Comparison of four UAV georeferencing methods for environmental monitoring purposes focusing on the combined use with airborne and satellite remote sensing platforms

Altres ajuts: C.P. is a recipient of a FI-DGR scholarship grant (2016B_00410). X.P. is a recipient of an ICREA Academia Excellence in Research Grant ().This work is aimed at the environmental remote sensing community that uses UAV optical frame imagery in combination with airborne and satellite data. Taking into account the economic costs involved and the time investment, we evaluated the fit-for-purpose accuracy of four positioning methods of UAV-acquired imagery: 1) direct georeferencing using the onboard raw GNSS (GNSSNAV) data, 2) direct georeferencing using Post-Processed Kinematic single-frequency carrier-phase without in situ ground support (PPK1), 3) direct georeferencing using Post-Processed Kinematic double-frequency carrier-phase GNSS data with in situ ground support (PPK2), and 4) indirect georeferencing using Ground Control Points (GCP). We tested a multispectral sensor and an RGB sensor, onboard multicopter platforms. Orthophotomosaics at <0.05 m spatial resolution were generated with photogrammetric software. The UAV image absolute accuracy was evaluated according to the ASPRS standards, wherein we used a set of GCPs as reference coordinates, which we surveyed with a differential GNSS static receiver. The raw onboard GNSSNAV solution yielded horizontal (radial) accuracies of RMSEr≤1.062 m and vertical accuracies of RMSEz≤4.209 m; PPK1 solution gave decimetric accuracies of RMSEr≤0.256 m and RMSEz≤0.238 m; PPK2 solution, gave centimetric accuracies of RMSEr≤0.036 m and RMSEz≤0.036 m. These results were further improved by using the GCP solution, which yielded accuracies of RMSEr≤0.023 m and RMSEz≤0.030 m. GNSSNAV solution is a fast and low-cost option that is useful for UAV imagery in combination with remote sensing products, such as Sentinel-2 satellite data. PPK1, which can register UAV imagery with remote sensing products up to 0.25 m pixel size, as WorldView-like satellite imagery, airborne lidar or orthoimagery, has a higher economic cost than the GNSSNAV solution. PPK2 is an acceptable option for registering remote sensing products of up to 0.05 m pixel size, as with other UAV images. Moreover, PPK2 can obtain accuracies that are approximate to the usual UAV pixel size (e.g. co-register in multitemporal studies), but it is more expensive than PPK1. Although indirect georeferencing can obtain the highest accuracy, it is nevertheless a time-consuming task, particularly if many GCPs have to be placed. The paper also provides the approximate cost of each solution

SLIC SUPERPIXELS FOR OBJECT DELINEATION FROM UAV DATA

Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05&thinsp;m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64&thinsp;%. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping

Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images

This paper presents a semantic edge-aware multi-task neural network (SEANet) to obtain closed boundaries when delineating agricultural parcels from remote sensing images. It derives closed boundaries from remote sensing images and improves conventional semantic segmentation methods for the extraction of small and irregular agricultural parcels. SEANet integrates three correlated tasks: mask prediction, edge prediction, and distance map estimation. Related features learned from these tasks improve the generalizability of the network. We regard boundary extraction as an edge detection task and extract rich semantic edge features at multiple levels to improve the geometric accuracy of parcel delineation. Moreover, we develop a new multi-task loss that considers the uncertainty of different tasks. We conducted experiments on three high-resolution Gaofen-2 images in Shandong, Xinjiang, and Sichuan provinces, China, and on two medium-resolution Sentinel-2 images from Denmark and the Netherlands. Results showed that our method produced a better layout of agricultural parcels, with higher attribute and geometric accuracy than the existing ResUNet, ResUNet-a, R2UNet, and BsiNet methods on the Shandong and Denmark datasets. The total extraction errors of the parcels produced by our method were 0.214, 0.127, 0.176, 0.211, and 0.184 for the five datasets, respectively. Our method also obtains closed boundaries by one single segmentation, leading to superiority as compared with existing multi-task networks. We showed that it could be applied to images with different spatial resolutions for parcel delineation. Finally, our method trained on the Xinjiang dataset could be successfully transferred to the Shandong dataset with different dates and landscapes. Similarly, we obtained satisfactory results when transferring from the Denmark dataset to the Netherlands dataset. We conclude that SEANet is an accurate, robust, and transferable method for various areas and different remote sensing images. The codes of our model are available at https://github.com/long123524/SEANet_torch.</p

