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With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model's ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model's capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model's sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance. [ABSTRACT FROM AUTHOR]

Accurate and non-destructive estimation of leaf chlorophyll content (LCC) is crucial for optimizing cotton production. This study enhances the SCOPE model by integrating unmanned aerial vehicle (UAV)-derived multispectral data with leaf area index (LAI) from LiDAR data, significantly improving precision of LCC estimation, particularly during crucial growth stages of cotton. We construct and analyze three cost functions: COST1, which relies solely on spectral data; COST2, which incorporates direct LAI inputs; and COST3, which adjusts for LAI measurement uncertainties by combining spectral term with an error term representing the squared relative error between measured and model-estimated LAI. Our findings indicate that while COST1 establishes a baseline, COST2 and COST3 provide more accurate LCC estimations. COST3, validated against theoretical data, field-measured cotton datasets, and an additional maize dataset, proves most robust, maintaining consistent accuracy across all growth stages especially when considering input data uncertainties. This highlights the importance of integrating appropriate forms of LAI in cost functions to refine LCC estimation. Future research should focus on improving data acquisition quality and developing more advanced cost functions to advance LCC estimation further.

Land use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study used Landsat images from 1986, 2006 and 2020 and the Random Forest classification method to analyze past LULCCs in these two watersheds. The results revealed: In Bafing, vegetation, settlement, agricultural areas and water increased, while the bareground decreased significantly between 1986-2020. In Faleme, two periods have different trends. Between 1986-2006, vegetation, settlement, agricultural areas and water increased, while bareground decreased. Between 2006-2020, settlement increased, while vegetation, agricultural areas, water and bareground decreased. To predict LULCCs in 2050 under business-as-usual assumptions, the Multilayer Perceptron and Marcov Chain model (MLP-MC) was used. The MLP-MC shows better results on Bafing than on Faleme but without questioning its application on the two watersheds. Bafing has seen a trend towards "more people, more trees", while Faleme has seen a trend towards "more people, more deforestation". These results contribute to develop appropriate land management policies and strategies to achieve or to maintain sustainable development in the SRB. [ABSTRACT FROM AUTHOR]

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance.

ABSTRACTLand use and Land cover change (LULCC) is a major global problem, and projecting change is critical for policy decision-making. Understanding LULCCs at the watershed level is essential for transboundary river basin management. The present study aims to analyse the past and future LULCCs in two significant watersheds of the Senegal River basin (SRB) in West Africa: Bafing and Faleme. This study used Landsat images from 1986, 2006 and 2020 and the Random Forest classification method to analyze past LULCCs in these two watersheds. The results revealed: In Bafing, vegetation, settlement, agricultural areas and water increased, while the bareground decreased significantly between 1986-2020. In Faleme, two periods have different trends. Between 1986-2006, vegetation, settlement, agricultural areas and water increased, while bareground decreased. Between 2006-2020, settlement increased, while vegetation, agricultural areas, water and bareground decreased. To predict LULCCs in 2050 under business-as-usual assumptions, the Multilayer Perceptron and Marcov Chain model (MLP-MC) was used. The MLP-MC shows better results on Bafing than on Faleme but without questioning its application on the two watersheds. Bafing has seen a trend towards ”more people, more trees”, while Faleme has seen a trend towards ”more people, more deforestation”. These results contribute to develop appropriate land management policies and strategies to achieve or to maintain sustainable development in the SRB.

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Early detection of rockslides at high-elevation and well-vegetated slopes remains challenging. This study used satellite and unmanned aerial vehicle (UAV) optical remote-sensing (ORS) images to track evidence of slope deformation and examine potential geomorphological precursors of five large rockslides in China. The multi-temporal image interpretation results were combined with available pre-sliding slope displacement data derived from synthetic aperture radar (SAR) or field monitoring to study the temporal changes in geomorphological precursors accompanying slope deformation. All the surveyed landslides had cracks or scarps and rockfalls within the landslide source area before the onset of rapid sliding. These precursors can be identified in ORS images taken several years or decades before the rapid slope failure, which provides sufficient time for the landslide early detection in practice. Local topography affects the spatial locations of cracks or scarps. Rockfalls within the landslide source area tend to locate at "key blocks" where slope mass provides forces resisting sliding. The rockfall area ratio, defined as the accumulated area of rockfalls over the landslide source area, ranged from 0.33 to 0.92 before rapid slope failure. The landslides developed on anti-dip and igneous rock slopes show a more significant rise of rockfall area ratio before the slope failure than the landslides on dip slopes. Given the broad availability of ORS data, this study could shed light on the ORS-based landslide early detection and landslide kinematics study. [ABSTRACT FROM AUTHOR]

