A Comparative Study of Drone and High Resolution Satellite Data for Detailed Land Use/Land Cover Mapping

2020 ◽  
pp. 1-10
Author(s):  
Ajay Mathur ◽  
P. K. Litoria ◽  
B. Pateriya
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Comparative Study ◽  
Satellite Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover

Land use — land cover mapping using satellite data for a forested watershed, Udupi district, Karnataka state, India

2005 ◽  
Vol 33 (2) ◽  
pp. 233-238 ◽  
Author(s):  
Amba Shetty ◽  
Lakshman Nandagiri ◽  
Sangeeta Thokchom ◽  
M. V. S. Rajesh
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Forested Watershed ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Karnataka State

scholarly journals Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

2017 ◽  
Vol 50 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Mirco Sturari ◽  
Emanuele Frontoni ◽  
Roberto Pierdicca ◽  
Adriano Mancini ◽  
Eva Savina Malinverni ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Elevation Data ◽  
High Resolution Images

scholarly journals OBJECT ORIENTED CLASSIFICATION AND FEATURE EXTRACTION FOR PARTS OF EAST DELHI USING HYBRID APPROACH

2018 ◽  
Vol XLII-5 ◽  
pp. 749-754
Author(s):  
P. Kumar ◽  
S. Ravindranath ◽  
K. G. Raj
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Data ◽  
Urban Land ◽  
Urban Land Use ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Master Plans ◽  
Very High

<p><strong>Abstract.</strong> Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp;amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.</p>

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

scholarly journals Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning

2018 ◽  
Vol 10 (12) ◽  
pp. 1910 ◽  
Author(s):  
Joseph Spruce ◽  
John Bolten ◽  
Raghavan Srinivasan ◽  
Venkat Lakshmi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Data ◽  
Reference Data ◽  
Assessment Tool ◽  
Deciduous Forest ◽  
Land Use Land Cover ◽  
Modis Ndvi ◽  
Mekong Basin ◽  
Lower Mekong Basin

This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment.

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