The use of landscape system approach in predicting the distribution of ecotopes : Samaria biosphere reserve, Crete, Greece = Een landschap-systeem benadering voor het voorspellen van ecotopen in het Samaria biosphere reservaat (Kreta, Griekenland) [Landschap 20 jaar jong, TYLE]

The scope of this study was to utilize methods widely used in soil survey and land evaluation in the ecologically important mountainous region (Lefka Ori, West Crete

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

The use of landscape system approach in predicting the distribution of ecotopes : Samaria biosphere reserve, Crete, Greece = Een landschap-systeem benadering voor het voorspellen van ecotopen in het Samaria biosphere reservaat (Kreta, Griekenland) [Landschap 20 jaar jong, TYLE]

The scope of this study was to utilize methods widely used in soil survey and land evaluation in the ecologically important mountainous region (Lefka Ori, West Crete

Prediction of leaf area index using thermal infrared data acquired by UAS over a mixed temperate forest

The leaf area index (LAI) is a crucial biophysical variable for remote sensing vegetation studies. LAI estimation through remote sensing data has mostly been investigated using visible and near-infrared (0.4–1.3 μm, VNIR) and Shortwave Infrared (1.4–3 μm, SWIR) data. However, Thermal Infrared (3–14 μm, TIR) data for LAI retrieval has rarely been explored. This study aims to predict LAI by integrating VNIR and TIR data from Unmanned Aerial Systems (UAS) in a mixed temperate forest, the Haagse Bos, Enschede, the Netherlands. The VNIR and TIR images were acquired in September 2020, in conjunction with fieldwork to collect LAI in situ data for 30 plots. TIR images were acquired at two heights (i.e., 85 m and 120 m above ground) to investigate the effect of flight height on the LAI prediction accuracy by means of UAS data. Land Surface Temperature (LST) and Land Surface Emissivity (LSE) were computed and extracted from the collected images. LAI was estimated using seven vegetation indices and Partial Least Squares Regression (PLSR). LAI prediction accuracy using VNIR reflectance spectra was compared to the accuracy achieved by integrating VNIR data with LST or LSE applying vegetation indices as well as PLSR. Among the applied vegetation indices, the Reduced Simple Ratio (RSR) gained the highest prediction accuracy using VNIR data (R2 = 0.5815, RMSE = 0.6972); the prediction accuracy was not improved when LST was integrated with VNIR data but increased when LSE was included (RSR: R2 = 0.7458, RMSE = 0.5081). However, when LST from 85 m altitude and VNIR data was applied as an input using PLSR (R2 = 0.5565, RMSECV = 0.7998), the LAI prediction accuracy was slightly increased compared to when only VNIR data was used (R2 = 0.4452, RMSECV = 0.8668). Integrating VNIR data with LSE significantly improved the LAI retrieval accuracy (R2 = 0.7907, RMSECV = 0.8351). These findings corroborate prior research indicating that combining LSE with VNIR data can increase the prediction accuracy of LAI. However, LST was found to be ineffective for this purpose. The relationship between LAI and LSE should be the subject of more investigation through various approaches to bridge the existing scientific gap

Analysing the potential of UAV point cloud as input in quantitative structure modelling for assessment of woody biomass of single trees

Accurate tree metrics is essential for forest management. Quantitative Structure Model (QSM) which can reconstruct an accurate 3D model of trees, has been used with Terrestrial Laser Scanning (TLS) point cloud as input. Indeed, image-based Structure from Motion (SfM) can produce point cloud as well. Unmanned Aerial Vehicle (UAV), which can collect images of a large scale in a short period, seems like a good choice for forest study. This study aims to investigate the feasibility of UAV point cloud for QSM of individual trees. Flights were carried out during the leaf-on and leaf-off seasons with an inclined camera onboard. Four oblique camera angles were used during the leaf-on season to obtain the optimal angle for UAV data collection. The Diameter at Breast Height (DBH) derived from UAV point cloud and QSM were compared with field measured data. The accuracy of QSM-biomass estimations was assessed with reference, which was calculated using field measured DBH through the allometry. In this study, it was found that the point density of the whole scene increased with the increase of oblique camera angle. DBH extracted from the UAV-generated point cloud versus reference showed no significant difference (p > 0.05), while a significant difference was found between QSM-estimated DBH and the reference DBH. The QSM-based biomass showed 49.16% underestimation for leaf-off season. Although the QSM did not behave well with UAV data, it was found that the UAV point cloud could be used for accurate tree parameter extraction and could be a useful tool for forest management

