Assessing Biodiversity from Space: an Example from the Western Ghats, India

Bawa, Kamaljit S.; Rose, Joseph; Ganeshaiah, K. N.; Barve, Narayani; Kiran, M.C.; Umashaanker, R.

doi:10.5751/es-00434-060207

Cited by 58 publications

(55 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies in evergreen landscapes [29,48] indicate that Landsat derived indices of vegetation are highly sensitive to plant abundance, perhaps even more so than to species richness and Shannon diversity. Yet, we find in our study that vegetation indices have low, non significant relationships with stand density, while they demonstrate stronger relationships with species richness and diversity.…”

Section: Discussionmentioning

confidence: 99%

“…In order to account for these positional uncertainties, we instead associated each plot with a 3 × 3 window around the central pixel within which the plot was located, and calculated the average value for the 9 pixels located within this window (see also [28]). This was done for the 30 m bands 1-5 and 7, Tasseled Cap indices of Brightness, Greenness, and Wetness, and the NDVI, Infra Red Index (IRI), Middle Infra Red Index (MIRI), which are believed to relate to vegetation density and species diversity [11,26,29]. For the 15 m panchromatic band, we used a correspondingly larger 6 × 6 window.…”

Section: Image Data Acquisition and Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Nagendra

Rocchini

Ghate³

et al. 2010

Remote Sensing

View full text Add to dashboard Cite

While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Image Data Acquisition and Pre-processingmentioning

confidence: 99%

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Nagendra

Rocchini

Ghate³

et al. 2010

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Several recent studies have demonstrated the potential of using remote sensing approaches in predicting species richness in natural landscapes (e.g. Bawa et al 2002, Luoto et al 2002, Oindo and Skidmore 2002, Willis and Whittaker 2002, Honnay et al 2003, Seto et al 2004, Nichol and Lee 2005, St-Louis et al 2006). Nevertheless, a recent literature review that included over 120 studies in which satellite images were used for avian applications (Gottschalk et al 2005) showed that few studies used satellite images when focusing on avian biodiversity in urban environments (Berry et al 1998, Alberti et al 2000, Mö rtberg and Wallentinus 2000.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…However, some of these indices can be sensitive to spatial resolution and to the number of landcover classes, making generalizations of their relationships to species richness difficult [4]. Moreover, researchers have demonstrated that the within-region variability of NDVI values, for instance defined as the standard deviation of maximum NDVI relate to the heterogeneity of habitats, and consequently have a positive relationship with species richness [19,20].…”

Section: Introductionmentioning

confidence: 99%

Predicting Mammal Species Richness from Remotely Sensed Data at Different Spatial Scales

Oindo¹

2008

TORMSJ

View full text Add to dashboard Cite

Spatial variability in species richness has been postulated to depend upon environmental factors such as climatic variability, Net primary productivity and habitat heterogeneity. The Advanced Very High Resolution Radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) has been shown to be correlated with climatic variability, Net primary productivity and habitat heterogeneity. Moreover, Landsat Thematic Mapper (TM) derived habitat diversity indices have been used to reflect habitat heterogeneity. Interannually average NDVI and its variability (standard deviation and coefficient of variation) as well as Landsat Thematic Mapper derived habitat diversity index were correlated with mammal species richness at landscape scale. Species richness related unimodally to interannual average NDVI and positively to variability of NDVI and habitat diversity index. Conversely, at regional scale mammal species richness were correlated with interannually average NDVI and coefficient of variation of NDVI. Species richness related negatively to the latter and positively to interannually average NDVI. Though these relationships are indirect, they apparently operate through the green vegetation cover. Understanding such relationships can help in estimating changes in species richness in response to global climatic change.

show abstract

Assessing Biodiversity from Space: an Example from the Western Ghats, India

Cited by 58 publications

References 10 publications

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

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

Predicting Mammal Species Richness from Remotely Sensed Data at Different Spatial Scales

Contact Info

Product

Resources

About