MSc handed in on impact of remote sensing for biodiversity monitoring

MSc handed in on impact of remote sensing for biodiversity monitoring

figure23_mangrove_degradation_AnnaStephanieAnna Stephanie handed in her MSc thesis on “Impact of remote sensing characteristics for biodiversity monitoring”. Very impressive study on multi-scale, multi-model and multi-variable analysis of mangroves in Myanmar.

While Myanmar is one of the world’s hotspots for biodiversity and endemism, it is currently undergoing enormous political and economic transformations which are likely to increase the pressure on its already endangered forest ecosystems. In this context, mangrove forests are of particular relevance, as they are not only among the Earth’s most imperiled tropical environments, but also provide numerous ecosystem services to humanity. To ensure an ecologically worthwhile management of mangrove ecosystems, it is necessary that inventories are undertaken on a regular basis. Remote sensing offers a cost efficient and rapid method to periodically monitor mangrove forests. However, the current availability of various sensor types and different classification methods complicateswell-informed selection of the most appropriate methodology for an effective biodiversity monitoring. In order to assist applied ecologists in this highly complex decision-making process, this study compared the suitability of medium-resolution Landsat 8 and high-resolution RapidEye imagery to accurately monitor mangrove forests. Spatial and spectral resolution, classification algorithms and different predictor combinations were investigated as influencing elements. A multi-scale classification approach was developed to account for the fact that biodiversity monitoring for conservation is typically conducted on numerous spatial scales ranging from local to global perspectives. By formulating recommendation for practitioners, this study’s aim was to bridge the gap between research and its implementation in applied conservation. Results of the analysis showed that medium-resolution Landsat 8 imagery mostly leads to higher classification accuracies than high-resolution RapidEye data in the context of mangrove mapping in Southern Myanmar. The comparison of different predictor combinations suggested, that this difference is mainly attributable to the additional spectral bands provided by the Landsat 8 sensor. By investigating RapidEye images with spatial resolutions of 5 – 30 meters, it was discovered that overall classification accuracies increased with coarser spatial resolution regarding the majority of land cover classes. Moreover, the accuracy of land cover predictions was strongly influenced by the choice of specific classification algorithms as well as the number and characteristics of predictor layers. Referring to the main findings of this study, the application of medium-resolution Landsat 8 data is recommended to applied conservationists. This is based on its superior performance in most of the classifications as well as on its cost-free availability.
a

MSc by Andrea Hess on deforestation drivers in Myanmar handed in

MSc by Andrea Hess on deforestation drivers in Myanmar handed in

Andrea_hess_landcover_MyanmarThe MSc by Andrea Hess “Deforestation in Myanmar – what can we say about causes?” has been handed in. It has been supervised by Peter Leimgruber (Smithsonian Conservation Biology Institute) and Martin Wegmann. Deforestation in the tropics is a global issue. Tropical forests are important not only for local livelihoods, but also for global climate regulation and biodiversity. Causes of forest loss have been extensively assessed on larger scales, but information on national and sub-
national level is often limited. In Myanmar, spatially explicit information on drivers of deforestation is almost non-existent. In this study I analyzed deforestation and factors affecting it in Myanmar, with focus on deforestation hotspots. I calculated change statistics from a Myanmar wide satellite imagery based forest cover change classification for deforestation on country level, the three states/regions Kachin, Sagaing and Tanintharyi and Homalin township. Using GAM, GLM and Random Forest models, I assessed the importance of eight factors in driving deforestation in Homalin township, namely elevation, slope, and distance to roads, waterways, populated places, protected areas, and reserved forests. To delineate areas under future deforestation risk, I also performed spatially explicit deforestation predictions. The results show a regionally distinct influence of mining in Kachin and Sagaing and plantation expansion in Tanintharyi on deforestation. In Homalin township, deforestation was strongly influenced by elevation, slope, distance to streams and populated places. The risk for  deforestation was predicted highest in low areas in proximity to waterways and towns and villages.

MSc handed in “Deforestation in Myanmar – what can we say about causes?”

MSc handed in “Deforestation in Myanmar – what can we say about causes?”

Andrea_Hess_MSc_Myanmar_deforestation_patternAndreas Hess, Global Change Ecology MSc student, just handed in her MSc “Deforestation in Myanmar – what can we say about causes?”. The MSc was conducted jointly with the Smithsonian Conservation Biology Institute (Peter Leimgruber) and Ned Horning and fieldwork was conducted in Myanmar with a local NGO.

Deforestation in the tropics is a global issue. Tropical forests are important not only for local livelihoods, but also for global climate regulation and biodiversity. Causes of forest loss have been extensively assessed on larger scales, but information on national and sub-national level is often limited. In Myanmar, spatially explicit information on drivers of deforestation is almost non-existent.

In this study I analyzed deforestation and factors affecting it in Myanmar, with focus on deforestation hotspots. I calculated change statistics from a Myanmar wide satellite imagery based forest cover change classification for deforestation on country level, the three states/regions Kachin, Sagaing and Tanintharyi and Homalin township. Using GAM, GLM and Random Forest models, I assessed the importance of eight factors in driving deforestation in Homalin township, namely elevation, slope, and distance to roads, waterways, populated places, protected areas, and reserved forests. To delineate areas under future deforestation risk, I also performed spatially explicit deforestation predictions.

The results show a regionally distinct influence of mining in Kachin and Sagaing and plantation expansion in Tanintharyi on deforestation. In Homalin township, deforestation was strongly influenced by elevation, slope, distance to streams and populated places. The risk for deforestation was predicted highest in low areas in proximity to waterways and towns and villages.