Retrieval of Vegetation Biochemicals Using a Radiative Transfer Model and Hyperspectral data

Accurate quantitative estimation of vegetation biochemical characteristics is necessary for a large variety of agricultural and ecological applications. The advent of hyperspectral remote sensing has offered possibilities for measuring specific vegetation variables that were difficult to measure using conventional multi-spectral sensors. In this study, the potential of biophysical modelling to predict leaf and canopy chlorophyll contents in a heterogeneous grassland is investigated. The well-known PROSAIL model was inverted with HyMap measurements by means of a look-up table (LUT). HyMap images along with simultaneous in situ measurements of chlorophyll content were acquired over a National Park. We tested the impact of using multiple solutions and spectral sub-setting on parameter retrieval. To assess the performance of the model inversion, the RMSE and R2 between independent in situ measurements and estimated parameters were used. The results of the study demonstrated that inversion of the PROSAIL model yield higher accuracies for Canopy chlorophyll content, in comparison to Leaf chlorophyll content (R2=0.84, RMSE=0.24). Further a careful selection of spectral subset, which comprised the development of a new method to subset the spectral data, proved to contain sufficient information for a successful model inversion. Consequently, it increased the estimation accuracy of investigated parameters (R2=0.87, RMSE=0.22). Our results confirm the potential of model inversion for estimating vegetation biochemical parameters using hyperspectral measurements.JRC.DG.G.3-Monitoring agricultural resource

Laboratory for Essential Biodiversity Variables (EBV) Concepts – The “Data Pool Initiative for the Bohemian Forest Ecosystem”

Forest ecosystems respond very sensitively to climate and atmospheric changes. Feedback mechanisms can be measured via changes in albedo, energy balance and carbon storage. The Bavarian Forest National Park is a unique forest ecosystem with large non-intervention zones, which promote a large scale re-wilding process with low human interference. It provides important ecosystem services of clear water, carbon sequestration and recreation, and has fragile habitats with endangered forest species. The national park is therefore a very suitable field of research to study natural and near natural ecosystem processes. Under the leadership of the national park authority, experts from various European research institutions have joined forces to systematically establish a remote sensing data pool on the Bavarian Forest as a resource for their research. This collaborative effort provides an opportunity to combine various methodological approaches and data and to optimize products by sharing knowledge and expertise. The first objective of the data pool is to develop methods for the establishment of Essential Biodiversity Variables (EBV) based on a very sound and comprehensive data base. The recent advances in tighter collaboration of remote sensing and biodiversity science, especially with regard to the newly established EBV and RS-EBV concepts will help to improve the interdisciplinary research. However, such concepts and especially the underlying remote sensing data need to be developed, adapted and validated against biodiversity patterns. Such process needs an extensive set of in-situ and remotely sensed data in order to allow a thorough analysis. The Bavarian data pool fits these requirements through the commitment of all members and hence provides a variety of remote sensing data sets such as hyperspectral, Lidar as well as CIR and multispectral data, as well as a wealth of in-situ data of zoological and botanical transects. This combination allows setting sensor-specific, as well as species-specific analysis on different aspects, i.e. different processes between managed and natural forest, impact of climate change or species distribution mapping. The second objective is to develop concepts for EBV using Sentinel mission data combined with data from future contributing hyperspectral missions such as EnMAP. Spaceborne hyperspectral data has been identified by the remote sensing related biodiversity community as an important data source. However, the acquisition of airborne data is very expensive for regular coverage of forest stands and the entire forest ecosystem. This drawback will be overcome by the launch of the space-borne imaging spectroscopy mission EnMAP. It is a contributing mission to the Copernicus program and will be launched in 2018. EnMAP is expected to provide high quality imaging spectroscopy data on an operational basis and will be suitable for the retrieval of high resolution plant traits at local scales. First studies within the data pool have been focused on e.g. derivation of plant traits like chlorophyll, LAI and nitrogen and tree species classification with a special focus on rare species within the national park, just to name a few. Objective, purpose and content of the data pool will be shown as well as first selective developments

A laboratory for conceiving Essential Biodiversity Variables (EBVs)—The ‘Data pool initiative for the Bohemian Forest Ecosystem’

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.publishedVersio

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales