Evaluating land use/cover change with temporal satellite data and information systems

Erener, Arzu; Düzgün, Şebnem; Yalçıner, Ahmet Cevdet

doi:10.1016/j.protcy.2012.02.079

Cited by 36 publications

(19 citation statements)

References 8 publications

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“…The most common way to present map classification accuracy outputs is using confusion matrix/error matrix [9]. For this study, 140 pixels were randomly selected from the classified images and the reference data collected on the real ground.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Assessment of land use and land cover change in Finote Birihan kebele, Amhara region, Ethiopia

Yigez¹,

Guo²,

Tsega³

2018

IJSRP

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A small scale changes in land use and land cover (LULC) in different part of the planet has a substantial contribution for the regional or global LULC changes. The combined effect of these changes could also result a global impact on the natural as well as the human environment. To make informed planning and management decisions in addressing these adverse impacts, in-deep knowledge and information on the local scale changes require an immediate attention. This study aimed to investigate the extent and magnitude of LULC changes in Finote Birihan kebele, Ethiopia from 1986 to 2018 using Geographic information system (GIS) and remote sensing approaches. It is found that in the current study area, agriculture land and built-up area has increased by about 37 and 497%, respectively at the expense of grassland (55%), forest land (33%) and shrub lands (32%).

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Section: Accuracy Assessmentmentioning

confidence: 99%

Assessment of land use and land cover change in Finote Birihan kebele, Amhara region, Ethiopia

Yigez¹,

Guo²,

Tsega³

2018

IJSRP

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“…The number of urban dwellers exceeded that of the rural in 2007, and most of the population growth has been concentrated in urban areas since then, resulting in further expansion and/or densification of urban areas (Haaland andvan den Bosch 2015, Gao et al 2019). Urban expansion and/or within-urban changes had diverse impacts on urban greenspace (UG) (Erener et al 2012, Zhao et al 2013, Yang et al 2014, Zhou et al 2018. For example, city densification can lead to the removal of greenspace for development on the one hand (Fuller andGaston 2009, Brunner andCozens 2013), but can also result in the generation of new greenspace simultaneously due to the high demand of UG in cities (Qian et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution of urban greenspace in response to urban development from a multi-scale perspective

Wang

Zhou

Wang

et al. 2020

Environ. Res. Lett.

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Urban expansion and renewal are one of the major drivers affecting urban landscapes worldwide. Considerable research has been conducted to understand how urban greenspace (UG) changes in response to urbanization at various scales from individual patches to landscapes. However, most of these studies have been conducted at a single scale, and little is known from a multiple scale perspective. Here, we present a multi-scale analytical framework to quantify the spatial pattern of greenspace and its change by integrating rank-size distribution, urban-rural gradient analysis and hotspots analysis. We applied this framework to nine major Chinese cities using 2.5 m resolution Advanced Land Observation Satellite and Systeme Probatoire d'Observation de la Terre imagery captured in 2005 and 2010. We found the multi-scale assessment provides integrated and synthesized information about the dynamics of UG that would otherwise be missed. First, the hotspots analysis revealed dramatic change in UG for all the nine cities, and such change tended to be spatially clustered. Second, the spatial heterogeneity of UG decreased from 2005 to 2010 for all the cities as a result of the increase of UG in urban core areas and loss of UG in the urban periphery, resulting in landscape homogenization along the urban-rural gradient. Third, substantial loss of UG co-occurred with densification of urban land development, indicating potential adverse impacts of compact city on urban greenery. Infill development became dominant, with percentage ranging from 52.7% in Shanghai to 90.6% in Nanjing, resulting in more compact urban form. This study underscores the importance of a multi-scale perspective on understanding the spatial distribution of UG and its change, and its response to urban development.

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“…Understanding the distribution of land cover is crucial to the better understanding of the earth's fundamental characteristics and processes, including productivity of the land, the diversity of plant and animal species, and the biogeochemical and hydrological cycles (Giri, 2012). Several authors have reported that remote sensing data (Rader and Optical) are become an important tools for gathering, monitoring and mapping land cover types using different methods and techniques (Pilesjo, 1992, Chen et al, 2016, Osman, 1996, Lillesand and Kiefer, 1989, Salih et al, 2017, Sobrino et al, 2004, Erener et al, 2011. For example, Osman (1996) suggest that the application of nonparametric methods or knowledge-based image analysis methods to increase the degree of classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

Salih¹

2018

Preprint

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Accurate, detailed and recent Information about land cover/use is important and much more needed for different aspects of sustainable development and environmental management. As remote sensing datasets are becomes one of the most important and effective tools to generate such information, this study aimed to generating land cover map for sub area in Al-Ahasaa Oasis, Saudi Arabia, by using and classifying a subset of Landsat-ETM+ image of the selected study area, as bases and required input for future studies and researches. Different image preprocessing techniques in addition to a will-known and widely used classification method (i.e., Maximum Likelihood classifier) were applied. To be reliable with the final product, accuracy assessment was carried out with 89% agreement and accepted according to the applied method. Different land cover classes were found in the study area, which includes (Sand dunes, Water bodies, Sabakha, Bare soil, Urban, and Agricultural lands). The study also revealed that the dominant land cover class is sand dunes with approximately ± 70% in area. The study strongly indicated that the area has long been affected by sand movement. Finally, the study suggested that, further researches with more advanced methods rather than traditional methods are needed in the future to support the findings of this study, with high degree of accuracy.

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Evaluating land use/cover change with temporal satellite data and information systems

Cited by 36 publications

References 8 publications

Assessment of land use and land cover change in Finote Birihan kebele, Amhara region, Ethiopia

Assessment of land use and land cover change in Finote Birihan kebele, Amhara region, Ethiopia

Spatial distribution of urban greenspace in response to urban development from a multi-scale perspective

Analysis of Remotely Sensed Data for Mapping Land Cover Types by Applying a Maximum Likelihood Classifier Algorithm, in Al-Ahsaa Oasis, Eastern Region, Saudi Arabia

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