Biology subjects

Arasumani, M.

Publications and source records attributed to Arasumani, M..

3 recordsLinked to original sources

bioRxiv2021

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Invasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species. Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall -98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.

ecology↗

bioRxiv2020

Opportunities and challenges in using remote sensing for identifying grassland restoration sites and invasive tree species management in a global biodiversity hotspot

Tropical montane grasslands (TMG) support biodiverse and endemic taxa and provide vital ecosystems services to downstream communities. Yet invasive alien tree species across the world have threatened tropical grasslands and grassland endemic species. In India, TMG in the Shola Sky Islands of the Western Ghats have been reduced due to exotic tree invasions (acacias, pines, and eucalyptus species). The loss of grassland habitat has, in turn, reduced the range sizes of species endemic to grasslands (plants, birds, amphibians, and mammals), driving some populations to local extinction. Grassland conversion to exotic trees has also reduced the annual runoff in the Western Ghats. Conserving existing grassland and restoring invaded habitat is critical to reverse these losses. This research focused on identifying grassland restoration sites using satellite images with a high spatial resolution (RapidEye). We used an object-oriented Random Forest classification to map the area for grassland restoration. We have identified an area of 254 sq. km. as suitable for grassland restoration and an area of 362 sq. km. for grassland conservation and prevention from invasion by exotic tree species. For restoration, we recommend a cautious removal of young and isolated exotic trees at the invasion front and restoring grasslands, instead of removing dense stands of mature exotic trees. We find that areas with low fire frequency areas tend to be invaded, but areas invaded by exotic trees tend to burn hotter which may be harmful to grassland species and ecosystems. We assume that removing exotic tree species in the identified restoration sites and restoring the grassland will be helpful in recovering lost habitat and ensuring the viability of populations of indigenous and endemic species and increasing streamflow.

ecology↗

bioRxiv2019

Local extinction of a montane, endemic grassland bird driven by landscape change across its global distribution

ContextTropical montane habitats support high biodiversity, and are hotspots of endemism, with grasslands being integral components of many such landscapes. The montane grasslands of the Western Ghats have seen extensive land-use change over anthropogenic timescales. The factors influencing the ability of grassland-dependent species to persist in habitats experiencing loss and fragmentation, particularly in montane grasslands, are poorly known.\n\nObjectivesWe studied the relationship between the Nilgiri pipit Anthus nilghiriensis, a threatened endemic bird that typifies these montane grasslands, and its habitat, across most of its global distribution. We examined what habitat features make remnants viable habitat, which is necessary for effective management.\n\nMethodsWe conducted 663 surveys in 170 sites and used both single-season occupancy modelling and N-mixture modelling to account for processes influencing detection, presence, and abundance.\n\nResultsElevation had a positive influence on species presence, patch size had a moderate positive influence and patch isolation a moderate negative influence. Species abundance was positively influenced by elevation and characteristics related to habitat structure, and negatively influenced by the presence of invasive woody vegetation.\n\nConclusionsThe strong effect of elevation on the highly range-restricted Nilgiri pipit makes it vulnerable to, and an indicator of, climate change. This highly range-restricted species is locally extinct at several locations and persists at low densities in remnants of recent fragmentation, suggesting an extinction debt. Our findings indicate a need to control and reverse the spread of exotic woody invasives to preserve the grasslands themselves and the specialist species dependent upon them.

ecology↗