Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales

Lambin, Éric F.; Ehrlich, Daniele

doi:10.1080/01431169508954423

Cited by 91 publications

(36 citation statements)

References 10 publications

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“…This concept is based on the assumption that vegetation coverage mitigates high LST occurrence values. The relationship between thermal characteristics of surface and vegetation indices has been extensively documented in the literature [7,13,[48][49][50][51][52]. Many studies have observed a negative relationship between LST and vegetation indices.…”

Section: Lst and Biophysical Descriptorsmentioning

confidence: 99%

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

Gusso

Cafruni

Bordin³

et al. 2015

Sustainability

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For a reliable assessment of sustainability in big cities, it is imperative to evaluate urban ecosystem conditions and the environment of the cities undergoing economic growth. Urban green spaces are valuable sources of evapotranspiration, which is generated by trees and vegetation; these spaces mitigate urban heat islands in cities. Land surface temperature (LST) is closely related to the distribution of land-use and land-cover characteristics and can be used as an indicator of urban environment conditions and development. This study evaluates the patterns of LST distribution through time by employing the thermal spatial distribution signature procedure using thermal infrared data obtained from Landsat-5 Thematic Mapper. A set of 18 images, between 1985 and 2010, OPEN ACCESSSustainability 2015, 7 3130 was used to study the urban environment during summer in 47 neighborhoods of Porto Alegre, Brazil. On a neighborhood scale, results show a non-linear inverse correlation (R² = 0.55) between vegetation index and LST. The overall average of the LST is 300.23 K (27.8 C) with a standard deviation of 1.25 K and the maximum average difference of 2.83 K between neighborhoods. Results show that the Thermal Spatial Distribution Signature (TSDS) analysis can help multi-temporal studies for the evaluation of UHI through time.

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Section: Lst and Biophysical Descriptorsmentioning

confidence: 99%

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

Gusso

Cafruni

Bordin³

et al. 2015

Sustainability

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“…Alguns trabalhos já exploraram uma relação entre ín-dices de vegetação e o comportamento térmico da superfície, obtido por dados de sensoriamento remoto (Nemani et al, 1993;Goetz, 1997;Gupta et al, 1997;Lambin & Ehrlich, 1995;Sandholt et al, 2002;Sims et al, 2008). Alguns desses estudos têm sido desenvolvidos sobre firmes bases teóricas (Moran et al, 1994), outros, como também no presente trabalho, estão fortemente fundamentados em parametrizações empíricas de imagens de satélite obtidas por sensores colocados em órbita (Sandholt et al, 2002;Kogan, 2002;Wan et al, 2004), mas exigem grande quantidade de dados para assegurar que as condições de contorno estejam adequadamente estabelecidas.…”

