@article{RemelgadoSafiWegmann2020, author = {Remelgado, Ruben and Safi, Kamran and Wegmann, Martin}, title = {From ecology to remote sensing: using animals to map land cover}, series = {Remote Sensing in Ecology and Conservation}, volume = {6}, journal = {Remote Sensing in Ecology and Conservation}, number = {1}, doi = {10.1002/rse2.126}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225200}, pages = {93-104}, year = {2020}, abstract = {Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.}, language = {en} } @article{LatifiHeurich2019, author = {Latifi, Hooman and Heurich, Marco}, title = {Multi-scale remote sensing-assisted forest inventory: a glimpse of the state-of-the-art and future prospects}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {11}, issn = {2072-4292}, doi = {10.3390/rs11111260}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197358}, year = {2019}, abstract = {Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue "Remote Sensing-Based Forest Inventories from Landscape to Global Scale", which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.}, language = {en} } @article{LatifiHolzwarthSkidmoreetal.2021, author = {Latifi, Hooman and Holzwarth, Stefanie and Skidmore, Andrew and Brůna, Josef and Červenka, Jaroslav and Darvishzadeh, Roshanak and Hais, Martin and Heiden, Uta and Homolov{\´a}, Lucie and Krzystek, Peter and Schneider, Thomas and Star{\´y}, Martin and Wang, Tiejun and M{\"u}ller, J{\"o}rg and Heurich, Marco}, title = {A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The 'Data pool initiative for the Bohemian Forest Ecosystem'}, series = {Methods in Ecology and Evolution}, volume = {12}, journal = {Methods in Ecology and Evolution}, number = {11}, doi = {10.1111/2041-210X.13695}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262743}, pages = {2073-2083}, year = {2021}, abstract = {Effects of climate change-induced events on forest ecosystem dynamics of composition, function and structure call for increased long-term, interdisciplinary and integrated research on biodiversity indicators, in particular within strictly protected areas with extensive non-intervention zones. The long-established concept of forest supersites generally relies on long-term funds from national agencies and goes beyond the logistic and financial capabilities of state- or region-wide protected area administrations, universities and research institutes. We introduce the concept of data pools as a smaller-scale, user-driven and reasonable alternative to co-develop remote sensing and forest ecosystem science to validated products, biodiversity indicators and management plans. We demonstrate this concept with the Bohemian Forest Ecosystem Data Pool, which has been established as an interdisciplinary, international data pool within the strictly protected Bavarian Forest and Šumava National Parks and currently comprises 10 active partners. We demonstrate how the structure and impact of the data pool differs from comparable cases. We assessed the international influence and visibility of the data pool with the help of a systematic literature search and a brief analysis of the results. Results primarily suggest an increase in the impact and visibility of published material during the life span of the data pool, with highest visibilities achieved by research conducted on leaf traits, vegetation phenology and 3D-based forest inventory. We conclude that the data pool results in an efficient contribution to the concept of global biodiversity observatory by evolving towards a training platform, functioning as a pool of data and algorithms, directly communicating with management for implementation and providing test fields for feasibility studies on earth observation missions.}, language = {en} } @article{BaeMuellerFoersteretal.2022, author = {Bae, Soyeon and M{\"u}ller, J{\"o}rg and F{\"o}rster, Bernhard and Hilmers, Torben and Hochrein, Sophia and Jacobs, Martin and Leroy, Benjamin M. L. and Pretzsch, Hans and Weisser, Wolfgang W. and Mitesser, Oliver}, title = {Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data}, series = {Methods in Ecology and Evolution}, volume = {13}, journal = {Methods in Ecology and Evolution}, number = {1}, doi = {10.1111/2041-210X.13726}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258222}, pages = {121-132}, year = {2022}, abstract = {Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. In a unique replicated field experiment manipulating gypsy moth Lymantria dispar densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ = 0.79 and 0.84, respectively). The ΔNCDII\(_{Defoliation(A-C)}\) significantly explained caterpillar abundance (R\(^{2}\) = 0.52). The NCDI at critical timesteps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.}, language = {en} } @article{RichardAbdelRahmanSubramanianetal.2017, author = {Richard, Kyalo and Abdel-Rahman, Elfatih M. and Subramanian, Sevgan and Nyasani, Johnson O. and Thiel, Michael and Jozani, Hosein and Borgemeister, Christian and Landmann, Tobias}, title = {Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya}, series = {Sensors}, volume = {17}, journal = {Sensors}, number = {11}, doi = {10.3390/s17112537}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173285}, year = {2017}, abstract = {Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93\% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85\%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.}, language = {en} } @article{KnauerGessnerFensholtetal.2017, author = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Forkuor, Gerald and Kuenzer, Claudia}, title = {Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment}, series = {Remote Sensing}, volume = {9}, journal = {Remote Sensing}, number = {2}, doi = {10.3390/rs9020132}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-171905}, year = {2017}, abstract = {Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92\% (2001), 91\% (2007), and 91\% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91\% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future.}, language = {en} } @article{LauschBorgBumbergeretal.2018, author = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.}, title = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches}, series = {Remote Sensing}, volume = {10}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs10071120}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691}, pages = {1120}, year = {2018}, abstract = {Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.}, language = {en} } @article{UereyenKuenzer2019, author = {Uereyen, Soner and Kuenzer, Claudia}, title = {A review of earth observation-based analyses for major river basins}, series = {Remote Sensing}, volume = {11}, journal = {Remote Sensing}, number = {24}, issn = {2072-4292}, doi = {10.3390/rs11242951}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193849}, pages = {2951}, year = {2019}, abstract = {Regardless of political boundaries, river basins are a functional unit of the Earth's land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream-downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.}, language = {en} } @article{WalzWegmannDechetal.2015, author = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Raso, Giovanna and Utzinger, J{\"u}rg}, title = {Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook}, series = {Parasites \& Vectors}, volume = {8}, journal = {Parasites \& Vectors}, number = {163}, doi = {10.1186/s13071-015-0732-6}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148778}, year = {2015}, abstract = {Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.}, language = {en} } @article{WalzWegmannDechetal.2015, author = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Vounastou, Penelope and Poda, Jean-Noel and N'Goran, Eli{\´e}zer K. and Raso, Giovanna and Utzinger, J{\"u}rg}, title = {Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing}, series = {PLoS Neglected Tropical Diseases}, volume = {9}, journal = {PLoS Neglected Tropical Diseases}, number = {11}, doi = {10.1371/journal.pntd.0004217}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125845}, pages = {e0004217}, year = {2015}, abstract = {Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in C{\^o}te d'Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of C{\^o}te d'Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.}, language = {en} }