Faced with the recent crises of water capture and distribution, managing water resources is a practice that has been gaining prominence in discussions and debates about environmental issues. Thus, this article aims to analyze, in a spatio-temporal way, the Changes in Land Use and Occupation (LULC) and Surface Temperature (LST) in the water catchment sub-basin of Ribeirão Santa Isabel, located in the municipality of Paracatu, Minas Gerais (MG), between the years 1990 and 2020. The choice of the area is justified by the conflicts of use that encompass the subbasin and by the fact that this is the only source of capture in the municipality, which has already carried out the alteration of the water resource responsible for the capture of water due to the intensity of agricultural practices in the region in the previous area. Through geoprocessing and Remote Sensing (RS) techniques, images from the Landsat-5 and Landsat-8 satellites were used to perform the LST calculations. Regarding usage and occupancy data, the images provided by the Mapbiomas platform were used. It was verified, through the increase in the percentage of the water class and vegetation and the decrease in the agricultural class, that the practices implemented in the study area, in order to conserve the remnants of the biome and maintain a good water availability to the municipality, are having an effect. It was also possible to conclude that LST is affected by LULC, and that the greatest variations occur in non-vegetated areas. [ABSTRACT FROM AUTHOR]

We present a detailed survey on the ongoing destabilization process of the Mosul dam. The dam is located on the Tigris river and is the biggest hydraulic structure in Iraq. From a geological point of view the dam foundation is unstable due to the underlying geology that is formed by alternate and variable strata of highly soluble materials such as gypsum, anhydrite, marl and limestone. Here we present the first comprehensive multi-sensor cumulative deformation map for the dam generated from space-based synthetic aperture radar (SAR) measurements from the Italian constellation COSMO-SkyMed and the European Sentinel-1a satellite. We compared 2014-2016 data to an historic dataset spanning 2004-2010 acquired with the Envisat ASAR sensor. We found that deformation was rapid during 2004-2010, slowed down in 2012-2014, and restarted in August 2014 when grouting operations stopped due to the temporary capture of the dam by the self proclaimed Islamic State in Iraq and Syria (ISIS). We took advantage of the availability of data from multiple SAR satellites to infer the deformation at the dam in great spatial and temporal detail and shed new light on the processes of the ongoing destabilization. This study highlights how new constellations of SAR sensors together with the availability of historical datasets are leading to important advances in deformation monitoring of small scale geologic and manmade features.

An optimized land management and the planning requires updating information on the land use changes over time. The application of remote sensing capabilities and land use change modeler (LCM) may provide a suitable platform for simulating land use changes. The LCM, land use changes in the upstream area of the Siahrud River Basin were evaluated in the 1977-2015 period and simulated for 2030. The land use maps of 1977, 1988, 1998, 2009 and 2015 were prepared, using the unsupervised and supervised classification methods of the Landsat satellite images, and the LCM, for the 1977-1988, 1988-1998 and 1998-2009 periods were implemented in the form of three models. After evaluation the performance of these models, the most appropriate one was selected. Using the data for the 2009-2015 periods a prediction was made for the year 2030. The results indicate that the use of appropriate inputs for the LCM, the application of the combined methods in classification, and utilizing expert knowledge, can be effective in improving the classification results and increasing the efficiency of the LCM. (Kappa coefficient 0.82 to 0.91). Predicting land use changes in the Siahroud River Basin during the 1977 to 2030 period indicates a 67% decrease in paddy fields and 12% decrease in forest area and a 34-fold increase in the extent of the mixed agricultural and orchards, and threefold increase in the extent of the residential areas.

The installation of hydroelectric plants has generated multiple environmental impacts on the world's river systems. In central Chile, the impacts of hydroelectric reservoir operation have been documented in ecological and hydrologic regime terms. This investigation assesses the changes in channel morphology, vegetation distribution, and flows in the middle section of the Maule River during the period following the start-up of a hydroelectric plant. Changes in fluvial morphology (active area) and land cover are quantified using LANDSAT images, contrasted with a vegetation sampling and flow analysis. The results show a 12% decrease in active areas of the river, indicating a loss of geomorphological diversity. Within the active channel, there was a gradual increase in plant-covered surface area, which reached 159% between 1989 and 2018, mainly due to reductions in water (−61%), active bar (−35%), and bare soil surface areas (−29%). The changes were evident ten years after plant operations began and intensified during the period known as the megadrought in central Chile (2008–2018). The flow magnitudes present a decrease for exceedance probabilities (P) below 85% in the period after 1985, with a slight increase recorded for low flows (P > 85%). In the segments with superior stabilization, invasive species such as Acacia dealbata (silver wattle) predominated, which are specialists at taking advantage of disturbances to settle and stabilize active areas, narrowing the possibilities for morphological change. [ABSTRACT FROM AUTHOR]

We present a detailed survey on the ongoing destabilization process of the Mosul dam. The dam is located on the Tigris river and is the biggest hydraulic structure in Iraq. From a geological point of view the dam foundation is unstable due to the underlying geology that is formed by alternate and variable strata of highly soluble materials such as gypsum, anhydrite, marl and limestone. Here we present the first comprehensive multi-sensor cumulative deformation map for the dam generated from space-based synthetic aperture radar (SAR) measurements from the Italian constellation COSMO-SkyMed and the European Sentinel-1a satellite. We compared 2014-2016 data to an historic dataset spanning 2004-2010 acquired with the Envisat ASAR sensor. We found that deformation was rapid during 2004-2010, slowed down in 2012-2014, and restarted in August 2014 when grouting operations stopped due to the temporary capture of the dam by the self proclaimed Islamic State in Iraq and Syria (ISIS). We took advantage of the availability of data from multiple SAR satellites to infer the deformation at the dam in great spatial and temporal detail and shed new light on the processes of the ongoing destabilization. This study highlights how new constellations of SAR sensors together with the availability of historical datasets are leading to important advances in deformation monitoring of small scale geologic and manmade features.

