant Pastoralism in Sudanian Zones of Burkina Faso, West Africa." Journal of Agricultural Studies 5, no. 4 (2018): 90. http://dx.doi.org/10.5296/jas.v6i3.13474.

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This research aimed to investigate how the rapid land use and cover changes is affecting pastoral resources and practices within Kompienga province in Sudanian zone of Burkina Faso. To achieve this aim, Landsat images data of years 1989, 2001, 2013 and 2015 were retrieved and analysed. Images were acquired following the path 193 and row 52, from Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI). Images processing were done using 350 training sample for both; the purpose of supervised classification and accuracy assessment

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Mbaya, L. A., G. O. Abu, Yila Caiaphas Makadi, and D. M. Umar. "Effect of Urbanization on Land use Land Cover in Gombe Metropolis." International Journal on Research in STEM Education 1, no. 1 (2019): 22–29. http://dx.doi.org/10.31098/ijrse.v1i1.58.

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This study examined the integration of Remote Sensing and Geographic Information System (RS/GIS) for analyzing land use and land cover dynamics in Gombe Metropolitan, the Gombe State capital for the period 1976 to 2016. Land sat (TM) images of 1976, 1996and 2016 were used. The study employed supervised digital image classification method using Erdas Imagine 9.2 and Arc GIS 10.5 software and classified the land use into undisturbed vegetation, sparse vegetation, Settlements, Farmlands, Rock outcrops, Bare surfaces. The images were analyzed via georeferencing, image enhancement, image resampling

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Maurya, Chnadrakesh, and V. N. Sharma. "Land use/ Land cover Change Detection in Auranga River Basin, Jharkhand." National Geographical Journal of India 66, no. 1 (2020): 51–58. http://dx.doi.org/10.48008/ngji.1729.

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Land use is a man-made dynamic process in which human uses land resource to fulfil their various economic, social and cultural needs and at the same time it also provides a base for development. The proper management needed for sustainable development of land can improve the eco-system and its productivity in a particular geographical region. The present study focuses on spatio-temporal changes in land use and land cover pattern in Auranga river basin of Jharkhand using geospatial approach. Supervised classification technique was applied in this study to detect land use/ land cover changes. Th

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Zhang, Yuwei, Jinting Liu, Luhe Wan, and Shaoqun Qi. "Land Cover/Use Classification Based on Feature Selection." Journal of Coastal Research 73 (March 3, 2015): 380–85. http://dx.doi.org/10.2112/si73-067.1.

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Albert, L., F. Rottensteiner, and C. Heipke. "A two-layer Conditional Random Field model for simultaneous classification of land cover and land use." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 17–24. http://dx.doi.org/10.5194/isprsarchives-xl-3-17-2014.

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This paper proposes a two-layer Conditional Random Field model for simultaneous classification of land cover and land use. Both classification tasks are integrated into a unified graphical model, which is reasonable due to the fact that land cover and land use exhibit strong contextual dependencies. In the CRF, we distinguish a <i>land cover layer</i> and a <i>land use layer</i>. Both layers differ with respect to the entities corresponding to the nodes and the classes to be distinguished. In the land cover layer, the nodes correspond to superpixels extracted from the i

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Rao, K. V. Ramana, and P. Rajesh Kumar. "Backscattering coefficient measurement and land use land cover classification using ENVI SAT ASAR data." International Journal of Engineering & Technology 7, no. 2.14 (2018): 529. http://dx.doi.org/10.14419/ijet.v7i2.9834.

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The polarimetric SAR data of the space borne sensor, ENVISAT-ASAR (Environmental Satellite - Academic & Science Astronomy & Space Science) has been used for the land use land cover classification of the study area. It was an earth observing satellite operated by the European Space Agency (ESA). Its mission was to observe the earth and monitor critical aspects of the environment such as climatic changes on the earth at the local, regional and global levels. The data set of this sensor is a dual co-polarization amplitude data consisting of HH and VV channels. Initially various incidence

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Albert, L., F. Rottensteiner, and C. Heipke. "AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (August 20, 2015): 369–76. http://dx.doi.org/10.5194/isprsannals-ii-3-w5-369-2015.

