Explore the vast range of titles available.

Agricultural intensification causes significant species loss in agricultural landscapes. One species particularly affected is the critically endangered common hamster (Cricetus cricetus). To counteract hamster decline, it is essential to analyze scale‐dependent factors that determine hamster occurrence and densities, especially in structurally simple landscapes. We mapped hamster burrows in the predominantly simple agricultural landscapes of Saxony‐Anhalt, Central Germany. At the local scale, we studied the effects of the hamster protection measure of high cut harvest and common vole abundance as well as satellite‐based vegetation indices. At the landscape scale, we studied the effects of landscape composition (percentage of winter cereals, oil seeds, uncultivated land, crop diversity) and configuration (edge density, mean field size, distance to the nearest forest, and urban fabric). Our results showed that hamster densities and vole abundance were negatively associated, whereas high cut harvest had a non‐significant but slightly positive effect on hamster densities. Satellite‐based vegetation indices showed no effect on hamster occurrence. At the landscape scale, the percentage of winter cereals around study field centers increased the probability of hamster occurrence, while further landscape indices had no effect, likely due to the already too simply structured landscape. Our study shows that at the local scale, attention should be paid to adapted vole pest management in order to ensure that hamsters are not further harmed. Whereas, high cut harvest as a single measure is not sufficient to stabilize hamster populations at the local scale, at the landscape scale other factors such as agri‐environmental schemes should be considered in simple landscapes to prevent the steady population decline. This study examined the effects of local‐ and landscape‐scale factors on the critically endangered common hamster (Cricetus cricetus) in one of Germany's largest remaining populations. At the local scale, we investigated the influence of a hamster protection measure, common vole abundance, as well as satellite‐based vegetation indices and at the landscape scale, we analyzed landscape composition and configuration. Results showed that hamster densities decreased with higher common vole abundance locally, while winter wheat increased the probability of hamster occurrence at the landscape level in spring.

Environmental remote sensing has faced increasing satellite data availability, advanced algorithms for thematic analysis, and novel concepts of ground truth. For that reason, contents and concepts of learning and teaching remote sensing are constantly evolving. This eventually leads to the intuition of methodologically linking academic learning assignments with case-related scopes of application. In order to render case-related learning possible, smart teaching and interactive learning contexts are appreciated and required for remote sensing. That is due to the fact that those contexts are considered promising to trigger and gradually foster students’ comprehensive interdisciplinary thinking. To this end, the following contribution introduces the case-related concept of applying simulation games as a promising didactic format in teaching/learning assignments of remote sensing. As to methodology, participating students have been invited to take on individual roles bound to technology-related profiles (e.g., satellite-mission planning, irrigation, etc.) Based on the scenario, stakeholder teams have been requested to elaborate, analyze and negotiate viable solutions for soil moisture monitoring in a defined context. Collaboration has been encouraged by providing the protected, specifically designed remoSSoil-incubator environment. This letter-type paper aims to introduce the simulation game technique in the context of remote sensing as a type of scholarly teaching; it evaluates learning outcomes by adopting certain techniques of scholarship of teaching and learning (SoTL); and it provides food for thought of replicating, adapting and enhancing simulation games as an innovative, disruptive next-generation learning environment in remote sensing.

One of the greatest challenges of our time is monitoring the rapid environmental changes taking place worldwide at both local and global scales. This requires easy-to-use and ready-to-implement tools and services to monitor and quantify aspects of bio- and geodiversity change and the impact of land use intensification using freely available and global remotely sensed data, and to derive remotely sensed indicators. Currently, there are no services for quantifying both raster- and vector-based indicators in a “compact tool”. Therefore, the main innovation of ESIS/Imalys is having a remote sensing (RS) tool that allows for RS data processing, data management, and continuous and discrete quantification and derivation of RS indicators in one tool. With the ESIS/Imalys project (Ecosystem Integrity Remote Sensing—Modelling and Service Tool), we try to present environmental indicators on a clearly defined and reproducible basis. The Imalys software library generates the RS indicators and remote sensing products defined for ESIS. This paper provides an overview of the functionality of the Imalys software library. An overview of the technical background of the implementation of the Imalys library, data formats and the user interfaces is given. Examples of RS-based indicators derived using the Imalys tool at pixel level and at zone level (vector level) are presented. Furthermore, the advantages and disadvantages of the Imalys tool are discussed in detail in order to better assess the value of Imalys for users and developers. The applicability of the indicators will be demonstrated through three ecological applications, namely: (1) monitoring landscape diversity, (2) monitoring landscape structure and landscape fragmentation, and (3) monitoring land use intensity and its impact on ecosystem functions. Despite the integration of large amounts of data, Imalys can run on any PC, as the processing and derivation of indicators has been greatly optimised. The Imalys source code is freely available and is hosted and maintained under an open source license. Complete documentation of all methods, functions and derived indicators can be found in the freely available Imalys manual. The user-friendliness of Imalys, despite the integration of a large amount of RS data, makes it another important tool for ecological research, modelling and application for the monitoring and derivation of ecosystem indicators from local to global scale.

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

The sustainable provision of ecological products and services, both natural and man-made, faces a substantial threat emanating from invasive plant species (IPS), which inflict considerable economic and ecological harm on a global scale. They are widely recognized as one of the primary drivers of global biodiversity decline and have become the focal point of an increasing number of studies. The integration of remote sensing (RS) and geographic information systems (GIS) plays a pivotal role in their detection and classification across a diverse range of research endeavors, emphasizing the critical significance of accounting for the phenological stages of the targeted species when endeavoring to accurately delineate their distribution and occurrences. This study is centered on this fundamental premise, as it endeavors to amass terrestrial data encompassing the phenological stages and spectral attributes of the specified IPS, with the overarching objective of ascertaining the most opportune time frames for their detection. Moreover, it involves the development and validation of a detection and classification algorithm, harnessing a diverse array of RS datasets, including satellite and unmanned aerial vehicle (UAV) imagery spanning the spectrum from RGB to multispectral and near-infrared (NIR). Taken together, our investigation underscores the advantages of employing an array of RS datasets in conjunction with the phenological stages, offering an economically efficient and adaptable solution for the detection and monitoring of invasive plant species. Such insights hold the potential to inform both present and future policymaking pertaining to the management of invasive species in agricultural and natural ecosystems.

In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.

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.

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.