Identification of potential rockfall sources using UAV-derived point cloud

Recent advances in remote sensing techniques and computer algorithms allow accurate, abundant, and high-resolution geometric information retrieval for rock mass characterization from 3D point clouds. The automatic application of the extracted information for local scale rockfall susceptibility assessment, where discontinuities characteristics play a major role in rocky slope stability, requires step by step logical procedures. This paper presents a novel methodology to use the extracted discontinuity set characteristics for a local scale rockfall susceptibility assessment, tailored for Uncrewed Aerial Vehicle (UAV) data acquisition. The method consists of 4 steps: (i) 3D slope model reconstruction using UAV digital photogrammetry, (ii) automatic characterization of discontinuity sets, (iii) slope stability analysis, and (iv) susceptibility assessment using a new Rockfall Susceptibility Index. The proposed method was applied to a road cut rocky slope in a mountainous area of the Samaria National Park, in Crete Island, Greece. Visual validation indicates that the areas of higher and moderate rockfall susceptibility on the 3D model of the rocky slope are adjacent to rockfall source areas marked by the presence of fallen blocks on the foot of the slope. The proposed methodological workflow presents novelties related to the use of point clouds for the estimation of the Rock Quality Designation (RQD) index, the visualization of discontinuity set spacing, the evaluation of the persistence and the Slope Mass Rating (SMR) index, as well as the incorporation of the persistence of overhangs into the rockfall susceptibility assessment and visualization

The Greek pilot project: Lefka Ori National Park, Crete

This chapter presents the range of activities undertaken for the Lefka Ori National Park in Crete, Creece within the frame of INNOVA project which aimed at achieving the following outcomes: -Evaluation of protected area management effectiveness -Introduction to the concept of sustainability in protected area management -Developing of a sustainability monitoring strategy for Lefka Ori -Increase of stakeholder and public awareness, regarding the protected areas threats and values -Enable stakeholder and local community engagement in protected area managemen

Combining unmanned aerial vehicle and multispectral Pleiades data for tree species identification, a prerequisite for accurate carbon estimation

Forest carbon estimation currently largely relies on remote sensing techniques in combination with field measurement. High-resolution images, which are commonly utilized for carbon estimation, are not readily available, and their cost prohibits communities from reaping the benefits of maintaining their forest under the UN reducing emissions from deforestation and forest degradation program. Our study explores the combination of readily available and relatively cheaper unmanned aerial vehicle (UAV) (4-cm resolution) and multispectral Pleiades (50-cm resolution) images for species classification robustness in view for carbon estimation through object-based image analysis. The images are resampled and used to evaluate the effect of combining multispectral Pleiades image on the accuracies of segmenting UAV images for tree crown projection area (CPA) estimation and species classification. RGB images from a UAV platform are processed in a photogrametric software and combined with the near-infrared band of a Pleiades image to get a UAV-Pleiades image composite. The images are segmented using the ESP 2 tool and the segmentation accuracy compared using a paired t-test. The segmented tree crowns are classified using random trees (RT), support vector machines (SVM), and maximum likelihood (ML) classifiers, and the classification accuracies of the three classifiers are compared using the McNemar's chi-squared test. Our study demonstrates a 93.5% accuracy of segmenting UAV-Pleiades image composite, which is significantly higher than the 84.8% accuracy of segmenting UAV images (p < 0.05). Also an 84% classification accuracy of UAV-Pleiades image composite is significantly higher than the 54% classification accuracy of the UAV images (p < 0.05). Of the three classifiers used, the classification accuracies of SVM and RT are significantly higher (p < 0.05) than that of the ML classifier. Given the significantly high accuracies observed from this study for tree CPA extraction and tree species classification, carbon/above ground biomass modeling is possible with significantly high accuracy using the combination of multispectral Pleiades and UAV images

Altitudinal Vascular Plant Richness and Climate Change in the Alpine Zone of the Lefka Ori, Crete

High mountain zones in the Mediterranean area are considered more vulnerable in comparison to lower altitudes zones. Lefka Ori massif, a global biodiversity hotspot on the island of Crete is part of the Global Observation Research Initiative in Alpine Environments (GLORIA) monitoring network. The paper examines species and vegetation changes with respect to climate and altitude over a seven-year period (2001–2008) at a range of spatial scales (10 m Summit Area Section-SAS, 5 m SAS, 1 m2) using the GLORIA protocol in a re-survey of four mountain summits (1664 m–2339 m). The absolute species loss between 2001–2008 was 4, among which were 2 endemics. At the scale of individual summits, the highest changes were recorded at the lower summits with absolute species loss 4 in both cases. Paired t-tests for the total species richness at 1 m2 between 2001–2008, showed no significant differences. No significant differences were found at the individual summit level neither at the 5 m SAS or the 10 m SAS. Time series analysis reveals that soil mean annual temperature is increasing at all summits. Linear regressions with the climatic variables show a positive effect on species richness at the 5 m and 10 m SAS as well as species changes at the 5 m SAS. In particular, June mean temperature has the highest predictive power for species changes at the 5 m SAS. Recorded changes in species richness point more towards fluctuations within a plant community’s normal range, although there seem to be more significant diversity changes in higher summits related to aspects. Our work provides additional evidence to assess the effects of climate change on plant diversity in Mediterranean mountains and particularly those of islands which remain understudied