Erasmi, Stefan

[["dc.bibliographiccitation.firstpage","1319"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Biogeosciences"],["dc.bibliographiccitation.lastpage","1333"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Klinge, Michael"],["dc.contributor.author","Dulamsuren, Choimaa"],["dc.contributor.author","Erasmi, Stefan"],["dc.contributor.author","Karger, Dirk Nikolaus"],["dc.contributor.author","Hauck, Markus"],["dc.date.accessioned","2019-07-09T11:45:23Z"],["dc.date.accessioned","2020-05-11T13:15:36Z"],["dc.date.available","2019-07-09T11:45:23Z"],["dc.date.available","2020-05-11T13:15:36Z"],["dc.date.issued","2018"],["dc.description.abstract","In northern Mongolia, at the southern boundary of the Siberian boreal forest belt, the distribution of steppe and forest is generally linked to climate and topography, making this region highly sensitive to climate change and human impact. Detailed investigations on the limiting parameters of forest and steppe in different biomes provide necessary information for paleoenvironmental reconstruction and prognosis of potential landscape change. In this study, remote sensing data and gridded climate data were analyzed in order to identify main distribution patterns of forest and steppe in Mongolia and to detect environmental factors driving forest development. Forest distribution and vegetation vitality derived from the normalized differentiated vegetation index (NDVI) were investigated for the three types of boreal forest present in Mongolia (taiga, subtaiga and forest–steppe), which cover a total area of 73 818 km2. In addition to the forest type areas, the analysis focused on subunits of forest and nonforested areas at the upper and lower treeline, which represent ecological borders between vegetation types. Climate and NDVI data were analyzed for a reference period of 15 years from 1999 to 2013. The presented approach for treeline delineation by identifying representative sites mostly bridges local forest disturbances like fire or tree cutting. Moreover, this procedure provides a valuable tool to distinguish the potential forested area. The upper treeline generally rises from 1800 m above sea level (a.s.l.) in the northeast to 2700 m a.s.l. in the south. The lower treeline locally emerges at 1000 m a.s.l. in the northern taiga and rises southward to 2500 m a.s.l. The latitudinal gradient of both treelines turns into a longitudinal one on the eastern flank of mountain ranges due to higher aridity caused by rain-shadow effects. Less productive trees in terms of NDVI were identified at both the upper and lower treeline in relation to the respective total boreal forest type area. The mean growing season temperature (MGST) of 7.9–8.9 °C and a minimum MGST of 6 °C are limiting parameters at the upper treeline but are negligible for the lower treeline. The minimum of the mean annual precipitation (MAP) of 230–290 mm yr−1 is a limiting parameter at the lower treeline but also at the upper treeline in the forest–steppe ecotone. In general, NDVI and MAP are lower in grassland, and MGST is higher compared to the corresponding boreal forest. One exception occurs at the upper treeline of the subtaiga and taiga, where the alpine vegetation consists of mountain meadow mixed with shrubs. The relation between NDVI and climate data corroborates that more precipitation and higher temperatures generally lead to higher greenness in all ecological subunits. MGST is positively correlated with MAP of the total area of forest–steppe, but this correlation turns negative in the taiga. The limiting factor in the forest–steppe is the relative humidity and in the taiga it is the snow cover distribution. The subtaiga represents an ecological transition zone of approximately 300 mm yr−1 precipitation, which occurs independently from the MGST. Since the treelines are mainly determined by climatic parameters, the rapid climate change in inner Asia will lead to a spatial relocation of tree communities, treelines and boreal forest types. However, a direct deduction of future tree vitality, forest composition and biomass trends from the recent relationships between NDVI and climate parameters is challenging. Besides human impact, it must consider bio- and geoecological issues like, for example, tree rejuvenation, temporal lag of climate adaptation and disappearing permafrost."],["dc.identifier.doi","10.5194/bg-15-1319-2018"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15188"],["dc.identifier.scopus","2-s2.0-85043494931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65009"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59218"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-85043494931&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.relation.issn","1726-4189"],["dc.subject.ddc","550"],["dc.title","Climate effects on vegetation vitality at the treeline of boreal forests of Mongolia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1161"],["dc.bibliographiccitation.firstpage","1161"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","de Souza