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Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification of land cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classification form a hierarchy consisting of two layers: a <i>land cover layer</i> and a <i>land use layer</i>. We apply Conditional Random Fields (CRF) at both layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes to be distinguished. In the land cove

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Close, Odile, Beaumont Benjamin, Sophie Petit, Xavier Fripiat, and Eric Hallot. "Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium." Land 7, no. 4 (2018): 154. http://dx.doi.org/10.3390/land7040154.

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Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover A

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Sun, Qiong, Chi Zhang, Min Liu, and Yongjing Zhang. "Land use and land cover change based on historical space–time model." Solid Earth 7, no. 5 (2016): 1395–403. http://dx.doi.org/10.5194/se-7-1395-2016.

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Abstract. Land use and cover change is a leading edge topic in the current research field of global environmental changes and case study of typical areas is an important approach understanding global environmental changes. Taking the Qiantang River (Zhejiang, China) as an example, this study explores automatic classification of land use using remote sensing technology and analyzes historical space–time change by remote sensing monitoring. This study combines spectral angle mapping (SAM) with multi-source information and creates a convenient and efficient high-precision land use computer automa

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Bai, Xiulian, Ram C. Sharma, Ryutaro Tateishi, Akihiko Kondoh, Bayaer Wuliangha, and Gegen Tana. "A Detailed and High-Resolution Land Use and Land Cover Change Analysis over the Past 16 Years in the Horqin Sandy Land, Inner Mongolia." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1316505.

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Land use and land cover (LULC) change plays a key role in the process of land degradation and desertification in the Horqin Sandy Land, Inner Mongolia. This research presents a detailed and high-resolution (30 m) LULC change analysis over the past 16 years in Ongniud Banner, western part of the Horqin Sandy Land. The LULC classification was performed by combining multiple features calculated from the Landsat Archive products using the Support Vector Machine (SVM) based supervised classification approach. LULC maps with 17 secondary classes were produced for the year of 2000, 2009, and 2015 in

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Dou, Yue, Francesca Cosentino, Ziga Malek, Luigi Maiorano, Wilfried Thuiller, and Peter H. Verburg. "A new European land systems representation accounting for landscape characteristics." Landscape Ecology 36, no. 8 (2021): 2215–34. http://dx.doi.org/10.1007/s10980-021-01227-5.

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Abstract Context While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classifica

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Wężyk, Piotr, Paweł Hawryło, Marta Szostak, Marcin Pierzchalski, and Roeland De Kok. "Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping." Quaestiones Geographicae 35, no. 1 (2016): 93–104. http://dx.doi.org/10.1515/quageo-2016-0009.

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Abstract Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. the classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBri

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Soares, Marinalva Dias, Luciano Vieira Dutra, Gilson Alexandre Ostwald Pedro da Costa, Raul Queiroz Feitosa, Rogério Galante Negri, and Pedro M. A. Diaz. "A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery." Remote Sensing 12, no. 6 (2020): 961. http://dx.doi.org/10.3390/rs12060961.

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Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions

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Hughes, Lloyd, Simon Streicher, Ekaterina Chuprikova, and Johan Du Preez. "A Cluster Graph Approach to Land Cover Classification Boosting." Data 4, no. 1 (2019): 10. http://dx.doi.org/10.3390/data4010010.

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When it comes to land cover classification, the process of deriving the land classes is complex due to possible errors in algorithms, spatio-temporal heterogeneity of the Earth observation data, variation in availability and quality of reference data, or a combination of these. This article proposes a probabilistic graphical model approach, in the form of a cluster graph, to boost geospatial classifications and produce a more accurate and robust classification and uncertainty product. Cluster graphs can be characterized as a means of reasoning about geospatial data such as land cover classific

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Zhou, Xiran, Xiao Xie, Yong Xue, and Bing Xue. "Ontology-Based Probabilistic Estimation for Assessing Semantic Similarity of Land Use/Land Cover Classification Systems." Land 10, no. 9 (2021): 920. http://dx.doi.org/10.3390/land10090920.

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To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing th

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