Search

Additional file 1: Table S1 Mean living-tree biomass (above and belowground) for different forest categories and site conditions. Plots, where site conditions were not clearly identified, were excluded from the respective part of the analysis. The two lower and higher forest-size classes in the forest-dominated area were combined for the statistical analysis, because of the small dataset for forest edges. SE = standard error, n = number of plots. Underlined data are not representative because of insufficient size of the respective dataset. Fig. S1 NDVIm of forests in the study area. Arithmetic means of NDVI from seven Sentinel 2 satellite images (17.05.2018, 11.06.2018, 25.08.2018, 4.09.2018, 14.09.2018, 19.09.2017, 16.07.2016). The shaded relief illustration is based on TanDEM-X data.

Graphical 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.

Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests&rsquo, vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey&rsquo, s height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type.

Various approaches exist to model scattering of a vegetation canopy above ground in terms of optical and radar wavelengths. Due to the different scattering properties these two spectral regions are modelled separately for visible/ infrared bands and for microwave regions. The newly developed RadOptics model (RO-M) integrates these two spectral regions semi-physically into one radiative transfer (RT)-based model framework, resting on the law of Beer-Bougert-Lambert. Due to the integrative nature of RO-M, it can calculate/simulate the canopy and soil reflectances for the optical and radar spectrum using a single unified model architecture. By Applying RO-M in radar domain (ROR-M) it is shown that the observed dependence of Backscattering coefficient on Leaf Area Index (LAI), soil moisture content and frequency can be simulated consistently with results in literature. The results of the RO-M within the optical domain (ROO-M) present an equivalent trend of reflectance and band ratio values with LAI compared to studies in literature.

Ongoing increase in human and climate pressures, in addition to the lack of monitoring initiatives, makes the Caatinga one of the most vulnerable forests in the world. The Caatinga is located in the semi-arid region of Brazil and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, a time series stack of 670 Landsat images over a period of 31 years (1985–2015) was used to investigate spatial and temporal patterns of land-cover clearing (LCC) due to vegetation removal in an area of the Caatinga. We compared the LCC detection accuracy of three spectral indices, i.e., the surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We applied a residual trend analysis (TSS-RESTREND) to attenuate seasonal climate effects on the vegetation time series signal and to detect only significant structural changes (breakpoints) from monthly Landsat time series. Our results show that SA was able to identify the general occurrence of LCC and the year that it occurred with a higher accuracy (89 and 62%, respectively) compared to EVI (44 and 22%) and NDVI (46 and 22%). The overall outcome of the study shows the benefits of using Landsat time series and a spectral index that incorporates the short-wave infrared range, such as the SA, compared to visible and near-infrared vegetation indices for monitoring LCC in seasonally dry forests such as the Caatinga.

Ongoing increases in human and climate pressures associated with the lack of monitoring initiatives make the Caatinga one of the most vulnerable biomes in the world. The Caatinga is located in the semi-arid region of Brazil, and its vegetation phenology is highly dependent on precipitation, which has a high spatial and temporal variability. Under these circumstances, satellite image-based methods are valued due to their ability to uncover human-induced changes from climate effects on land cover. In this study, 670 continuous Landsat images over a period of 31 years (1985–2015) were analysed to investigate spatial and temporal patterns of land-cover change (LCC) due to vegetation clearing in an area of the Caatinga biome. We compared the performance of surface albedo (SA), the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) to evaluate their suitability for monitoring LCC driven by human actions in contrast to precipitation-related variations. We applied a residual trend analysis, with detection of significant breakpoints (TSS-RESTREND), to a monthly Landsat time series. Our results show that SA was able to identify the year of land-cover clearing with a higher accuracy (83%) than that of EVI (20%) and NDVI (34%). The overall outcome of the study shows the benefits of using different spectral bands instead of greenness indices of Landsat time series for the monitoring of LCC, as a result of environmental land surface processes in seasonal dry forests such as the Caatinga.

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., Open-Access-Publikationsfonds 2018

The fertile alluvial plain of Cilicia is bordered by the Taurus and Amanus mountain ranges to the west, north and east and the Mediterranean Sea to the south. Since the Neolithic Period, Plain Cilicia was an important interface between Anatolia and the Levant. The alluvial plain is dominated by three rivers: Tarsus, Seyhan and Ceyhan. The avulsion history of the lower course of the rivers Seyhan and Ceyhan during the Holocene remains an unresolved issue. The knowledge about how former river courses have changed is essential for the identification of ancient toponyms with archaeological sites. The analysis of silted up riverbeds based on high-resolution digital elevation models (TanDEM-X) and historic satellite imagery (CORONA) in this paper provide the first indications for the reconstruction of former river channels. Further evidence is given by the evaluation of the settlement patterns from 3rd to 1st millennium BC.&nbsp

The spatio-temporal variability of trends in vegetation greenness in dryland areas is a well-documented phenomenon in remote sensing studies at global to regional scales. The underlying causes differ, however, and are often not well understood. Here, we analyzed the trends in vegetation greenness for a semi-arid area in northeastern Brazil (NEB) and examined the relationships between those dynamics and climate anomalies, namely the El Nino Southern Oscillation (ENSO) for the period 1982 to 2010, based on annual Normalized Difference Vegetation Index (NDVI) values from the latest version of the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI dataset (NDVI3g) dataset. Against the ample assumption of ecological and socio-economic research, the results of our inter-annual trend analysis of NDVI and precipitation indicate large areas of significant greening in the observation period. The spatial extent and strength of greening is a function of the prevalent land-cover type or biome in the study area. The regression analysis of ENSO indicators and NDVI anomalies reveals a close relation of ENSO warm events and periods of reduced vegetation greenness, with a temporal lag of 12 months. The spatial patterns of this relation vary in space and time. Thus, not every ENSO warm event is reflected in negative NDVI anomalies. Xeric shrublands (Caatinga) are more sensitive to ENSO teleconnections than other biomes in the study area., Open-Access-Publikationsfonds 2014

Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time- and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators. peerReviewed

Hintergrund: Die „homogene“, flächendeckende und wohnortnahe medizinische Versorgung der Bevölkerung ist eines der übergeordneten Ziele des Gesetzgebers. Der Versorgungsgrad (Bedarfsplanungs-Richtlinie des Gemeinsamen Bundesausschuss) kann zur Beschreibung der regionalen[zum vollständigen Text gelangen Sie über die oben angegebene URL], HEC 2016: Health – Exploring Complexity; Joint Conference of GMDS, DGEpi, IEA-EEF, EFMI

Anthropogenic stress and disturbance of forest ecosystems (FES) has been increasing at all scales from local to global. In rapidly changing environments, in-situ terrestrial FES monitoring approaches have made tremendous progress but they are intensive and often integrate subjective indicators for forest health (FH). Remote sensing (RS) bridges the gaps of these limitations, by monitoring indicators of FH on different spatio-temporal scales, and in a cost-effective, rapid, repetitive and objective manner. In this paper, we provide an overview of the definitions of FH, discussing the drivers, processes, stress and adaptation mechanisms of forest plants, and how we can observe FH with RS. We introduce the concept of spectral traits (ST) and spectral trait variations (STV) in the context of FH monitoring and discuss the prospects, limitations and constraints. Stress, disturbances and resource limitations can cause changes in FES taxonomic, structural and functional diversity; we provide examples how the ST/STV approach can be used for monitoring these FES characteristics. We show that RS based assessments of FH indicators using the ST/STV approach is a competent, affordable, repetitive and objective technique for monitoring. Even though the possibilities for observing the taxonomic diversity of animal species is limited with RS, the taxonomy of forest tree species can be recorded with RS, even though its accuracy is subject to certain constraints. RS has proved successful for monitoring the impacts from stress on structural and functional diversity. In particular, it has proven to be very suitable for recording the short-term dynamics of stress on FH, which cannot be cost-effectively recorded using in-situ methods. This paper gives an overview of the ST/STV approach, whereas the second paper of this series concentrates on discussing in-situ terrestrial monitoring, in-situ RS approaches and RS sensors and techniques for measuring ST/STV for FH. peerReviewed

Satellite remote sensing provides a powerful instrument for mapping and monitoring traces of historical settlements and infrastructure, not only in distant areas and crisis regions. It helps archaeologists to embed their findings from field surveys into the broader context of the landscape. With the start of the TanDEM-X mission, spatially explicit 3D-information is available to researchers at an unprecedented resolution worldwide. We examined different experimental TanDEM-X digital elevation models (DEM) that were processed from two different imaging modes (Stripmap/High Resolution Spotlight) using the operational alternating bistatic acquisition mode. The quality and accuracy of the experimental DEM products was compared to other available DEM products and a high precision archaeological field survey. The results indicate the potential of TanDEM-X Stripmap (SM) data for mapping surface elements at regional scale. For the alluvial plain of Cilicia, a suspected palaeochannel could be reconstructed. At the local scale, DEM products from TanDEM-X High Resolution Spotlight (HS) mode were processed at 2 m spatial resolution using a merge of two monostatic/bistatic interferograms. The absolute and relative vertical accuracy of the outcome meet the specification of high resolution elevation data (HRE) standards from the National System for Geospatial Intelligence (NSG) at the HRE20 level. Open-Access-Publikationsfonds 2014 peerReviewed

The present study is based on the assumption that vegetation in Indonesia is significantly affected by climate anomalies that are related to El Nin˜ o–Southern Oscillation (ENSO) warm phases (El Nin˜ o) during the past decades. The analysis builds upon a monthly time series from the normalized difference vegetation index (NDVI) gridded data from the Advanced Very High Resolution Radiometer (AVHRR) and two ENSO proxies, namely, sea surface temperature anomalies (SSTa) and Southern Oscillation index (SOI), and aims at the analysis of the spatially explicit dimension of ENSO impact on vegetation on the Indonesian archipelago. A time series correlation analysis between NDVI anomalies and ENSO proxies for the most recent ENSO warm events (1982–2006) showed that, in general, anomalies in vegetation productivity over Indonesia can be related to an anomalous increase of SST in the eastern equatorial Pacific and to decreases in SOI, respectively. The net effect of these variations is a significant decrease in NDVI values throughout the affected areas during the ENSO warm phases. The 1982/83 ENSO warm episode was rather short but—in terms of ENSO indices—the most extreme one within the study period. The 1997/98 El Nin˜o lasted longer but was weaker. Both events had significant impact on vegetation in terms of negative NDVI anomalies. Compared to these two major warm events, the other investigated events (1987/88, 1991/92, 1994/95, and 2002/03) had no significant effect on vegetation in the investigated region. The land cover–type specific sensitivity of vegetation to ENSO anomalies revealed thresholds of vegetation response to ENSO warm events. The results for the 1997/98 ENSO warm event confirm the hypothesis that the vulnerability of vegetated tropical land surfaces to drought conditions is considerably affected by land use intensity. In particular, it could be shown that natural forest areas are more resistant to drought stress than degraded forest areas or cropland. Comparing the spatially explicit patterns of El Nin˜ o–related vegetation variation during the major El Nin˜o phases, the spatial distribution of affected areas reveals distinct core regions ofENSOdrought impact on vegetation for Indonesia that coincide with forest conversion and agricultural intensification hot spots. peerReviewed