CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES
ABSTRACT Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landsca...
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| description | ABSTRACT Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property) and range (φ-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.
RESUMO Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetro |
| doi_str_mv | 10.1590/01047760201723042370 |
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RESUMO Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (σ²-variabilidade espacial total) e alcance (φ-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros σ² e φ, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. Outras aplicações combinando estas duas técnicas devem ser investigadas, como por exemplo a detecção de mudanças na cobertura do solo e a classificação de imagens utilizando análises orientada a objetos (OBIA).</description><identifier>ISSN: 0104-7760</identifier><identifier>ISSN: 2317-6342</identifier><identifier>EISSN: 2317-6342</identifier><identifier>DOI: 10.1590/01047760201723042370</identifier><language>eng</language><publisher>UFLA - Universidade Federal de Lavras</publisher><subject>FORESTRY</subject><ispartof>CERNE, 2017-12, Vol.23 (4), p.413-422</ispartof><rights>This work is licensed under a Creative Commons Attribution 4.0 International License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-3e705f8510872dea79332b0650d08d319c965c166fba5445f4fb151a1072422c3</citedby><cites>FETCH-LOGICAL-c336t-3e705f8510872dea79332b0650d08d319c965c166fba5445f4fb151a1072422c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,860,881,27901,27902</link.rule.ids></links><search><creatorcontrib>Silveira, Eduarda Martiniano de Oliveira</creatorcontrib><creatorcontrib>Mello, José Márcio de</creatorcontrib><creatorcontrib>Acerbi Júnior, Fausto Weimar</creatorcontrib><creatorcontrib>Reis, Aliny Aparecida dos</creatorcontrib><creatorcontrib>Withey, Kieran Daniel</creatorcontrib><creatorcontrib>Ruiz, Luis Angel</creatorcontrib><title>CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES</title><title>CERNE</title><addtitle>CERNE</addtitle><description>ABSTRACT Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property) and range (φ-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.
RESUMO Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (σ²-variabilidade espacial total) e alcance (φ-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros σ² e φ, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. 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The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property) and range (φ-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.
RESUMO Assumindo a existência de uma relação entre a heterogeneidade da paisagem e medidas de dependência espacial obtidas de dados de sensoriamento remoto, o objetivo deste estudo foi avaliar o potencial dos parâmetros do semivariograma derivados de imagens de satélite com diferentes resoluções espaciais, para caracterizar áreas cobertas por floresta e áreas sob ação antrópica. Para isso, o NDVI (Índice de Vegetação da Diferença Normalizada) de cada umas das imagens (SPOT 6, Landsat 8 e MODIS Terra) foi gerado em uma área de floresta tropical Amazônica (1.000 km²), onde foram selecionadas amostras (1 x 1 km) de áreas florestadas e áreas antrópicas. A partir destes dados, foram gerados os semivariogramas e extraídos os parâmetros patamar (σ²-variabilidade espacial total) e alcance (φ-distância dentro da qual as amostras apresentam-se estruturadas espacialmente). A análise revelou que a resolução espacial das imagens influencia os parâmetros σ² e φ, apresentando significativo aumento das áreas de florestas para as áreas sob ação antrópica. A maior variação entre estas classes foi obtida com as imagens Landsat 8, indicando estas imagens, com resolução espacial de 30 metros, a mais apropriada para a obtenção dos parâmetros do semivariograma objetivando a caracterização da heterogeneidade espacial da paisagem. Combinando o sensoriamento remoto e técnicas geostatisticas, demonstrou-se que os parâmetros do semivariograma derivados de imagens NDVI podem ser utilizados como um simples indicador de heterogeneidade da paisagem, gerando mapas que permitem aos pesquisadores delinearem com maior eficácia o regime de amostragem. Outras aplicações combinando estas duas técnicas devem ser investigadas, como por exemplo a detecção de mudanças na cobertura do solo e a classificação de imagens utilizando análises orientada a objetos (OBIA).</abstract><pub>UFLA - Universidade Federal de Lavras</pub><doi>10.1590/01047760201723042370</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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| title | CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES |
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