Estimation of urban vegetation abundance by spectral mixture analysis

Small, Christopher

doi:10.1080/01431160151144369

Cited by 415 publications

(243 citation statements)

References 41 publications

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“…This was done by identifying 11 classes of regions of interest on the satellite image: built-up areas, roads, water bodies, bare soil, two types of bare rocks (in desert or Mediterranean areas), urban grass lawns, and four types of natural vegetation. As urban vegetation cover can be reliably estimated using linear spectral unmixing (Small 2001), we applied a supervised unmixing mapping method implemented in Idrisi 32 called probability guided linear spectral unmixing (Clark Labs 2002). Eleven maps were produced representing the percentage cover of each one of the 11 classes in the image.…”

Section: Rectification Of the Satellite Images To The Israel New Gridmentioning

confidence: 99%

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Bino

Levin

Darawshi

et al. 2008

International Journal of Remote Sensing

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Urban landscapes are expanding rapidly and are reshaping the distribution of many animal and plant species. With these changes, the need to understand and to include urban biodiversity patterns in research and management programmes is becoming vital. Recent studies have shown that remote sensing tools can be useful in studies examining biodiversity patterns in natural landscapes. The present study aimed to explore whether remote sensing tools can be applied in biodiversity research in an urban landscape. More specifically, the study examined whether the Landsat-derived Normalized Difference Vegetation Index (NDVI) and linear spectral unmixing of urban land cover can predict bird richness in the city of Jerusalem. Bird richness was sampled in 40 1-ha sites over a range of urban environments in 329 surveys. NDVI and the per cent cover of built-up area were strongly and negatively correlated with each other, and were both very successful in explaining the number of bird species in the study sites. Mean NDVI in each site was positively correlated with the site bird species richness. A hump-shaped relationship between NDVI and species richness was observed (when calculated over increasing spatial scales), with a maximum value (Pearson's R50.87, p,0.001, n540) at a scale of 15 ha. We suggest that remote sensing approaches may provide planners and conservation biologists with an efficient and cost-effective method to study and estimate biodiversity across urban environments that range between densely built-up areas, residential neighbourhoods, urban parks and the peri-urban environment.

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Section: Rectification Of the Satellite Images To The Israel New Gridmentioning

confidence: 99%

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Bino

Levin

Darawshi

et al. 2008

International Journal of Remote Sensing

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“…Land use types with different thermal properties and radiation features make different contributions to the UHI. For example, normalized differential vegetation index (NDVI ) is traditionally used as the indicator of vegetation abundance to estimate the LST-vegetation relationship, and the scatter diagram proved that there was a negative correlation between NDVI and LST [37,38]. Artificial buildings directly change the ratio of surface sensible heat flux and latent heat flux.…”

Section: Introductionmentioning

confidence: 99%

Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China

Yang

Yan

et al. 2017

Sustainability

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Abstract:The spatio-temporal patterns of land use/land cover changes (LUCC) can significantly affect the distribution and intensity of the urban heat island (UHI) effect. However, few studies have mapped a clear picture of the influence of LUCC on UHI. In this study, both qualitative and quantitative models are employed to explore the effect of LUCC on UHI. UHI and LUCC maps were retrieved from Landsat data acquired from 1984, 1992, 2000, 2007, and 2014 to show their spatio-temporal patterns. The results showed that: (1) both the patterns of LUCC and UHI have had dramatic changes in the past 30 years. The urban area of Changchun increased more than four times, from 143.15 km 2 in 1984 to 577.45 km 2 in 2014, and the proportion of UHI regions has increased from 15.27% in 1984 to 29.62% in 2014; (2) the spatiotemporal changes in thermal environment were consistent with the process of urbanization. The average LST of the study area has been continuously increasing as many other land use types have been transformed to urban regions. The mean temperatures were higher in urban regions than rural areas over all of the periods, but the UHI intensity varied based on different measurements; and (3) the thermal environment inside the city varied widely even within a small area. The LST possesses a very strong positive relationship with impervious surface area (ISA), and the relationship has become stronger in recent years. The UHI we employ, specifically in this study, is SUHI (surface urban heat island).

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“…Several methods for retrieval of FVC using remote sensing have been developed including spectral mixture analysis (SMA) [2][3][4], artificial neural networks [5][6][7], fuzzy classifiers [8], maximum likelihood classifiers [9], regression trees [10][11][12], and simple regression based on the Normalized Difference Vegetation Index (NDVI) [13]. In particular, SMA has often been used to estimate FVC from multi-spectral remote sensing data [2,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

Wei

Zeng

et al. 2014

Remote Sensing

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Abstract:Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VI soil and VI veg , with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.

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Estimation of urban vegetation abundance by spectral mixture analysis

Cited by 415 publications

References 41 publications

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China

Improving Estimates of Grassland Fractional Vegetation Cover Based on a Pixel Dichotomy Model: A Case Study in Inner Mongolia, China

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