Mendes, Flávia"],["dc.contributor.author","Baron, Daniel"],["dc.contributor.author","Gerold, Gerhard"],["dc.contributor.author","Liesenberg, Veraldo"],["dc.contributor.author","Erasmi, Stefan"],["dc.date.accessioned","2019-07-09T11:51:29Z"],["dc.date.accessioned","2020-05-11T13:22:28Z"],["dc.date.available","2019-07-09T11:51:29Z"],["dc.date.available","2020-05-11T13:22:28Z"],["dc.date.issued","2019"],["dc.description.abstract","Mapping vegetation types through remote sensing images has proved to be e ective, especially in large biomes, such as the Brazilian Cerrado, which plays an important role in the context of management and conservation at the agricultural frontier of the Amazon. We tested several combinations of optical and radar images to identify the four dominant vegetation types that are prevalent in the Cerrado area (i.e., cerrado denso, cerradão, gallery forest, and secondary forest). We extracted features from both sources of data such as intensity, grey level co-occurrence matrix, coherence, and polarimetric decompositions using Sentinel 2A, Sentinel 1A, ALOS-PALSAR 2 dual/full polarimetric, and TanDEM-X images during the dry and rainy season of 2017. In order to normalize the analysis of these features, we used principal component analysis and subsequently applied the Random Forest algorithm to evaluate the classification of vegetation types. During the dry season, the overall accuracy ranged from 48 to 83%, and during the dry and rainy seasons it ranged from 41 up to 82%. The classification using Sentinel 2A images during the dry season resulted in the highest overall accuracy and kappa values, followed by the classification that used images from all sensors during the dry and rainy season. Optical images during the dry season were su cient to map the di erent types of vegetation in our study area."],["dc.identifier.doi","10.3390/rs11101161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16135"],["dc.identifier.scopus","2-s2.0-85066761706"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59955"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65039"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-85066761706&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.issn","2072-4292"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","Optical and SAR Remote Sensing Synergism for Mapping Vegetation Types in the Endangered Cerrado/Amazon Ecotone of Nova Mutum—Mato Grosso"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","284"],["dc.bibliographiccitation.journal","MethodsX"],["dc.bibliographiccitation.lastpage","299"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Nketia, Kwabena Abrefa"],["dc.contributor.author","Asabere, Stephen Boahen"],["dc.contributor.author","Erasmi, Stefan"],["dc.contributor.author","Sauer, Daniela"],["dc.date.accessioned","2019-07-09T11:50:02Z"],["dc.date.accessioned","2020-05-11T13:20:38Z"],["dc.date.available","2019-07-09T11:50:02Z"],["dc.date.available","2020-05-11T13:20:38Z"],["dc.date.issued","2019"],["dc.description.abstract","Analysing spatial patterns of soil properties in a landscape requires a sampling strategy that adequately covers soil toposequences. In this context, we developed a hybrid methodology that couples global weighted principal component analysis (GWPCA) and cost-constrained conditioned Latin hypercube algorithm (cLHC). This methodology produce an optimized sampling stratification by analysing the local variability of the soil property, and the influence of environmental factors. The methodology captures the maximum local variances in the global auxiliary dataset with the GWPCA, and optimizes the selection of representative sampling locations for sampling with the cLHC. The methodology also suppresses the subsampling of auxiliary datasets from areas that are less representative of the soil property of interest. Consequently, the method stratifies the geographical space of interest in order to adequately represent the soil property. We present results on the tested method (R2 = 0.90 and RMSE = 0.18 m) from the Guinea savannah zone of Ghana. •It defines the local structure and accounts for localized spatial autocorrelation in explaining variability.•It suppresses the occurrence of model-selected sampling locations in areas that are less representative of the soil property of interest."],["dc.identifier.doi","10.1016/j.mex.2019.02.005"],["dc.identifier.pmid","30815367"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15840"],["dc.identifier.scopus","2-s2.0-85061396843"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59685"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/65033"],["dc.identifier.url","http://www.scopus.com/inward/record.url?eid=2-s2.0-85061396843&partnerID=MN8TOARS"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2215-0161"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","550"],["dc.title","A new method for selecting sites for soil sampling, coupling global weighted principal component analysis and a cost-constrained conditioned Latin hypercube algorithm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]