The Opt4Environment project set out to evaluate how remote‑sensing techniques can support environmental monitoring for two specific themes: the modification of habitat types by global change, especially anthropogenic drivers, and the influence of continuous urbanisation on animal movements. The first work package focused on early detection of vitality loss in forest ecosystems. Researchers examined the potential of high‑spectral‑resolution EnMAP data combined with the high‑temporal‑resolution Sentinel‑2 and the ocean‑observation Sentinel‑3 sensors. A data‑fusion model was developed that merges hyperspectral and multispectral observations through wavelet‑based and pansharpening techniques, yielding a single dataset with enhanced spectral and spatial resolution. The fusion approach was validated by simulating multisensor time series and comparing the resulting spectra with laboratory reference spectra, confirming that the combined EnMAP‑Sentinel‑2 product improves the discrimination of habitat types and the assessment of their vitality. Temporal statistics were adapted to the specific monitoring objectives, and a pre‑disposition index was derived to flag early signs of habitat degradation. Change detection algorithms were then applied to the fused data, allowing the identification of non‑sustainable landscape development at fine spatial scales.

The second work package addressed the interaction between stork movements and urban environments. The study integrated a wide range of remote‑sensing products, including Sentinel‑1 SAR, Sentinel‑2 and Landsat multispectral imagery, MODIS, and the Global Urban Footprint derived from TerraSAR‑X and Tandem‑X. Atmospheric reanalysis data from ECMWF and NCEP were used to contextualise environmental conditions. Urban structure metrics such as city core size, edge structure, and population density were extracted from Copernicus Land Information and the Maryland Global Forest Change database. Stork tracking data from the MPIO were merged with these environmental layers to investigate how urban morphology influences movement patterns. A spatio‑temporal homogenisation procedure was applied to all datasets, followed by a sensitivity analysis that quantified the robustness of the derived relationships. The project also extended the methodology to include Sentinel‑1 data, enabling continuous monitoring of animal behaviour across heterogeneous landscapes. The resulting change‑modelling framework can be used to predict how future urban expansion may alter stork habitat suitability and movement corridors.

Throughout the project, a comprehensive database of environmental and biodiversity data was constructed, validated, and homogenised. The database supports the generation of surface reflectance composites and the derivation of remote‑sensing variables that feed into the habitat vitality and animal movement models. The project produced several scientific publications and interim reports, documenting the methodological advances and the empirical findings. The work was carried out from May 2015 to January 2018, with key milestones achieved in 2016, 2017, and 2018. The consortium comprised the Remote Sensing Chair at Julius‑Maximilians‑University Würzburg and the German Remote‑Sensing Data Centre at the German Aerospace Center (DLR). Coordination responsibilities lay with the Würzburg team, while administrative and scientific support was provided by the DLR project team in Bonn. Two half‑time scientist positions were funded for the duration of the project. The project built on earlier research outputs from both institutions, ensuring continuity and leveraging existing expertise in data fusion, temporal statistics, and ecological remote sensing. The outcomes of Opt4Environment demonstrate that integrated multisensor remote‑sensing, combined with robust statistical and modelling techniques, can deliver actionable insights into habitat vitality and the ecological impacts of urbanisation, thereby informing sustainable land‑use planning and biodiversity conservation.