Principal component analysis applied to feature-oriented band ratios of hyperspectral data: A tool for vegetation studies

Almeida, Teodoro Isnard Ribeiro de; Filho, Cláudio Henrique de Melo Pereira

doi:10.1080/01431160412331270812

Cited by 36 publications

(14 citation statements)

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“…These four bands contain most of the spectral information in vegetation-related studies (Huemmrich et al 1999, Almeida andFilho 2004), including two visible and a near infra-red bands (TM/ETM 2, 3, and 4), and a middle infra-red (TM/ETM 5). The second, third and fourth components showed the most striking in relation to the fire features of interest.…”

Section: Principal Component Analysismentioning

confidence: 99%

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Segah

Tani

Hirano

2010

International Journal of Remote Sensing

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Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat TM/ETM+ satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002) and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. SPOT-VEGETATION 10-days Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat TM/ETM+, SPOT-VEGETATION data and ground-based NDVI to identifying land cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires that occurred in 1997 and 2002

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Section: Principal Component Analysismentioning

confidence: 99%

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Segah

Tani

Hirano

2010

International Journal of Remote Sensing

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“…o mapeamento destas duas variáveis concomitantemente implica que o método é capaz de rastrear a assinatura específica da vegetação estressada (marcada por variações bruscas de gradiente entre bandas), bem como variações na reflectância relativa de espectros de mesma geometria. No caso do MF-MT, a menor quantidade de pixels mapeados como anômalos do ponto de vista geobotânico deve-se pelo menos aos seguintes fatores: (i) o método busca a assinatura específica de endmembers, o que restringe o comportamento espectral mais holís-tico, característico da vegetação (e.g, Almeida & Souza Filho, 2004); (ii) o algoritmo MT diminui consideravelmente a quantidade de pixels mapeados, função da possibilidade de descarte de falsos-positivos.…”

Section: Figura 9 -Análise De Dados Aster Na Estação Seca (A): Imageunclassified

Detecção de exsudações de hidrocarbonetos por geobotânica e sensoriamento remoto multi-temporal: estudo de caso no Remanso do Fogo (MG)

Filho

Augusto

Oliveira

et al. 2008

RBG

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“…Our use of PCA helped reduce spectral overlap and redundancy of image data, which has been shown to improve model discrimination between vegetation and bare ground in savannas [99]. While the overall accuracy of our classification was 86.7% (95% CI 0.843, 0.888) with a Kappa coefficient of 0.832, it is important to note that any land cover classification will contain error, and this is particularly true for semi-arid savanna systems where environmental variability creates a mosaic landscape of diverse plant community assemblages [62].…”

Section: Limitationsmentioning

confidence: 99%

Land Cover Change in Northern Botswana: The Influence of Climate, Fire, and Elephants on Semi-Arid Savanna Woodlands

Fox

Vandewalle²,

Alexander

2017

Land

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Abstract:Complex couplings and feedback among climate, fire, and herbivory drive short-and long-term patterns of land cover change (LCC) in savanna ecosystems. However, understanding of spatial and temporal LCC patterns in these environments is limited, particularly for semi-arid regions transitional between arid and more mesic climates. Here, we use post-classification analysis of Landsat TM (1990), ETM+ (2003), and OLI (2013) satellite imagery to classify and assess net and gross LCC for the Chobe District, a 21,000 km 2 area encompassing urban, peri-urban, rural, communally-managed (Chobe Enclave), and protected land (Chobe National Park, CNP, and six protected forest reserves). We then evaluate spatiotemporal patterns of LCC in relation to precipitation, fire detections (MCD14M, 2001(MCD14M, -2013 from the Moderate Resolution Imaging Spectroradiometer (MODIS), and dry season elephant (Loxodonta africana) aerial survey data (2003, 2006, 2012, 2013). Woodland cover declined over the study period by 1514 km 2 (16.2% of initial class total), accompanied by expansion of shrubland (1305 km 2 , 15.7%) and grassland (265 km 2 , 20.3%). Net LCC differed importantly in protected areas, with higher woodland losses observed in forest reserves compared to the CNP. Loss of woodland was also higher in communally-managed land for the study period, despite gains from 2003-2013. Gross (class) changes were characterized by extensive exchange between woodland and shrubland during both time steps, and a large expansion of shrubland into grassland and bare ground from 2003-2013. MODIS active fire detections were highly variable from year to year and among the different protected areas, ranging from 1.8 fires*year −1 /km 2 in the Chobe Forest Reserve to 7.1 fires*year −1 /km 2 in the Kasane Forest Reserve Extension. Clustering and timing of dry season fires suggests that ignitions were predominately from anthropogenic sources. Annual fire count was significantly related to total annual rainfall (p = 0.009, adj. R 2 = 0.50), with a 41% increase in average fire occurrence in years when rainfall exceeded long-term mean annual precipitation (MAP). Loss of woodland was significantly associated with fire in locations experiencing 15 or more ignitions during the period 2001-2013 (p = 0.024). Although elephant-mediated damage is often cited as a major cause of woodland degradation in northern Botswana, we observed little evidence of unsustainable pressure on woodlands from growing elephant populations. Our data indicate broad-scale LCC processes in semi-arid savannas in Southern Africa are strongly coupled to environmental and anthropogenic forcings. Increased seasonal variability is likely to have important effects on the distribution of savanna plant communities due to climate-fire feedbacks. Long-term monitoring of LCC in these ecosystems is essential to improving land use planning and management strategies that protect biodiversity, as well as traditional cultures and livelihoods under future climate change scenarios for Southern Afr...

show abstract

Principal component analysis applied to feature-oriented band ratios of hyperspectral data: A tool for vegetation studies

Cited by 36 publications

References 0 publications

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Detection of fire impact and vegetation recovery over tropical peat swamp forest by satellite data and ground-based NDVI instrument

Detecção de exsudações de hidrocarbonetos por geobotânica e sensoriamento remoto multi-temporal: estudo de caso no Remanso do Fogo (MG)

Land Cover Change in Northern Botswana: The Influence of Climate, Fire, and Elephants on Semi-Arid Savanna Woodlands

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