The objective of this research is to detect seasonal agricultural land use and cropping pattern of irrigated crops during the 2017/2018 growing season in the Mujib basin in Jordan. The Mujib basin is considered as one of the major sources for water production in Jordan, while the agricultural production is the main consumer sector for water in the basin. High multi-temporal multi-spectral resolutions Sentinel-2b satellite images were used to identify the land use types using the supervised classification that applied on spring and summer growing season. The results show that the temporal NDVI curve is very useful in distinguishing between different crops type with high details, and is used in assisting the agricultural land use pattern. Multi-temporal NDVI images were used to identify the cropping pattern within the agricultural land using the unsupervised classification approach. The results show that the rainfed agriculture is confined in the high rainfall zones with one period of growing season, while the irrigated agricultural crops are spreading in the basin over the whole year with different periods of growing seasons. Irrigated vegetables and tree crops are the dominant agricultural crops in the basin with more than 70% of cultivated land. Most of the irrigated vegetables and trees are grown in the summer seasons under high temperatures conditions which consumes the highest percentage of irrigated water. These results show that the patterns of agricultural land use under Dryland regions are very essential issue in determining water consumption and management alternatives. [ABSTRACT FROM AUTHOR]

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence. This study considers both environmental (altitude, precipitation, forest type, terrain and humidity index) and socioeconomic (population density, distance from roads and urban areas) factors to analyze how human behavior affects the risk of forest fires. Maximum entropy (Maxent) modelling and random forest (RF) machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to compare the models. We studied the fire history from 1990 to 2019 to establish the relationship between the probability of forest fire and environmental and socioeconomic changes. Using Maxent, the AUC fire probability values for the 1999s, 2009s, and 2019s were 0.532, 0.569, and 0.518, respectively; using RF, they were 0.782, 0.825, and 0.789, respectively. Fires were mainly distributed in urban areas and their probability of occurrence was related to accessibility and human behaviour/activity. AUC principles for validation were greater in the random forest models than in the Maxent models. Our results can be used to establish preventive measures to reduce risks of forest fires by considering socio-economic and environmental conditions. [ABSTRACT FROM AUTHOR]

Introduction: Yariguíes's mountain range, located in the center of the department of Santander, constitutes one of the most relevant strategic ecosystems in the eastern Colombian Andes. However, there is no updated information that describes how deforestation and overexploitation have affected the ecosystem coverage of the Cinco Mil micro-basin. Objective: establish the change in the land cover of the Cinco Mil micro-basin for the 2010-2016 period. Materials and Methods: a multi-temporal analysis was carried out, based on the information available in the Geographic Information System for Planning and the National Territorial Ordering of the Agustín Codazzi Geographical Institute, techniques of visual classification based on satellite images and the cross-tabulation methodology developed by Robert Pontius. Results: The dense forest and fragmented forest categories presented the highest total change in a proportion of 11.79 % (1,178.79 ha) and 14.72 % (1,472.21 ha) respectively. Conclusions: advance reconnaissance processes in the field, which allow characterizing the protected natural areas of the Yariguíes through their inhabitants, in order to identify initiatives that contribute to the conservation of ecosystem services. [ABSTRACT FROM AUTHOR]

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old “legacy” data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003–2013 period changed from 3,237,896 m3 to 135,902 m3 in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m3. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

Many applications require remotely sensed brightness temperature (BT) data acquired with high temporal and spatial resolutions. In this regard, a viable strategy to overtake the physical limitations of space-borne sensors to achieve these data relies on fusing low temporal but high spatial resolution (HSR) data with high temporal but low spatial resolution data. The most promising methods rely on the fusion of spatially interpolated high temporal resolution data with temporally interpolated HSR data. However, the unavoidable presence of cloud masses in the acquired image sequences is often neglected, compromising the functionality and/or the effectiveness of the most of these fusion algorithms. To overcome this problem, a framework combining techniques of temporal smoothing and spatial enhancement is proposed to estimate surface BTs with high spatial and high temporal resolutions even when cloud masses corrupt the scene. Numerical results using real thermal data acquired by the SEVIRI sensor show the ability of the proposed approach to reach better performance than techniques based on either only interpolation or only spatial sharpening, even dealing with missing data due to the presence of cloud masses.

