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Relevant bibliographies by topics / Land use / land cover classification

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Journal articles

Academic literature on the topic 'Land use / land cover classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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

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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2

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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