High spatial resolution spectral mixture analysis of urban reflectance

Small, Christopher

doi:10.1016/j.rse.2003.04.008

Cited by 239 publications

(129 citation statements)

References 23 publications

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“…Band sharpening, applying the most commonly used algorithm known as Intensity-Hue-Saturation (IHS; see Vrabel 1996, Park andKang 2004). This was done to improve mapping accuracy of the Landsat image, as the characteristic scale of urban reflectance is usually between 10 and 20 m (Small 2003). 2.…”

Section: Satellite Images and Remote Sensing Applicationmentioning

confidence: 99%

“…The increasing availability of remotely sensed observations facilitates the development of new tools and approaches for understanding the urban environment (Ben-Dor et al 2001, Miller andSmall 2003). Satellite-derived vegetation indices are excellent estimates of productivity and can also quantify spatial heterogeneity of vegetation, two important factors shaping biodiversity patterns (Tucker andSellers 1986, Mittelbach et al 2001).…”

Section: Introductionmentioning

confidence: 99%

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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: Satellite Images and Remote Sensing Applicationmentioning

confidence: 99%

Section: Introductionmentioning

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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“…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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“…Rapid socio-economic development and population growth have greatly encouraged the expansion of urban land areas [2,3]. According to the World Bank, by the end of 2015, the global urbanization rate had hit 53.85%, a dramatic increase from 46.54% in 2000.…”

Section: Introductionmentioning

confidence: 99%

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

Wang

Wan

et al. 2017

Remote Sensing

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Abstract:The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer's accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user's accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.

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High spatial resolution spectral mixture analysis of urban reflectance

Cited by 239 publications

References 23 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

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

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

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