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.
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.
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.
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.
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