Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

Peiman, Reihaneh

doi:10.1080/01431161.2010.486806

Cited by 55 publications

(28 citation statements)

References 14 publications

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“…Furthermore, direct use of multi-temporal spectral responses may not lead to satisfactory change detection results, since change detection based on spectral response or textures is sensitive to factors such as different atmospheric conditions, soil moisture conditions, sun elevation angle and plant phenology [37]. In order to avoid these impacts, we used the post-classification method by conducting image classification for each date of imagery separately before conducting change detection, like many previous land-use change studies [38][39][40][41].…”

Section: Urban Land Cover Mappingmentioning

confidence: 99%

Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City

Yan

Zhang

et al. 2015

Remote Sensing

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Lack of research into the complexity in urban land conversion, and paucity of observational data of soil organic carbon (SOC) beneath impervious surface area (ISA) limit our understanding of the urbanization effects on carbon (C) pools in dryland cities. Employing Landsat TM images acquired in 1990 and 2010, a hybrid classification method consisting of Linear Spectral Mixture Analysis and decision tree classification was applied to retrieve the land cover (water, ISA, greenspace, cropland, and remnant desert) of the largest dryland city in China-Urumqi. Based on vegetation carbon (VEGC) and SOC density data determined through field observations and literature reviews, we developed Urumqi's C pool maps in 1990 and 2010, and assessed the urbanization impacts on ecosystem C. Our results showed that ISA tripled from 1990 to 2010 displacing remnant desert and cropland. The urban landscape, especially the greenspaces, became obviously fragmented. In 2010, more than 95% of the urban ecosystem C was SOC, 48% of which under the ISA. The city lost 19% of C stock from 1990 to 2010. About 82% of the ecosystem C loss was caused by the conversion of remnant desert and cropland into ISA, mainly in the northern city.

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Section: Urban Land Cover Mappingmentioning

confidence: 99%

Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City

Yan

Zhang

et al. 2015

Remote Sensing

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“…The largest increases of runoff were found when converting forest and savanna to agriculture, whereas the largest decrease of runoff resulted from a conversion to forest in this study. Further Indian studies focus on the impact of land use change on groundwater (e.g., Khan et al, 2011;Ramesh, 2001;Singh, 2001).…”

Section: P D Wagner Et Al: Impacts Of Land Use Changes On Water Rementioning

confidence: 99%

An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India

Wagner

Kumar

Schneider

2013

Hydrol. Earth Syst. Sci.

162

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Abstract. Land use changes are altering the hydrologic system and have potentially large impacts on water resources. Rapid socio-economic development drives land use change. This is particularly true in the case of the rapidly developing city of Pune, India. The present study aims at analyzing past land use changes between 1989 and 2009 and their impacts on the water balance in the Mula and Mutha Rivers catchment upstream of Pune. Land use changes were identified from three Rivers catchment multitemporal land use classifications for the cropping years 1989/1990, 2000/2001, and 2009/2010. The hydrologic model SWAT (Soil and Water Assessment Tool) was used to assess impacts on runoff and evapotranspiration. Two model runs were performed and compared using the land use classifications of 1989/1990 and 2009/2010. The main land use changes were identified as an increase of urban area from 5.1 % to 10.1 % and cropland from 9.7 % to 13.5 % of the catchment area during the 20 yr period. Urbanization was mainly observed in the eastern part and conversion to cropland in the mid-northern part of the catchment. At the catchment scale we found that the impacts of these land use changes on the water balance cancel each other out. However, at the sub-basin scale urbanization led to an increase of the water yield by up to 7.6 %, and a similar decrease of evapotranspiration, whereas the increase of cropland resulted in an increase of evapotranspiration by up to 5.9 %.

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“…Therefore, many applications are focusing on LULC change and urban growth employed PCCD method to identify specific categories of LULC. Thus to explore the change pattern and change effect on surrounding environment (Abd El-Kawy et al 2011;Yuan et al 2005;Sundarakumar et al 2012;Peiman 2011) has been stressed.…”

Section: Change Detection Methodsmentioning

confidence: 99%

“…It showed that the most rapid growth happened during 1986-1992. According to previous studies, most of the urban area change detection analyses were conducted based on bi-temporal scheme (Afify 2011) or coarsely multitemporal scheme (Abd El-Kawy et al 2011;Yuan et al 2005;Sundarakumar et al 2012;Peiman 2011;Tian et al 2011;Zha et al 2003;Zhao et al 2005). With easy accessibility of data availability recently, more and more studies used multi-temporal datasets to detect change dynamics of urban area.…”

Section: Change Detection Methodsmentioning

confidence: 99%

Monitoring and Modeling of Global Changes: A Geomatics Perspective

Li¹,

Yang²

2015

Springer Remote Sensing/Photogrammetry

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Pre-classification and post-classification change-detection techniques to monitor land-cover and land-use change using multi-temporal Landsat imagery: a case study on Pisa Province in Italy

Cited by 55 publications

References 14 publications

Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City

Urban Land-Cover Change and Its Impact on the Ecosystem Carbon Storage in a Dryland City

An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India

Monitoring and Modeling of Global Changes: A Geomatics Perspective

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