Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data

Punia, Milap; Joshi, P. K.; Porwal, M. C.

doi:10.1016/j.eswa.2010.10.078

Cited by 68 publications

(28 citation statements)

References 19 publications

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“…Kandrika and Roy [25] achieved an OA of 87.50% and KC of 0.87 with See-5 DTC and IRS-P6 AWiFS data. Punia et al [20] derived an OA of 91.81% using the same method as Kandrika and Roy [25]. Thus, the accuracy results for this study are comparable with those of other studies.…”

Section: Analysis Of Classification Accuracy and Other Similar Studiessupporting

confidence: 81%

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Hua

Zhang

Chen

et al. 2017

IJGI

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Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user's accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.

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Section: Analysis Of Classification Accuracy and Other Similar Studiessupporting

confidence: 81%

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Hua

Zhang

Chen

et al. 2017

IJGI

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“…Overall classification accuracy of LULC map for 1990 was 90.4 % and for 2009 was 84.4 % ( Table 4). The expert classification accuracy is remarkably good as compared to hard classification methods (Punia et al 2011;Wentz et al 2008). Due to Fig.…”

Section: Resultsmentioning

confidence: 94%

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009)

Sharma

Ghosh

Joshi

2012

Environ Monit Assess

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Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa = 0.88) for 1990 and 85 % (kappa = 0.81) for 2009. It was found that the city has expanded over 42.75 km(2) within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5 ± 2.6 °C increase in land surface temperature, vegetation to fallow 6.7 ± 3 °C, fallow to built-up is 3.5 ± 2.9 °C and built-up to dense built-up is 5.3 ± 2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N-S and NE-SW profiles.

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“…Due to the lack of availability of sensors providing both high spatial and high temporal resolution imagery, most crop mapping studies using multi-temporal optical data had to be based on low to medium spatial resolution images with pixel sizes > 30 m [3], [6], [7], [8], [9], [10]. However, in highly fractured landscapes with heterogeneous cropping patterns of smaller fields, images with higher spatial resolution are needed [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Crop identification by means of seasonal statistics of RapidEye time series

Zillmann¹,

Weichelt²

2014

2014 the Third International Conference on Agro-Geoinformatics

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Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye's high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km² in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.

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Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data

Cited by 68 publications

References 19 publications

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009)

Crop identification by means of seasonal statistics of RapidEye time series

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