DSM and DTM generation from VHR satellite stereo imagery over plastic covered greenhouse areas

Agriculture under Plastic Covered Greenhouses (PCG) has represented a step forward in the evolution from traditional to industrial farming. However, PCG-based agricultural model has been also criticized for its associated environmental impact such as plastic waste, visual impact, soil pollution, biodiversity degradation and local runoff alteration. In this sense, timely and effective PCG mapping is the only way to help policy-makers in the definition of plans dealing with the trade-off between farmers’ profit and environmental impact for the remaining inhabitants. This work proposes a methodological pipeline for producing high added value 3D geospatial products (Digital Surface Models (DSM) and Digital Terrain Models (DTM)) from VHR satellite imagery over PCG areas. The 3D information layer provided through the devised approach could be very valuable as a complement to the traditional 2D spectral information offered by VHR satellite imagery to improve PCG mapping over large areas. This methodological approach has been tested in Almeria (Southern Spain) from a WorldView-2 VHR satellite stereo-pair. Once grid spacing format DSM and DTM were built, their vertical accuracy was assessed by means of lidar data provided by the Spanish Government (PNOA Programme). Regarding DSM completeness results, the image matching method based on hierarchical semi-global matching yielded much better scores (98.87%) than the traditional image matching method based on area-based matching and cross-correlation threshold (86.65%) when they were tested on the study area with the highest concentration of PCG (around 85.65% of PCG land cover). However, both image matching methods yielded similar vertical accuracy results in relation to the finally interpolated DSM, with mean errors ranging from 0.01 to 0.35m and random errors (standard deviation) between 0.56 and 0.82 m. The DTM error figures also showed no significant differences between both image matching methods, although being highly dependent on DSM-to- DTM filtering error, in turn closely related to greenhouse density and terrain complexity

Comparison of GEOBIA classification algorithms based on Worldview-3 imagery in the extraction of coastal coniferous forest

Šume primorskih četinjača, sa svojom ekološkom, ekonomskom, estetskom i društvenom funkcijom, predstavljaju važan dio europskih šumskih zajednica. Osnovni cilj ovoga rada je usporediti najkorištenije GEOBIA (engl. Geographic Object-Based Image Analysis) klasifikacijske algoritme (engl. Random Trees – RT, Maximum Likelihood – ML, Support Vector Machine – SVM) s ciljem izdvajanja šuma primorskih četinjača na visoko-rezolucijskom WorldView-3 snimku unutar topografskog slijevnog područja naselja Split. Metodološki okvir istraživanja uključuje (1) izvođenje izoštrenog multispektralnog snimka (WV-3MS-a); (2) testiranje segmentacijskih korisničko-definiranih parametara; (3) dodavanje testnih uzoraka; (4) klasifikaciju segmentiranog modela; (5) procjenu točnosti klasifikacijskih algoritama, te (6) procjenu točnosti završnog modela. RT se prema korištenim pokazateljima (correctness – COR, completeness – COM i overall quality – OQ) pokazao kao najbolji algoritam. Iterativno postavljanje segmentacijskih parametara omogućilo je detekciju najprikladnijih vrijednosti za generiranje segmentacijskog modela. Utvrđeno je da sjene mogu uzrokovati značajne probleme ako se klasificiranje vrši na visoko-rezolucijskim snimkama. Modificiranim Cohen’s kappa coefficient (K) pokazateljem izračunata je točnost konačnog modela od 87,38%. WV-3MS se može smatrati kvalitetnim podatkom za detekciju šuma primorskih četinjača primjenom GEOBIA metode.With their ecological, economic, aesthetic, and social function, coniferous forests represent an important part of European forest communities. The main objective of this paper is to compare the most used GEOBIA (Geographic Object-Based Image Analysis) classification algorithms (Random Trees - RT, Maximum Likelihood - ML, Support Vector Machine - SVM) for the purposes of the coastal coniferous forest detection on a high-resolution WorldView-3 (WV-3) imagery on the topographic basin of the Split settlement (Figure 1). The methodological framework (Figure 2) includes: (1) derivation of a sharpened multispectral image (WV-3MS) (Figure 3); (2) testing of the user-defined parameters in segmentation process (Figure 4); (3) marking of test samples (signatures); (4) classification of a segmented model; (5) accuracy assessment of the classification algorithms, and (6) accuracy assessment of the final model. The developed ACP tool (Automated Classification Process) (Supplement figure 5) for speeding up the entire classification process, enabled the simultaneous generation of output results for three selected classification algorithms (RT, ML and SVM) (Figure 6). Metric indicators (correctness - COR, completeness - COM, and overall quality - OQ) have shown that RT is the most accurate classification algorithm for the coastal coniferous forest detection (Table 1; Figure 7). The iterative setting of segmentation parameters enabled the detection of the most optimal values &8203;&8203;for generating a segmentation model. It is found that shadows can cause significant problems if classification is done on high-resolution images (Figure 8). The solution may be to collect a larger number of samples in different areas for the purpose of more detailed class differentiation. The modified Cohen’s kappa coefficient (K) indicator shown the accuracy of the final model of 87.38% (Table 2; Figure 9). WV-3MS can be considered as very good data for the detection of coniferous forests using the GEOBIA method (Figure 10). According to this research, 31.36% of the Split topographic basin is covered by highly and extremely flammable vegetation

The UK saltmarsh elevations and Ecosystem Services

Saltmarshes are ecologically important and provide diverse ecosystem services, including protection of shorelines, carbon sequestration, and wildlife habitat provision. Assessments of saltmarsh ecosystem services often consider them homogeneous, ignoring differences within and between marshes. This research examined the extent to which saltmarsh elevations and local tidal levels altered estimates of ecosystem service provision for 35 UK saltmarshes. LiDAR derived Digital Terrain Models (DTMs) gave reliable estimates of sediment surface elevation with an accuracy of better than 20cm. Most of the area of marshes in the South of the UK, from the Tees Estuary on the East Coast moving clockwise to the Dovey Estuary on the West coast lies at or below the level of MHWS, often with a sub-horizontal platform lying just below MHWS. These marshes make a substantial contribution to coastal protection during normal conditions by dissipating wave energy, but their contribution in reducing flooding risks is less important during storm surge events when they may be submerged to a depth of more than 2m. In the northern UK, substantial areas of marsh occur above the level of MHWS, potentially playing a bigger role in dissipating wave energy during storm surges, but the areas of land vulnerable to coastal flooding are much smaller here. The proportion of low marsh is small at most sites in the UK. The majority of saltmarsh area is predicted to have relatively high redox values and emissions of the greenhouse gases methane and nitrous oxide will therefore be low. Rapid sedimentation occurs mainly on low marshes, and rates of carbon burial will be overestimated if this is not taken into account. Mechanisms of sedimentation and vegetation succession appear to be variable and unstable, whether across the whole saltmarsh or in different parts in an individual marsh