The Brindisi di Montagna Scalo Landslide in Southern Italy is an active complex mass movement, which affects the left slope of the Basento River. In the last few decades, this landslide has been continuously monitored, as it directly threatened some of the most important communication routes in the Basilicata Region. Nevertheless, little progresses have been made to prevent further landslide advancement, and continuous maintenance is required. With the aims of better understanding, the main factors controlling the evolution of this landslide, and suggesting the most appropriate countermeasures, a multidisciplinary study, based on the integration of direct and indirect techniques, was carried out. Direct techniques included multi-temporal geomorphological analysis of the slope, alongside geological and structural field observations. Indirect techniques consisted of electrical resistivity tomography acquisition. The combined analyses of the geological and geophysical data showed that Quaternary tectonic processes played a fundamental role as a predisposing factor, whereas seasonal rainfall, and the perpetual undercutting by erosional processes caused by the Basento River at the toe of the landslide are the main triggering mechanisms. The Brindisi di Montagna Scalo Landslide represents an outstanding case-study, concerning the interaction between a flow-like complex landslide and essential linear infrastructure, such as motorways and railways.

Multi-source mudslides are common in the Daunia Apennines, i.e., the Apulian sector of the Southern Apennines of Italy, and play an important role in the historical evolution of the landscape, with significant impact on the social and economic activities of the area. They also represent the most relevant geological hazard along the front of the Apennine Chain and expose to a considerable risk the local population and infrastructures. The outer sector of the Daunia Apennines is part of the foreland thrust belt system, made up of fissured fine-grained materials, belonging to the Daunia tectonic unit formations. Concerning landslide typology, the most frequent slope movements are represented by flows and composite mudslides, the latter typically starting as slumps evolving from flows. This paper deals with the production of landslide activity maps of three case studies of composite mudslides from the front of the Daunia Apennines. The maps were produced integrating traditional interpretation of multi-year aerial photograph coverage and field surveys with the available digital elevation models to analyze surface morphology. Each landslide activity map corresponds to multi-temporal landslide inventories compiled for the last 15–20 years. This paper outlined that in the last 20 years the front of the Daunia Apennines underwent a significant reactivation of slope failure phenomena after years of quiescence, or of limited activity. The three case studies clearly confirm that: (i) the active portions of the mudslides are located inside or near preexisting slope failures, and (ii) the spatial distribution of instability phenomena is strictly dependent upon the presence of older, larger and dormant, phenomena. Landslide activity maps are an important tool for the evaluation of landslide susceptibility and hazard in the study area, and for quantitative geomorphological analyses it aimed at understanding the long-term geomorphological evolution of this portion of the Southern Apennines. Moreover, we argue that high-quality multi-temporal inventories could have positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations as well. [ABSTRACT FROM AUTHOR]

The installation of hydroelectric plants has generated multiple environmental impacts on the world’s river systems. In central Chile, the impacts of hydroelectric reservoir operation have been documented in ecological and hydrologic regime terms. This investigation assesses the changes in channel morphology, vegetation distribution, and flows in the middle section of the Maule River during the period following the start-up of a hydroelectric plant. Changes in fluvial morphology (active area) and land cover are quantified using LANDSAT images, contrasted with a vegetation sampling and flow analysis. The results show a 12% decrease in active areas of the river, indicating a loss of geomorphological diversity. Within the active channel, there was a gradual increase in plant-covered surface area, which reached 159% between 1989 and 2018, mainly due to reductions in water (−61%), active bar (−35%), and bare soil surface areas (−29%). The changes were evident ten years after plant operations began and intensified during the period known as the megadrought in central Chile (2008–2018). The flow magnitudes present a decrease for exceedance probabilities (P) below 85% in the period after 1985, with a slight increase recorded for low flows (P > 85%). In the segments with superior stabilization, invasive species such as Acacia dealbata (silver wattle) predominated, which are specialists at taking advantage of disturbances to settle and stabilize active areas, narrowing the possibilities for morphological change.

Recently, Unmanned Aerial Vehicles (UAV) have opened up unparalleled opportunities for alpine glacier monitoring, as they allow for reconstructing extensive and high-resolution 3D models. In order to evaluate annual ice flow velocities and volume variations, six yearly measurements were carried out between 2015 and 2020 on the debris-covered Belvedere Glacier (Anzasca Valley, Italian Alps) with low-cost fixed-wing UAVs and quadcopters. Every year, ground control points and check points were measured with GNSS. Images acquired from UAV were processed with Structure-from-Motion and Multi-View Stereo algorithms to build photogrammetric models, orthophotos and digital surface models, with decimetric accuracy. Annual glacier velocities were derived by combining manually-tracked features on orthophotos with GNSS measurements. Velocities ranging between 17 m y−1 and 22 m y−1 were found in the central part of the glacier, whereas values between 2 m y−1 and 7 m y−1 were found in the accumulation area and at the glacier terminus. Between 2 × 106 m3 and 3.5 × 106 m3 of ice volume were lost every year. A pair of intra-year measurements (October 2017–July 2018) highlighted that winter and spring volume reduction was ∼1/4 of the average annual ice loss. The Belvedere monitoring activity proved that decimetric-accurate glacier models can be derived with low-cost UAVs and photogrammetry, limiting in-situ operations. Moreover, UAVs require minimal data acquisition costs and allow for great surveying flexibility, compared to traditional techniques. Information about annual flow velocities and ice volume variations of the Belvedere Glacier may have great value for further understanding glacier dynamics, compute mass balances, or it might be used as input for glacier flow modelling.

