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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.

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.

Soil is a fundamental natural resource that is vital to the sustainable development of human societies. However, in many developing countries, increased intensity of use and inadequate land use planning has put a lot of pressure on marginal soil, leading to various forms of land degradation. The purpose of this study is to generate an integrated the land cover and terrain classification of the Ban Dan Na Kham watershed of Northern Thailand as a tool for sustainable land use planning. The watershed boundary and slope classes were delineated using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). The slope was subsequently classified into gentle (8o), moderate (8-30o) and steep (30o). The land cover map was generated through the supervised classification of Sentinel2 satellite imagery. Both map products were then integrated to provide the basis for land allocation and land use planning. The results show that 58 % of land currently under arable farming is either marginally suitable or practically unsuitable for that purpose. This ultimately leads to increased land degradation and soil loss. The land should consequently be reforested. Nevertheless, up to 10 km2 of the watershed that is dedicated to other land use types – almost twice the current arable land area – is suitable for arable cropping. As such, given the proposed reforestation of the marginal and unsuitable arable lands, a large proportion of suitable land is still available to make up for the deficit. This will ultimately lead to increased productivity and reduced land degradation.

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.

• The point density of UAV point cloud increased with the increase of oblique camera angle. • The Quantitative Structure Modelling (QSM) did not behave well with UAV data. • UAV point cloud acquired in the leaf-off season could be used for accurate tree parameter extraction. 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. [ABSTRACT FROM AUTHOR]