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München, Germany

Jubanski J.,Remote Sensing Solutions GmbH | Ballhorn U.,Remote Sensing Solutions GmbH | Kronseder K.,Remote Sensing Solutions GmbH | Franke J.,Remote Sensing Solutions GmbH | And 2 more authors.
Biogeosciences | Year: 2013

Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R2 = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments. © Author(s) 2013. Source


Dlamini S.N.,Swiss Tropical and Public Health Institute | Dlamini S.N.,University of Basel | Franke J.,Remote Sensing Solutions GmbH | Vounatsou P.,Swiss Tropical and Public Health Institute | Vounatsou P.,University of Basel
Geospatial Health | Year: 2015

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites. © Copyright M. Eckardt et al. Source


Navratil P.,Remote Sensing Solutions GmbH | Wilps H.,In der Herget 20
Journal of Applied Remote Sensing | Year: 2013

Three different object-based image classification techniques are applied to high-resolution satellite data for the mapping of the habitats of Asian migratory locust (Locusta migratoria migratoria) in the southern Aral Sea basin, Uzbekistan. A set of panchromatic and multispectral Système Pour l'Observation de la Terre-5 satellite images was spectrally enhanced by normalized difference vegetation index and tasseled cap transformation and segmented into image objects, which were then classified by three different classification approaches: a rule-based hierarchical fuzzy threshold (HFT) classification method was compared to a supervised nearest neighbor classifier and classification tree analysis by the quick, unbiased, efficient statistical trees algorithm. Special emphasis was laid on the discrimination of locust feeding and breeding habitats due to the significance of this discrimination for practical locust control. Field data on vegetation and land cover, collected at the time of satellite image acquisition, was used to evaluate classification accuracy. The results show that a robust HFT classifier outperformed the two automated procedures by 13% overall accuracy. The classification method allowed a reliable discrimination of locust feeding and breeding habitats, which is of significant importance for the application of the resulting data for an economically and environmentally sound control of locust pests because exact spatial knowledge on the habitat types allows a more effective surveying and use of pesticides. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE). Source


Englhart S.,Ludwig Maximilians University of Munich | Englhart S.,Remote Sensing Solutions GmbH | Jubanski J.,Remote Sensing Solutions GmbH | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH
Remote Sensing | Year: 2013

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r2 = 0.77, n = 79) and 2011 (r2 = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+. © 2013 by the authors. Source


Wang X.,Wuhan University | Wang X.,Ludwig Maximilians University of Munich | Siegert F.,Ludwig Maximilians University of Munich | Siegert F.,Remote Sensing Solutions GmbH | And 2 more authors.
Global and Planetary Change | Year: 2013

The alpine ecosystem of the Western Nyainqentanglha region, located in the Central Tibetan Plateau, has experienced a lot of changes in the context of climatic change. The long data record of remote sensing data allowed us to evaluate spatio-temporal change in this remote area. The ecosystem changes of the Western Nyainqentanglha region were detected by using Landast MSS/TM/ETM+, Hexagon KH-9, Glas/ICESat, SRTM3 DEM remote sensing data and GIS techniques. The area of glacier lakes was delineated by visual interpretation, while for the inland lake by image classification. The change of glacier thickness was obtained by Glas/ICESat data of 2004 and 2008. Results show high variation in extent of glaciers and lakes with increased temperature and precipitation in the past 40years. These variations include glacial retreat, increased water level of inland lakes and increased number of glacier lakes to higher altitudes. Glaciers lost 22% of its coverage from 1977 to 2010, and the annual shrinkage rate accelerated in the last decade compared with the previous time period of 1977-2001. In average, the thickness of the monitored glaciers reduced by 4.48m from 2004 to 2008 with an annual rate of 1.12m. From 1972 to 2009, the number of new formed glacier lakes increased by 150 and the area of glacier lakes increased by 173% (4.53km2). At the same time, the surface area of the largest salt lake in Tibet expanded by 4.13% (80.18km2). These variations appear to be associated with an increase in mean annual temperature of 0.05°C per year, and an increase in annual precipitation of 1.83mm per year in the last four decades. By analyzing the relationship between the decreased glacier area and the increased number and extent of lakes in the vertical zones over the past 40years, there is a high correlation of 0.81. These results indicate that the climate change has great impacts on glaciers and glacier lakes on the central Tibetan Plateau. Further detailed investigations are required to understand the contribution of melting water and precipitation to the water cycle and the complicated hydrological relationship between the characteristics of glaciers and glacier lakes and climate warming in this alpine region. © 2013 Elsevier B.V. Source

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