In this study, we aim to analyse the glacier dynamics of land-terminating glaciers in King George Island (Antarctica) between 1956 and 2018. Glacial fluctuations are estimated using space-borne remote sensing data (SPOT, Landsat, PlanetScope, Sentinel-1, Sentinel-2, WorldView-2 and TanDEM-X). The eastern sector of Warszawa Icefield witnessed continuous glacier retreat during 1979–2018 (surface loss of 30%). The decreases in the ice-covered areas of the Tower, Windy, Ecology, Baranowski and Sphinx glaciers were 70%, 31%, 25%, 25% and 21%, respectively, with their accumulation area ratios (AARs) exhibiting negative mass balances. The winter air temperature was cooler during the 1970s with warming trends in the 1980s and early 2000s followed by a cooling trend until the present day. However, the annual time series has shown high interannual variability in air temperature during these periods. We show that the AAR, dimensions, length, frontal elevation, maximum elevation, slope and changes in the terminus position influence the glacier response to climate change at various timescales. Furthermore, three geomorphic activity intensity zones and a complete paraglacial sequence are identified while contrasting the proglacial systems. Overall, subglacial deposits predominate and indicate that meltwater flows on the bed, producing wet-based thermal regimes. [ABSTRACT FROM AUTHOR]

Subsidence has been adversely affecting Jharia Coalfield (JCF) for the last few decades. This study attempts to show the feasibility of the modified Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique with C-band SAR data to investigate the slow surface deformation caused by coal mine fire and underground mining activities in JCF. Also, a multi-temporal analysis of SAR images of ENVISAT ASAR has been carried out for monitoring and mapping of temporal land subsidence of the area under study. The modified PSI technique has proven its ability to detect land subsidence over the vegetated and rural areas. It also resolves low spatial density of permanent scatterers by considering partially correlated scatterers as permanent scatterers (PSs) and extracting information from these PSs. The study has been concentrated towards detecting continuous slow rate subsidence of five major sites of JCF. The maximum rate of slow deformation among all sites is recorded as 29 mm/year with a cumulative subsidence value of 90 mm. Field validation of subsidence results obtained through PS-InSAR is correlated with the previously published report and the master plan of JCF, showing subsidence locations. Conclusively, the adopted methodology is practically feasible for detection, monitoring and mapping of slow deformation using C-band SAR data in coal mine area. [ABSTRACT FROM AUTHOR]

The Brindisi di Montagna Scalo Landslide in Southern Italy is an active complex mass movement, which affects the left slope of the Basento River. In the last few decades, this landslide has been continuously monitored, as it directly threatened some of the most important communication routes in the Basilicata Region. Nevertheless, little progresses have been made to prevent further landslide advancement, and continuous maintenance is required. With the aims of better understanding, the main factors controlling the evolution of this landslide, and suggesting the most appropriate countermeasures, a multidisciplinary study, based on the integration of direct and indirect techniques, was carried out. Direct techniques included multi-temporal geomorphological analysis of the slope, alongside geological and structural field observations. Indirect techniques consisted of electrical resistivity tomography acquisition. The combined analyses of the geological and geophysical data showed that Quaternary tectonic processes played a fundamental role as a predisposing factor, whereas seasonal rainfall, and the perpetual undercutting by erosional processes caused by the Basento River at the toe of the landslide are the main triggering mechanisms. The Brindisi di Montagna Scalo Landslide represents an outstanding case-study, concerning the interaction between a flow-like complex landslide and essential linear infrastructure, such as motorways and railways. [ABSTRACT FROM AUTHOR]

• Effective channel control plans mitigate hydro-geomorphic risk in mountain basins. • DoDs and updated cadastre support the efficacy assessment of channel control works. • Sediment dynamics and channel control works data integration improve risk management. • Proposed workflow identifies hazard-prone areas to direct the maintenance works. • Maintenance Priority index could provide guidelines to better invest resources. In the last decade with increasing frequency of extreme weather events, an accurate, sustainable, and effective planning of torrent control structures has become essential to reduce hydro-geomorphic risk. Quite often in planning interventions, there is a lack of information on the effectiveness of existing structures, the evolution of the ongoing hydro-geomorphic process, and a priori in-depth study to analyze the sediment morphology dynamics and the interaction with possible existing torrent control structures. Nowadays, High-Resolution Topography data (HRT) greatly simplifies the monitoring of sediment morphology dynamics and the understanding of the interaction with torrent control structures over time. Therefore, thanks to repeated HRT surveys, it is possible to derive multi-temporal Digital Terrain Models (DTMs), and DTMs of Difference (DoDs) to quantify the morphological changes and study continuously the catchment morphodynamics. This information can be very valuable to support watershed management plans if combined with up-to-date field surveys that identify the existing torrent control structures, and asses their current status and functionality. The present work aims at introducing a methodological approach based on the integration of the sediment morphology dynamics data over large time spans (e.g., from 2003 to 2022), obtained by multi-temporal DoDs (realized from three DTMs at 1 m resolution), with an updating cadastre of torrent control structures enriched by a very simple, quick, and user-friendly Maintenance Priority index (MPi). The proposed workflow proved to be very useful in the test basins analysed, providing more complete data on torrent control structures and sediment dynamics evidence to stakeholders compared to the past. Moreover, it served as a proxy to assess the long-term effectiveness of the management interventions. The approach also helped to constantly identify the areas most prone to hazards, improve the intervention planning, and find more appropriate solutions or direct the maintenance works. Finally, the suggested workflow could be the starting point to outline up-to-date guidelines to be used in other catchments equipped with torrent control structures, emphasizing possible intervention priorities on where decision-makers could better invest resources. By providing current information and accurate tools to realize a more complete decision-making chain, which is often neglected, it is certainly possible to support more sustainable and effective risk management decisions. [ABSTRACT FROM AUTHOR]