Section: Combinação Do Ndvi E Lstunclassified

Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja

Gusso

2013

Rev. Ceres

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An early assessment of national crop development conditions while the plants are still in the fields is highly needed to calculate correctly projections for decision-making and policies related to government planning and food security. The aim of this study was to evaluate the suitability of NOAA /AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) to detect changes in vegetation conditions, due to water stress during soybean crop, by means a combination of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI). Both LST and NDVI data were combined and compared in a pixel basis over a soybean crop area in Rio Grande do Sul State. The predicted theoretical inverse relationship for the combination of LST and NDVI was detected. An average increase of LST was observed in a normal crop cycle ( from 301.02 K to 308.36 K) compared to a crop cycle under water stress condition. An average reduction in NDVI was observed for normal crop cycle development (from 0.65 to 0.53) compared to a crop cycle under drought-induced effects. It was observed a higher correlation of municipality yield with LST (R2=0.78) than NDVI (R2=0.59). Results obtained indicate that the aggregation of AVHRR images, from Uma avaliação inicial das condições do desenvolvimento da safra nacional, enquanto as plantas ainda estão nos campos, é altamente necessária para o cálculo correto das projeções na tomada de decisão e políticas relacionadas com o planejamento governamental e segurança alimentar. O objetivo deste trabalho foi avaliar a adequação dos dados NOAA/AVHRR (National Oceanic and Atmospheric Administration / Advanced Very High Resolution Radiometer) em detectar mudanças nas condições da vegetação, devidas à ocorrência de estresse hídrico, na soja, por meio de uma combinação do índice NDVI (Normalized Difference Vegetation Index) e da LST (Land Surface Temperature). Os dados LST e NDVI foram combinados e comparados pixel a pixel, sobre uma área de cultivo de soja, no Rio Grande do Sul. A relação teórica inversa prevista na combinação de LST e NDVI foi detectada. Foi observado que ocorre um aumento médio na LST em uma safra de ciclo normal (de 301,02 K para 308,36 K), quando comparada a uma safra sob condição de estresse hídrico, no desenvolvimento da cultura. Uma redução média do NDVI foi observada no ciclo normal (de 0,65 para 0,53), comparada com uma safra sob efeitos ocasionados pela estiagem no desenvolvimento da cultura. Foi observado maior correlação da produtividade municipal com LST (R2=0,78) do que com o NDVI (R2 = 0,59). Os resultados obtidos indicam que a integração de imagens do sensor AVHRR, proveniente de diferentes instituições, proporciona a adequada combinação espacial e temporal dos dados LST e NDVI, a fim de detectar a ocorrência de estresse hídrico, bem como sua intensidade, caracterizando as condições do ciclo de desenvolvimento da soja.Palavras-chave: sensoriamento remoto, estiagem, temperatura da superfície terrestre, índice de vegetação.

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“…Kerber and Schutt (1986) have used AVHRR channel 3 data, which is sensitive to re ected and emitted radiation, to locate the boundary between forest and non-forest. More recently, Lambin and Ehrlich (1995) have used multi-temporal NDVI, land surface temperature (T s ) and the ratio between these two variables to map land cover based on the AVHRR Global Area Coverage (GAC) dataset over the African continent, and concluded that the ratio between T s and NDVI shows the greater potential to yield a reliable land cover classi cation at the continental/global scale than does a single variable of either T s or NDVI. In this study, the authors developed a land cover map of the whole of Asia using the NOAA AVHRR 1-km dataset.…”

Section: Introductionmentioning

confidence: 99%

30-second degree grid land cover classification of Asia

Wen

Tateishi

2001

International Journal of Remote Sensing

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With the development of the global economy, environmental research has become more important than ever, especially in the Asian region. The objective of this study is to produce a land cover classi cation dataset for the whole of Asia using the NOAA AVHRR 1-km dataset. Ground data were mainly collected from existing thematic maps which were obtained from members of the Land Cover Working Group (LCWG) of the Asian Association of Remote Sensing (AARS). Classi cation was mainly based on cluster analysis of the monthly ratio of surface temperature and Normalized DiVerence Vegetation Index (NDVI ) for seven months from April to October 1992. Additional variables, such as DEM, the maximum monthly composite NDVI in a year, and the minimum monthly composite NDVI in a year were also used in the classi cation processing. In order to add and improve ground data in the future, collected ground data will be published with the developed land cover dataset. IntroductionThe National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has been serving an important role in global/continental land use management and planning, and analysis of land cover change. Most land cover mapping applications at broad spatial scales have been based on multi-temporal Normalized DiVerence Vegetation Index (NDVI) data (Tucker et al. 1985, Loveland et al. 1991. In order to extract phenomenological information contained in AVHRR data more eVectively, the thermal data can be used for land cover mapping. Kerber and Schutt (1986) have used AVHRR channel 3 data, which is sensitive to re ected and emitted radiation, to locate the boundary between forest and non-forest. More recently, Lambin and Ehrlich (1995) have used multi-temporal NDVI, land surface temperature (T s ) and the ratio between these two variables to map land cover based on the AVHRR Global Area Coverage (GAC) dataset over the African continent, and concluded that the ratio between T s and NDVI shows the greater potential to yield a reliable land cover classi cation at the continental/global scale than does a single variable of either T s or NDVI. In this study, the authors developed a land cover map of the whole of Asia using the NOAA AVHRR 1-km dataset. Classi cation was performed in the following steps:

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Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales

Cited by 91 publications

References 10 publications

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja

30-second degree grid land cover classification of Asia

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