Urbanization and agricultural land loss in India: Comparing satellite estimates with census data

Pandey, Bhartendu; Seto, Karen C.

doi:10.1016/j.jenvman.2014.05.014

Cited by 197 publications

(94 citation statements)

References 31 publications

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“…First, the ISC changes are mainly distributed near existing urban clusters (e.g., New Delhi, Mumbai, and Bengaluru) where there has been strong population and economic growth during the 2000-2010 period. Second, Uttar Pradesh, Maharashtra, Gujarat, and Tamil Nadu are among the states with most ISC, which agrees with the MODIS-based analysis of agricultural land to urban land conversion in India [34]. Finally, as shown by the zoom-in views for New Delhi and Bengaluru, most of the impervious surface changes are in the urban fringe areas, which is an accurate representation of urban development patterns of these two cities.…”

Section: Visual Assessments Of the Is And Isc Productssupporting

confidence: 80%

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

2017

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Understanding and monitoring the environmental impacts of global urbanization requires better urban datasets. Continuous field impervious surface change (ISC) mapping using Landsat data is an effective way to quantify spatiotemporal dynamics of urbanization. It is well acknowledged that Landsat-based estimation of impervious surface is subject to seasonal and phenological variations. The overall goal of this paper is to map 2000-2010 ISC for India using Global Land Survey datasets and training data only available for 2010. To this end, a method was developed that could transfer the regression tree model developed for mapping 2010 impervious surface to 2000 using an iterative training and prediction (ITP) approachAn independent validation dataset was also developed using Google Earth™ imagery. Based on the reference ISC from the validation dataset, the RMSE of predicted ISC was estimated to be 18.4%. At 95% confidence, the total estimated ISC for India between 2000 and 2010 is 2274.62 ± 7.84 km 2 .

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Section: Visual Assessments Of the Is And Isc Productssupporting

confidence: 80%

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

2017

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“…The rate of conversions of agricultural land to other types of land uses remains high throughout the study period (see details in Section 4.3). The loss of agricultural land in the KV resembles the worldwide trend of the urban conversions of agricultural lands reported elsewhere [45][46][47], but much is unknown about how these conversions will affect fragile ecosystems of this mountainous valley, including with the loss of green space, sealing of soil, disturbance to stream corridors, and alteration of agro-ecological services (e.g., water retention, vegetation, air circulation). The forested or tree covered area comprises a small portion of the central KV, which include urban parks (e.g., Bhandarkhal, Boudha), forest resorts (e.g., Mrigasthali), forest reserves (e.g., Gokarna), and some forest patches on the steep slopes.…”

Section: Lulc Change Trajectories In the Kvmentioning

confidence: 77%

Rapid Urban Growth in the Kathmandu Valley, Nepal: Monitoring Land Use Land Cover Dynamics of a Himalayan City with Landsat Imageries

2017

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Abstract:The Kathmandu Valley of Nepal epitomizes the growing urbanization trend spreading across the Himalayan foothills. This metropolitan valley has experienced a significant transformation of its landscapes in the last four decades resulting in substantial land use and land cover (LULC) change; however, no major systematic analysis of the urbanization trend and LULC has been conducted on this valley since 2000. When considering the importance of using LULC change as a window to study the broader changes in socio-ecological systems of this valley, our study first detected LULC change trajectories of this valley using four Landsat images of the year 1989, 1999, 2009, and 2016, and then analyzed the detected change in the light of a set of proximate causes and factors driving those changes. A pixel-based hybrid classification (unsupervised followed by supervised) approach was employed to classify these images into five LULC categories and analyze the LULC trajectories detected from them. Our results show that urban area expanded up to 412% in last three decades and the most of this expansion occurred with the conversions of 31% agricultural land. The majority of the urban expansion happened during 1989-2009, and it is still growing along the major roads in a concentric pattern, significantly altering the cityscape of the valley. The centrality feature of Kathmandu valley and the massive surge in rural-to-urban migration are identified as the primary proximate causes of the fast expansion of built-up areas and rapid conversions of agricultural areas.

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“…Studies on decadal datasets reveal that the built-up area has grown both in circular (radial growth of metropolitan cities) and linear patterns (settlements along the roads). In short, the present study provides important information to assess the changes in urban areas in terms of their spatial dimension and growth [46][47][48]. The maximum expansion in built-up area is observed around Delhi followed by Surat, Hyderabad, Bangalore, Kolkata, and Mumbai.…”

Section: Resultsmentioning

confidence: 98%

Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

et al. 2015

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India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for Remote Sens. 2015, 7 2403 comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

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Urbanization and agricultural land loss in India: Comparing satellite estimates with census data

Cited by 197 publications

References 31 publications

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Mapping 2000–2010 Impervious Surface Change in India Using Global Land Survey Landsat Data

Rapid Urban Growth in the Kathmandu Valley, Nepal: Monitoring Land Use Land Cover Dynamics of a Himalayan City with Landsat Imageries

Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

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