Property development and other co-requisite courses have been embedded within academic programs in Estate Management. Among the objectives of this study include identifying the co-requisite courses that accounted for a variation in students' academic performance in property development, and to examine the characteristics of the diagnostics of ordinary least square (OLS) regression model for this relationship over time. Data for this study comprised 2007/2008 to 2014/2015 (8-year) scores for the 1st- and 2nd semester property development and its co-requisite courses in the Department of Estate Management of the Federal Polytechnic Idah, Nigeria. Tools of analysis comprised descriptive statistics (mean score, standard deviation, and coefficient of variation), fitted trend equations, multiple linear regression analysis, and a multi-temporal visualization of the regression diagnostics comprising *ZRESID vs. *ZPRED, normality tests for residuals, and the Durbin-Watson test. The variation in the scores for property development courses during the 8-year period was attributed to specific 1st- and 2nd semester co-requisite courses. EST311 (Valuation I) was found to be the most consistent regressor for EST313 (Property Development I) in the 1st semester, while EST325 (Estate Accounting), was the most consistent regressor for EST323 (Property Development II) in the 2nd semester. Multi-temporal diagnostics analyses did not provide any sufficient evidence to invalidate the OLS regression models for the 1st- and 2nd semester Property Development courses and their co-requisites. Stakeholders' commitment to the teaching and learning of the co-requisite courses was recommended as triggers for a sustained pass rate in the property development courses.

Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.

In this study, bias-corrected daily rainfall data of eight global climate models (GCMs) was used as input for a hydrologic model (Hydrological Engineering Center - Hydrological Modeling System (HEC-HMS)) to simulate daily streamflow in the Upper Ayerawaddy River basin (UARB), Myanmar. Monthly, seasonal, annual, and decadal mean flows, calculated for the baseline (1975–2014), were compared with projections for future periods (2040s: 2021–2060 and 2080s: 2061–2100) under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The spread of low flows (10th and 25th percentile of daily flows) and high flows (75th, 90th, and 100th percentiles) were analyzed for each period. The ensemble of GCMs indicates an increase in mean monthly (except in October and November), seasonal (except post-monsoon), annual, and decadal rainfalls and corresponding flows in the UARB. Future low flows are expected to have high variability while high flows are expected to have higher means than that of baseline. The density distribution analysis of baseline and future flows reveals that future periods are likely to experience an increase in the magnitude of mean flows but a decrease in variability. Rainfall extremes indicated by 1-day maximum rainfall, 5-day consecutive maximum rainfall, and the number of extreme rainfall days reveals frequent wetter extremes in the UARB under future climate conditions. Extreme floods, as estimated by the frequency analysis of daily flows, are also expected to become more frequent during the future periods. These changes in flows can be attributed solely to climate change since the analyses did not account impacts of possible land use change and water resources development in the UARB. This study is a good starting point to assess future flows, and further research is recommended to address the limitations of this study for improved understanding and assessments that will prove useful for planning purposes in the study area. [ABSTRACT FROM AUTHOR]

Many applications require remotely sensed brightness temperature (BT) data acquired with high temporal and spatial resolutions. In this regard, a viable strategy to overtake the physical limitations of space-borne sensors to achieve these data relies on fusing low temporal but high spatial resolution (HSR) data with high temporal but low spatial resolution data. The most promising methods rely on the fusion of spatially interpolated high temporal resolution data with temporally interpolated HSR data. However, the unavoidable presence of cloud masses in the acquired image sequences is often neglected, compromising the functionality and/or the effectiveness of the most of these fusion algorithms. To overcome this problem, a framework combining techniques of temporal smoothing and spatial enhancement is proposed to estimate surface BTs with high spatial and high temporal resolutions even when cloud masses corrupt the scene. Numerical results using real thermal data acquired by the SEVIRI sensor show the ability of the proposed approach to reach better performance than techniques based on either only interpolation or only spatial sharpening, even dealing with missing data due to the presence of cloud masses. [ABSTRACT FROM AUTHOR]

