Land-use and land-cover mapping and change detection in Tripura using satellite LANDSAT data

Gautam, N.; Chennaiah, G. Ch.

doi:10.1080/01431168508948473

Cited by 34 publications

(21 citation statements)

References 2 publications

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“…Up-to-date datasets on LULC change provide critical inputs to evaluate complex causes and responses in order to project future trends better, ranging from local, regional, to global scales [5,6]. They are also prerequisites for making development plans [7,8]. However, the magnitude of LULC change differs with the time period being examined [9], geographical location [10], slope gradient, and elevation range [11,12].…”

Section: Introductionmentioning

confidence: 99%

Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands

et al. 2013

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Abstract:The objective of this study was to analyze land use/land cover (LULC) changes in the landscape of Munessa-Shashemene area of the Ethiopian highlands over a period of 39 years . Satellite images of Landsat MSS (1973), TM (1986), ETM+ (2000, and RapidEye (2012) were used. All images were classified using object-based image classification technique. Accuracy assessments were conducted for each reference year. Change analysis was carried out using post classification comparison in GIS. Nine LULCs were successfully captured with overall accuracies ranging from 85.7% to 93.2% and Kappa statistic of 0.822 to 0.924. The classification result revealed that grasslands (42.3%), natural forests (21%), and woodlands (11.4%) were dominant LULC types in 1973. In 2012, croplands (48.5%) were the major LULC types followed by others. The change result shows that a rapid reduction in woodland cover of 81.8%, 52.3%, and 36.1% occurred between the first (1973-1986), second (1986-2000), and third (2000-2012) study periods, respectively. Similarly, natural forests cover decreased by 26.1% during the first, 21.1% during the second, and 24.4% during the third periods. Grasslands also declined by 11.9, 17.5, and 21.1% during the three periods, respectively. On the contrary, croplands increased in all three periods by 131, 31.5, and 22.7%, respectively. Analysis of the 39-year change matrix revealed that about 60% of the land showed changes in LULC. Changes were OPEN ACCESS Remote Sens. 2013, 5 2412 also common along the slope gradient and agro-ecological zones with varying proportions. Further study is suggested to investigate detailed drivers and consequences of changes.

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Section: Introductionmentioning

confidence: 99%

Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands

et al. 2013

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“…Research on LULC change detection has attracted much attention since the 1970s [2][3][4][5][6]. Timely and accurate LULC change detection of Earth's surfaces provided the foundation for better understanding human-nature relationships and interactions [7][8][9][10][11]. Since the Landsat satellite was launched in the early 1970s, multitemporal remote sensing data, especially time-series Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Landsat Data Continuity Mission (LDCM) images, have been widely used for examining LULC change [6,12,13].…”

Section: Introductionmentioning

confidence: 99%

Examining Land Use and Land Cover Spatiotemporal Change and Driving Forces in Beijing from 1978 to 2010

Tian

Yin

et al. 2014

Remote Sensing

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Land use and land cover (LULC) datasets for Beijing in 1978Beijing in , 1987Beijing in , 1992Beijing in , 2000 and 2010 were developed from Landsat images using the object-oriented classification approach. The relationships between social-economic, demographic and political factors and time-series LULC data were examined for the periods between 1978 and 2010. The results showed the effectiveness of using the object-oriented decision tree classification method for LULC classification with time series of Landsat images. Combined with anthropogenic driving forces, our research can effectively explain the detailed LULC change trajectories corresponding to different stages and give new insights for Beijing LULC change patterns. The results show a significant increase in forest and built-up areas, but a decrease in arable lands, due to urbanization and reforestation. Large ecological projects result in an increase of forest areas and population, and economic conditions result in urban expansion. The 10594 anthropogenic driving forces analysis results further prove that both population increase and economic development played important roles in the expansion of built-up areas. Both the qualitative and quantitative anthropogenic driving forces analysis methods were helpful for better understanding the mechanisms of LULC change.

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“…The important accuracy assessment terms involve overall accuracy, commission errors and Kappa coefficient (Gautam & Chennaiah 1985;Congalton 1991;Congalton & Green 1998;Congalton & Plourde 2002;Congalton et al 1983). With experimental outcomes, it is observed that CVA technique achieved 0.40 kappa coefficient and 70% accuracy assessment for dataset 1 (table 4), 0.48 kappa coefficient and 74% accuracy assessment for dataset 2 (table 5) and 0.48 kappa coefficient and 74% accuracy assessment for dataset 3 (table 6).…”

Section: Resultsmentioning

confidence: 98%

“…Change detection analysis includes the use of multi-spectral bands of multi-temporal satellite data sets to discriminate the LULC changes (Gautam & Chennaiah 1985). Lu et al (2003) represent different classes of change detection techniques such as algebraic techniques, transformation, classification, progressive techniques, geographical information system (GIS) techniques, visual analysis and other techniques.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on change vector analysis based change detection techniques

Singh

Talwar²

2014

Sadhana

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Detection of Earth surface changes are essential to monitor regional climatic, snow avalanche hazard analysis and energy balance studies that occur due to air temperature irregularities. Geographic Information System (GIS) enables such research activities to be carried out through change detection analysis. From this viewpoint, different change detection algorithms have been developed for land-use land-cover (LULC) region. Among the different change detection algorithms, change vector analysis (CVA) has level headed capability of extracting maximum information in terms of overall magnitude of change and the direction of change between multispectral bands from multi-temporal satellite data sets. Since past two-three decades, many effective CVA based change detection techniques e.g., improved change vector analysis (ICVA), modified change vector analysis (MCVA) and change vector analysis posterior-probability space (CVAPS), have been developed to overcome the difficulty that exists in traditional change vector analysis (CVA). Moreover, many integrated techniques such as cross correlogram spectral matching (CCSM) based CVA. CVA uses enhanced principal component analysis (PCA) and inverse triangular (IT) function, hyper-spherical direction cosine (HSDC), and median CVA (m-CVA), as an effective LULC change detection tools. This paper comprises a comparative analysis on CVA based change detection techniques such as CVA, MCVA, ICVA and CVAPS. This paper also summarizes the necessary integrated CVA techniques along with their characteristics, features and shortcomings. Based on experiment outcomes, it has been evaluated that CVAPS technique has greater potential than other CVA techniques to evaluate the overall transformed information over three different MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets of different regions. Results of this study are expected to be potentially useful for more accurate analysis of LULC changes which will, in turn, improve the utilization of CVA based change detection techniques for such applications. * For correspondence 1311 1312 Sartajvir Singh and Rajneesh TalwarKeywords. Change vector analysis (CVA); improved change vector analysis (ICVA); modified change vector analysis (MCVA); change vector analysis posteriorprobability space (CVAPS).

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Land-use and land-cover mapping and change detection in Tripura using satellite LANDSAT data

Cited by 34 publications

References 2 publications

Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands

Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands

Examining Land Use and Land Cover Spatiotemporal Change and Driving Forces in Beijing from 1978 to 2010

A comparative study on change vector analysis based change detection techniques

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