Crowd-Driven and Automated Mapping of Field Boundaries in Highly Fragmented Agricultural Landscapes of Ethiopia with Very High Spatial Resolution Imagery

Mapping the extent and location of field boundaries is critical to food security analysis but remains problematic in the Global South where such information is needed the most. The difficulty is due primarily to fragmentation in the landscape, small farm sizes, and irregular farm boundaries. Very high-resolution satellite imagery affords an opportunity to delineate such fields, but the challenge remains of determining such boundaries in a systematic and accurate way. In this paper, we compare a new crowd-driven manual digitization tool (Crop Land Extent) with two semi-automated methods (contour detection and multi-resolution segmentation) to determine farm boundaries from WorldView imagery in highly fragmented agricultural landscapes of Ethiopia. More than 7000 one square-kilometer image tiles were used for the analysis. The three methods were assessed using quantitative completeness and spatial correctness. Contour detection tended to under-segment when compared to manual digitization, resulting in better performance for larger (approaching 1 ha) sized fields. Multi-resolution segmentation on the other hand, tended to over-segment, resulting in better performance for small fields. Neither semi-automated method in their current realizations however are suitable for field boundary mapping in highly fragmented landscapes. Crowd-driven manual digitization is promising, but requires more oversight, quality control, and training than the current workflow could allow

The classification and mapping of Lapalala Wilderness Reserve, Limpopo

Dissertation (MSc (Plant Science))--University of Pretoria, 2021.Vegetation classifications and maps form the foundation of understanding spatial variation in vegetation and the environmental conditions driving the occurrence of plant assemblages and form a baseline for detecting changes in vegetation. They are considered an important tool for land-use planning and conservation of natural ecosystems, allowing managers to make informed decisions. The aim of this dissertation was to (1) create a vegetation classification for Lapalala Wilderness Nature Reserve, distinguishing major plant communities and correlated environmental factors, and (2) map the distribution of these plant communities across the study area. Lapalala Wilderness Nature Reserve spans 48 000 ha and is part of the Waterberg Bioregion in Limpopo, South Africa. The reserve plays an important part in conservation of both flora and fauna, and to support management and develop conservation strategies, the need for an updated vegetation map was recognised. One hundred and eighty 20 x 20 m relevés (comprised of 355 species) were sampled in January-March 2019 for this study. Canopy cover was estimated for all vascular plant species and environmental variables collected in the field include bare ground, rock cover, geographic location and elevation. Slope, aspect, curvature, topographic wetness index, topographic position index, distance to water, number of years since the last fire, and the number of fires in the last 10 years were determined for each relevé. Soil samples were analysed for phosphorus, sodium, calcium, potassium, magnesium, organic carbon and pH, and their particle size distribution was determined. The OptimClass method identified that the best data-analytical combination for this dataset was Relativized Manhattan dissimilarity index and group average clustering with 10 clusters and no data transformations.The identified communities were Community 1: Combretum molle-Schmidtia pappophoroides woodland, Community 2: Senegalia nigrescens-Heteropogon contortus woodland, Community 3: Terminalia sericea-Aristida diffusa woodland, Community 4: Burkea africana-Eragrostis gummiflua woodland, Community 5: Cynodon dactylon-Eragrostis patentipilosa grassland, Community 6: Grewia monticola-Vachellia nilotica woodland, Community 7: Euclea linearis shrubland, Community 8: Cymbopogon pospischilii grassland, Community 9: Vitex obovata-Phyllanthus parvulus shrubland, Community 10: Andropogon eucomus-Eragrostis heteromera grassland. Out of the 37 environmentals variables, 21 had a significant effect on the composition of communities, with many of these variables being related to soil texture (n = 10) and soil nutrient content (n = 7). CART was used to map the communities. However, mapping the study area was not very accurate due to weak relationships between satellite-derived variables and the occurrence of the communities, but estimates a heterogeneous mosaic of communities. Two communities were widely distributed across the study area, Community 1, comprising 66% of the mapped area, and Community 2 (26%), with small patches of Community 3 (3 %) and Community 5 (5 %). An accuracy assessment of the map showed an overall accuracy of 70 % and kappa index of 40%. In summary, there was no strong differentiation between the communities in terms of species composition or environmental variables, and, as a result, the plant communities do not represent clear management units. Due to a paucity of vegetation studies and landscape-scale vegetation maps in the Waterberg, this study provides an important step in developing a deeper understanding of the vegetation in this ecologically-important region.Mapula Trust, Lapalala Wilderness Nature ReservePlant ScienceMSc (Plant Science)Unrestricte

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information