Debris flows are fast‐moving gravity flows of poorly sorted rock and soil, mixed and saturated with water. Debris‐flow initiation has been studied using empirical and experimental modelling, but the geomorphic changes, indicative of different triggering processes, are difficult to constrain with field observations only. We identify signatures to distinguish two different debris‐flow release styles by integrating high‐resolution multi‐temporal remote sensing datasets and morphometric analysis. We analyse debris flows sourced above the town of Ísafjörður (Iceland). Two debris‐flow triggering processes were previously hypothesised for this site: (i) slope failure, characterised by landslides evolving into debris flows; and (ii) the fire‐hose effect, in which debris accumulated in pre‐existing, steep‐sided bedrock passages is transported by a surge of water. It is unknown which process dominates and determines the local risk. To investigate this question, we compare airborne LiDAR elevation models and aerial photographs collected in 2007 with similar data from 2013. We find that two new debris‐flow tracks were created by slope failures. These are characterised by steep sliding surfaces and lateral leveed channels. Slope failure also occurred in two large, recently active tracks, creating the preparatory conditions for the fire‐hose effect to mobilise existing debris. These tracks show alternating zones of fill and scour along their length, and debris stored below the source‐area at rest angles >35°. Our approach allows us to identify and quantify the morphological changes produced by slope failure release process, which generated the preparatory conditions for the fire‐hose effect. As debris flows are rarely observed in action and morphological changes induced by them are difficult to detect and monitor, the same approach could be applied to other landscapes to understand debris‐flow initiation in the absence of other monitoring information, and can improve the identification of zones at risk in inhabited areas near hillslopes with potential for debris flows. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd. We compare two airborne datasets (LiDAR topography and aerial images) to study debris flows in Ísafjörður (Iceland). We assess and distinguish the role of two release mechanisms in debris‐flow generation: slope failure and fire‐hose effect. We find that slope failure is the dominant debris‐flow release process on the slope of Ísafjörður. Two new debris‐flow tracks were created by slope failures, which also created the preparatory conditions for the fire‐hose effect to mobilise existing debris in two other recently active tracks. [ABSTRACT FROM AUTHOR]

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

Understanding environmentally sensitive areas (ESAs) prone to desertification can lead to substantial gains in the efficiency of land use planning and partly avoid negative outcomes. The main objectives of this research were the monitoring and mapping of ESAs to desertification in the agro-ecosystem of the Khanmirza plain, Iran, during two time series (2000 and 2013). In the current study, an adjusted “Mediterranean desertification and land use (MEDALUS)” approach was applied to identify the most ESAs to desertification in the study area and monitorchanges inthe environmental sensitivity area indicator (ESAI)between 2000 and 2013 over the studied area.Fivemain thematic indicators have been evaluated including, Soil quality indicator (SQI), Management quality indicator (MQI), Climate quality indicator (CQI), Vegetation quality indicator (VQI), and Irrigation water quality indicator (IWQI). Results show that the areas affected by the critical desertification status covered approximately 7% of the farmlands and the meadowlands in this agro-ecosystem region in the year 2000. Likewise, in 2013, almost 24% of the study area was sensitive to and affected by desertification, giving a rate of increase of approximately 1.3% per year.More than half of the land used for agriculture has been moderately to severely degraded. The results also show that the central places intheregionwere affected by farmlands and meadowlands degradingto barrenlands due to mismanagement and a lack ofeffective planning withland and water resources. However, rehabilitation of irreversibly degraded land requires serious measures that aim torestore the capability of those areas and increase resistance to degradation through effective planning in water and land in the region.

Ecosystem services offered by mangrove forests are facing severe risks, particularly through land use change driven by human development. Remote sensing has become a primary instrument to monitor the land use dynamics surrounding mangrove ecosystems. Where studies formerly relied on bi-temporal assessments of change, the practical limitations concerning data-availability and processing power are slowly disappearing with the onset of high-performance computing (HPC) and cloud-computing services, such as in the Google Earth Engine (GEE). This paper combines the capabilities of GEE, including its entire Landsat-7 and Landsat-8 archives and state-of-the-art classification approaches, with a post-classification temporal analysis to optimize land use classification results into gap-free and consistent information. The results demonstrate its application and value to uncover the spatio-temporal dynamics of mangrove forests and land use changes in Ngoc Hien District, Ca Mau province, Vietnamese Mekong delta. The combination of repeated GEE classification output and post-classification optimization provides valid spatial classification (94–96% accuracy) and temporal interpolation (87–92% accuracy). The findings reveal that the net change of mangroves forests over the 2001–2019 period equals −0.01% annually. The annual gap-free maps enable spatial identification of hotspots of mangrove forest changes, including deforestation and degradation. Post-classification temporal optimization allows for an exploitation of temporal patterns to synthesize and enhance independent classifications towards more robust gap-free spatial maps that are temporally consistent with logical land use transitions. The study contributes to a growing body of work advocating full exploitation of temporal information in optimizing land cover classification and demonstrates its use for mangrove forest monitoring.

