1,721,073 research outputs found
Artificial Intelligence for a Multi-temporal Classification of Fluvial Geomorphic Units of the River Isonzo: A Comparison of Different Techniques
No full textThe pressure of human activities is particularly relevant on fluvial ecosystems, where activity such hydroelectric energy production can change natural dynamics. For this reason it is important to monitor, with a systematic approach, river geomorphic units distribution and their evolution over time. In particular this work consists of an application of different AI techniques to process Sentinel-2 optical data to acquire a multitemporal classification of fluvial geomorphic units (Channels, Pools, Bars, Island, Vegetation) on a study area of the river Isonzo in Friuli Venezia Giulia (Italy). Results showed that all the AI methods tested allow to perform accurate classification, with best results obtained by Random Forest, that reach an overall accuracy of 0.986, and the most confusion between Bars and Island classes with F-measure of 0.931 and 0.961 respectively
Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought conditions
Full text linkIn this work the relationship between climatic indices and seasonal vertical ground motion (SVGM) from earth observation data is investigated. The European Ground Motion Service (EGMS) vertical ground movement measurements provided from 2018 to 2022 are used together with temperature and precipitation data from MODIS and CHIRP datasets respectively. Precipitation and temperature are further processed to provide Drought Code (DC) maps calculated ad hoc for this study at 1 km spatial resolution and daily temporal resolution. Measurement Points (MP) from EGMS over Italy provide a value of ground vertical movement approximately every 6 days. Seasonality is analysed to assess correlation between SVGM and DC from Copernicus CEMS (DC1) and from MODIS+CHIRP (DC2) and from temperature using Spearman's rank correlation coefficient (ρ). Initial results over Italy show that DC2 is significantly better correlated to SVGM than DC1 and temperature, with a stronger median absolute value of ρ of 0.025 and 0.042 respectively for negative and positive correlation scenarios. A total of 1275 MPs have correlation coefficients between DC2 map and EGMS measurements above 0.8 (positive correlation) and 2594 ρ<-0.8 (negative correlation). Correlations lagged in time are also analysed, resulting in most being inside a window of +/- 6 days. Because DC and temperature are strongly collinear, further analysis to assess which is better at explaining the seasonality of GM was carried out, resulting in DC2 significantly explaining more variance of the SVGM than temperature for the inversely correlated points more than the directly correlated points. These points are unevenly distributed in the Italian territory, with clusters in some areas that appear to show reliable SVGM-DC correlations. We conclude that further investigation is necessary at a local scale. An interactive web-gis open to the public is available for data consultation and all data are shared in a public repository for full replicability of the method
A COMPARATIVE ASSESSMENT OF LAND USE-LAND COVER DYNAMICS BETWEEN BANGLADESH AND INDIAN SUNDARBANS FROM 1975-2020: A GEOSPATIAL AND STATISTICAL-BASED APPROACH
Full text linkEnhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management
Full text linkPlanning harvesting operations in forest environment: Remote sensing for decision support
Full text linkThe goal of this work is to assess a method for supporting decisions regarding identification of most suitable areas for two types of harvesting approaches in forestry: skyline vs. forwarder. The innovative aspect consists in simulating the choices done during the planning in forestry operations. To do so, remote sensing data from an aerial laser scanner were used to create a digital terrain model (DTM) of ground surface under vegetation cover. Features extracted from the DTM are used as input for several machine learning predictors. Features are slope, distance from nearest roadside, relative height from nearest roadside and roughness index. Training and validation is done using areas defined by experts in the study area. Results show a K value of almost 0.92 for the classifier with best results, random forest. Sensibility of each feature is assessed, showing that both distance and height difference from nearest road-side are more significant than overall DTM value
ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS
Full text linkEstimating Economic and Livelihood Values of the World’s Largest Mangrove Forest (Sundarbans): A Meta-Analysis
Full text linkWe explored the state of the art economic and livelihood valuation of ecosystem services (ES) in the Sundarbans mangroves, including a comparative analysis between the Bangladesh and Indian parts of the region. We identified 145 values from 26 studies to estimate the Sundarbans' economic and livelihood values. The number of ES valuation studies of the Sundarbans is scant, and it has gradually increased over time, focusing mainly on the estimation of provisioning ES (66.2%), followed by regulating and maintenance (25.5%), and cultural (8.3%) ES. However, recently, attention has been paid to estimation, regulating and maintenance, and cultural ES. The number of studies on ES was higher for the Bangladesh (73%) part of the Sundarbans than the Indian (27%) one. The estimated economic values of the Sundarbans' provisioning, regulating and maintenance, and cultural ES were US 2584.46 ha-1 yr-1, and US $ 151.88 ha-1 yr-1, respectively. Except for cultural ES, the identified values for the other two ES categories were about 1.5 to 2.5 times higher for the Bangladesh Sundarbans compared to the Indian ones. The results of the meta-regression model showed that the estimated economic and livelihood values of ES are affected by the associated variables (e.g., type of ES, valuation methods, study area, population, and GDP). Our study also identified some remarkable gaps and limitations in the economic and livelihood valuation of the ES of the Sundarbans, highlighting the need for further research to find out the values of all ES to help with policy decision-making
Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events
Full text linkThis study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km(2) in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R-2 = 0.324 to 0.438 -p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered
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