Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics

Fichera, Carmelo Riccardo; Modica, Giuseppe; Pollino, Maurizio

doi:10.5721/eujrs20124501

Cited by 256 publications

(157 citation statements)

References 33 publications

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“…Fichera et al (2012) carried out a change detection analysis in Avellino, Italy from 1954 to 2004 using multi-source imagery including aerial photographs, ancillary data and Landsat images. Local urban landscape patterns using landscape metrics were investigated by same authors for evaluating landscape indices measured along the two transects (W-E and SW-NE directions) converging in the core urban area of the Avellino City.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

Mugiraneza

Haas

Ban

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88% with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349 ha to 11,579 ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

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Section: Related Workmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

Mugiraneza

Haas

Ban

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The same theoretical and methodological steps taken to build the product are still replicable to other assessments, keeping the basic knowledge fixed on the classification of land uses (Cerreta and De Toro, 2012;Fichera et al, 2011Fichera et al, , 2012. However, it is not possible, without a real experimentation in other fields, to assess whether the routine, structured as follows, although replicable, is the most appropriate for subjects of a different nature.…”

Section: Perspectives and Remarksmentioning

confidence: 99%

The support of multidimensional approaches in integrate monitoring for SEA: a case of study

Torre

Selicato

2013

Earth Syst. Dynam.

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Abstract. During the centuries, the seaside has represented a crucial pole for future human development and civilization. The use of the sea for transport and trade and the overwhelming availability of food derived from coastal waters have encouraged and strengthened the growth of urban settlements. In the same time, the human pressure menaces to destroy coastal habitats and consequently their carrying capacity that allows for many essential functions.

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“…The changes of landscape, as a complex structure of land use, at different scales, shapes, and quantities, express inherence mechanism of land use and land cover change (LULCC) [2]. The research of interaction mechanism between landscape pattern and LUCC has become an essential scientific topic in earth surface environmental change [3].…”

Section: Introductionmentioning

confidence: 99%

“…At present, more and more attentions are paid to the changing trend, vulnerability, sensitivity, and heterogeneity analysis methods of landscape pattern, such as landscape pattern index, the particle size effect, and spatial statistics algorithm [2,[5][6][7]. The wildly accepted ecological security assessment system include the "ecological footprint" concept and model, the concept of pressure-state-response (PSR) framework model, and the concept of natureeconomy-society framework model [8,9].…”

Section: Introductionmentioning

confidence: 99%

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

et al. 2018

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In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CAMarkov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026.

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Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics

Cited by 256 publications

References 33 publications

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

The support of multidimensional approaches in integrate monitoring for SEA: a case of study

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

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