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC-Predicted validation display a best fits in comparison to the EC-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R2 = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R2 = 0.73 and D = 0.85).

3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.

Remote sensing studies of vegetation phenology increasingly benefit from freely available satellite imagery acquired with high temporal frequency at fine spatial resolution. Particularly for heterogeneous landscapes this is good news, given the drawback of medium-resolution sensors commonly used for phenology retrieval (e.g., MODIS) to properly represent the fine-scale spatial variability of vegetation types. The Sentinel-2 mission acquires spectral data globally at 10 to 60 m resolution every five days. To illustrate the mission's potential for studying vegetation phenology, we retrieved phenological parameters for the Dutch barrier island Schiermonnikoog for a full season of Sentinel-2A observations in 2016. Overlapping orbits resulted in two acquisitions per 10 days, similar to what is achieved globally since the launch of Sentinel-2B. For eight locations on the island's salt marsh we compared greenness chromatic coordinate (GCC) series derived from digital repeat RGB-cameras with vegetation index series derived from Sentinel-2 (NDVI and GCC). For each series, a double hyperbolic tangent model was fitted and thresholds were applied to the modelled data to estimate start-, peak-, and end-of-season (SOS/PS/EOS). Variability in Sentinel-2 derived SOS, when taken as the midpoint between minimum and peak NDVI, was well-explained by camera GCC-based SOS (R 2  = 0.74, MSD = 8.0 days, RMSD = 13.0 days). However, EOS estimates from camera GCC series were on average almost two months before NDVI-based estimates. This could partially be explained by the observed exponential relationship between GCC and NDVI, as well as by the combined effect of viewing angle differences and the presence of non-photosynthetic elements in the vegetation canopy. A two-layer canopy radiative transfer model incorporating reduced chlorophyll levels in the upper layer provided a physically-based explanation of the viewing angle effect. Finally, we applied the phenology retrieval approach to NDVI series for all pixels of the island in order to map spatial patterns of phenology at fine resolution. Our results demonstrate the potential of the Sentinel-2 mission for providing spatially-detailed retrievals of phenology. [ABSTRACT FROM AUTHOR]

Microwave imagery has a distinct advantage over optical imagery in high-latitude areas because it allows data to be acquired independently of cloud cover and solar illumination. Synthetic aperture radar (SAR)-based monitoring has become increasingly important for understanding the state and dynamics of permafrost landscapes at the regional scale. This study presents a permafrost landscape mapping method that uses multi-temporal TerraSAR-X backscatter intensity and interferometric coherence information. The proposed method can classify permafrost landscape features and map the two most important features in sub-arctic permafrost environments: permafrost-affected areas and thermokarst ponds. First, a land cover map is generated through the combined use of object-based image analysis (OBIA) and classification and regression tree (CART) analysis. An overall accuracy of 98% is achieved when classifying rock and water bodies, and an accuracy of 79% is achieved when discriminating between different vegetation types with one year of single-polarized acquisitions. Second, the distributions of the permafrost-affected areas and thermokarst ponds are derived from the classified landscapes. Permafrost-affected areas are inferred from the relationship between vegetation cover and the existence of permafrost, and thermokarst pond distributions are directly inherited from the land cover map. The two mapped features exhibit good agreement with manually delineated references. The proposed method can produce permafrost landscape maps in complex sub-arctic environments and improve our understanding of the effects of climate change on permafrost landscapes. This classification strategy can be transferred to other time-series SAR datasets, e.g., Sentinel-1, and other heterogeneous environments. [ABSTRACT FROM AUTHOR]

Land cover is one of the most important conditioning factors in landslide susceptibility analysis. Usually it is considered as a static factor, but it has proven to be dynamic, with changes occurring even in few decades. In this work the influence of land cover changes on landslide susceptibility are analyzed for the past and for future scenarios. For the application, an area representative of the hilly-low mountain sectors of the Italian Southern Apennines was chosen (Rivo basin, in Molise Region). With this purpose landslide inventories and land cover maps were produced for the years 1954, 1981 and 2007. Two alternative future scenarios were created for 2050, one which follows the past trend (2050-trend), and another one more extreme, foreseeing a decrease of forested and cultivated areas (2050-alternative). The landslide susceptibility analysis was performed using the Spatial Multi-Criteria Evaluation method for different time steps, investigating changes to susceptibility over time. The results show that environmental dynamics, such as land cover change, affect slope stability in time. In fact there is a decrease of susceptibility in the past and in the future 2050-trend scenario. This is due to the increase of forest or cultivated areas, that is probably determined by a better land management, water and soil control respect to other land cover types such as shrubland, pasture or bareland. Conversely the results revealed by the alternative scenario (2050-alternative), show how the decrease in forest and cultivated areas leads to an increase in landslide susceptibility. This can be related to the assumed worst climatic condition leading to a minor agricultural activity and lower extension of forested areas, possibly associated also to the effects of forest fires. The results suggest that conscious landscape management might contribute to determine a significant reduction in landslide susceptibility. [ABSTRACT FROM AUTHOR]