Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

DFG, 357874777, FOR 2694: Large-Scale and High-Resolution Mapping of Soil Moisture on Field and Catchment Scales - Boosted by Cosmic-Ray NeutronsDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial requirement for preventing further spread to so far unaffected regions. An established approach to map invasive species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the invasive shrub species Ulex europaeus (common gorse) on Chilo´e Island (south-central Chile), based on ultrahigh- resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition dates for estimating the actual distribution of Ulex europaeus. In Step 2 we translate the challenging co-registration task between two datasets with very different spatial resolutions into an (machine learning) optimization problem where the UAV-based land cover classification maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest (RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is identified to correct the spatial offset between both datasets. Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use cases where individual target species that are visually detectable in UAV imagery are considered. These findings confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species

Combining remote sensing, habitat suitability models and cellular automata to model the spread of the invasive shrub Ulex europaeus

Modeling the past or future spread patterns of invasive plant species is challenging and in an ideal case requires multi-temporal and spatially explicit data on the occurrences of the target species as well as information on the habitat suitability of the areas at risk of being invaded. Most studies either focus on modeling the habitat suitability of a given area for an invasive species or try to model the spreading behavior of an invasive species based on temporally or spatially limited occurrence data and some environmental variables. Here we suggest a workflow that combines habitat suitability maps, occurrence data from multiple time steps collected from remote sensing data, and cellular automata models to first reconstruct the spreading patterns of the invasive shrub Ulex europaeus on the island Chiloé in Chile and then make predictions for the future spread of the species. First, U. europaeus occurrences are derived for four time steps between 1988 and 2020 using remote sensing data and a supervised classification. The resulting occurrence data is combined with occurrence data of the native range of U. europaeus from the GBIF database and selected environmental variables to derive habitat suitability maps using Maxent. Then, cellular automata models are calibrated using the occurrence estimates of the four time steps, the suitability map, and some additional geo-layer containing information about soils and human infrastructure. Finally, a set of calibrated cellular automata models are used to predict the potential spread of U. europaeus for the years 2070 and 2100 using climate scenarios. All individual steps of the workflow where reference data was available led to sufficient results (supervised classifications Overall Accuracy > 0.97; Maxent AUC > 0.85; cellular automata Balanced Accuracy > 0.91) and the spatial patterns of the derived maps matched the experiences collected during the field surveys. Our model predictions suggest a continuous expansion of the maximal potential range of U. europaeus, particularly in the Eastern and Northern part of Chiloé Island. We deem the suggested workflow to be a good solution to combine the static habitat suitability information—representing the environmental constraints—with a temporally and spatially dynamic model representing the actual spreading behavior of the invasive species. The obtained understanding of spreading patterns and the information on areas identified to have a high invasion probability in the future can support land managers to plan prevention and mitigation measures

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany

Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b)