Satellite Image Time Series Research Articles

Classification from Sky: A Robust Remote Sensing Time Series Image Classification Using Spatial Encoder and Multi-Fast Channel Attention

The unparalleled availability of Satellite Image Time Series (SITS) for crop phenology classification unravels agricultural parcel observation and monitoring with applications of both economic and ecological importance. Moreover, the need for distinct classification of agricultural parcels into individual crop types falls on state-of-the-art deep learning models for this extrinsic task. However, most existing approaches implemented are complex and ineffective attention incorporated models, which in turn lack the resilience to recognize useful bands in achieving greater accuracy. We propose a Multi-Fast Channel Attention module for deep CNNs based on a Spatial Encoder (SE-MFCA) that requires a few parameters while enhancing the performance-complexity trade-off dilemma. Hence, we leverage on spatial encoder module to extract the images as disorderly sets of pixels to enhance the coarse spatial resolution features. We empirically show that appropriate parameter sharing in the cross channel interaction can preserve performance while significantly reducing model complexity. The proposed multi-channel attention module can efficiently be implemented via an encoder-decoder network to prevent the loss of detailed spatial information. Again, we parallelly distributed the input channel into multiple heads in our network to recover the specialized input features, which will concatenate with the residual to form a rich single feature representation. The extensive experiment has shown that our model SE-MFCA is efficient and effective compared with the previous state-of-the-art time series classification algorithm on a publicly available dataset of Sentinel-2 images for agricultural parcels. Performance-wise SE-MFCA achieves the highest overall accuracy of 94.50% and the highest mean intersection over union score of 51.92%, besides the least trainable params of 131 K and fewer floating point operations of 0.16 M.

Assessment of Fire Regimes and Post-Fire Evolution of Burned Areas with the Dynamic Time Warping Method on Time Series of Satellite Images—Setting the Methodological Framework in the Peloponnese, Greece

Forest fires are considered to be an important part of numerous terrestrial ecosystems and vegetation types, being also a significant factor of ecosystem disruption. In this sense, fires play an important role in the structure and function of the ecosystems. Biomes are characterized by a specific type of fire regime, which is a synergy of the climate conditions and the characteristics of the vegetation types dominating each biome. The assessment of burned areas and the identification of the fire regimes can be implemented with freely available low- to high-resolution satellite data as those of Landsat and Sentinel-2. Moreover, the biomes are characterized by the phenology, a useful component for vegetation monitoring, especially when time series of satellite images are used. Both the identification of fire regime by reconstructing the fire history and the monitoring of the post-fire evolution of burned areas were studied with remote sensing methods. Specifically, the present paper is a pilot study implemented in a Mediterranean biome, aimed at establishing the methodological framework to (i) define fire regimes, (ii) characterize the phenological pattern of the vegetation (pre-fire situation) of the fire-affected areas, and (iii) compare the phenology of the recovered fire-affected areas with the corresponding one of the pre-fire situation. At the global level, based on MODIS fire perimeters, we found that fires are occurring at 70% in the tropical and subtropical grasslands, savannas, and shrublands, followed by fires at tropical and subtropical moist broadleaf forests by 7% and by fires at deserts and xeric shrublands by 6.5%.

Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France

Current Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim was to forecast the land cover class of a group of pixels as a multi-class single-label classification problem given their time series gathered using satellite images. In this article, we exploit SITS to assess the capability of several spatial and temporal deep learning models with the proposed architecture. The models implemented are the bidirectional gated recurrent unit (GRU), temporal convolutional neural networks (TCNN), GRU + TCNN, attention on TCNN, and attention of GRU + TCNN. The proposed architecture integrates univariate, multivariate, and pixel coordinates for the Reunion Island’s landcover classification (LCC). the evaluation of the proposed architecture with deep neural networks on the test dataset determined that blending univariate and multivariate with a recurrent neural network and pixel coordinates achieved increased accuracy with higher F1 scores for each class label. The results suggest that the models also performed exceptionally well when executed in a partitioned manner for the LCC task compared to the temporal models. This study demonstrates that using deep learning approaches paired with spatiotemporal SITS data addresses the difficult task of cost-effectively classifying land cover, contributing to a sustainable environment.

Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R2 of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture.

A Workflow for Collecting and Preprocessing Sentinel-1 Images for Time Series Prediction Suitable for Deep Learning Algorithms

The satellite image time series are used for several applications such as predictive analysis. New techniques such as deep learning (DL) algorithms generally require long sequences of data to perform well; however, the complexity of satellite image preprocessing tasks leads to a lack of preprocessed datasets. Moreover, using conventional collection and preprocessing methods is time- and storage-consuming. In this paper, a workflow for collecting, preprocessing, and preparing Sentinel-1 images to use with DL algorithms is proposed. The process mainly consists of using scripts for collecting and preprocessing operations. The goal of this work is not only to provide the community with easily modifiable programs for image collection and batch preprocessing but also to publish a database with prepared images. The experimental results allowed the researchers to build three time series of Sentinel-1 images corresponding to three study areas, namely the Bouba Ndjida National Park, the Dja Biosphere Reserve, and the Wildlife Reserve of Togodo. A total of 628 images were processed using scripts based on the SNAP graph processing tool (GPT). In order to test the effectiveness of the proposed methodology, three DL models were trained with the Bouba Ndjida and Togodo images for the prediction of the next occurrence in a sequence.

Cropland expansion, intensification, and reduction in Mato Grosso state, Brazil, between the crop years 2000/01 to 2017/18

Growing populations require increased food production through increasing yields and expanding land use. Thus, agricultural production has a dialectical relationship with natural resource conservation. South America has been the recent site of both intensification and areal expansion of crop production. The presented study investigates the dynamics of land use and land cover (LULC) in Mato Grosso state at two-year intervals for an 18-year period consisting of the 2000/01 and 2017/18 crop years. We used a stratified sampling approach and visual interpretation of satellite image time series data to obtain estimates of LULC extent and dynamics, including area of cropland expansion, intensification, and reduction. Cropland dynamics exhibited an overall increase in both extent and intensification. The cropland use area in Mato Grosso increased from 38,324 km2 (±5618 km2) in 2000/01 to 97,394 km2 (±9938 km2) in 2017/2018. For the Cerrado tropical woodland biome portion of Mato Grosso, the expansion of cropland areas was related to soy and corn prices. For the humid tropical forest biome of Amazonia, soy production and deforestation were found to not be directly correlated. The intensification in Mato Grosso was increased from 8142 km2 (±1.39%) in the period from 2008/09 to 2010/11, to 30,487 km2 (±2.56%) in the period from 2010/11 to 2012/13. Overall dynamics reflect public and private conservation policies, soy market, climate and technology factors that spur or inhibit increased production of commodity crops. The end result reflects Brazil's increasing role as a global commodity crop producer.

A Methodology for National Scale Coastal Landcover Mapping in New Zealand

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, be uncertain because the waterline is sensitive to marine conditions and beach gradient. In addition, the technique disregards potentially useful data stored in surrounding pixels. In this paper, we describe a pixel-based technique to analyse coastal change. A hybrid rule-based and machine learning methodology was developed using a combination of Sentinel multispectral and Synthetic Aperture Radar composite imagery. The approach was then used to provide the first national-scale pixel-based landcover classification for the open coast of New Zealand. Nine landcover types were identified including vegetation, rock, and sedimentary classes that are common on beaches (dark sand, light sand, and gravel). Accuracy was assessed at national scale (overall accuracy: 86%) and was greater than 90% when normalised for class area. Using a combination of optical and Synthetic Aperture Radar data improved overall accuracy by 14% and enhanced the separation of coastal sedimentary classes. Comparison against a previous classification approach of sandy coasts indicated improvements of 30% in accuracy. The outputs and code are freely available and open-source providing a new framework for per-pixel coastal landcover mapping for all regions where public earth observation data is available.

Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.

Image-driven hydrological parameter coupled identification of flood plain wetland conservation and restoration sites

A good many works focus on wetland vulnerability; some works also explore restoration sites at a very limited spatial extent. But the satellite image-driven hydrological data-based approach adopted in this work is absolutely new. Moreover, existing work only focused on identifying restoration sites in the present context, but for devising long-term sustainable planning, predicted hydrological parameters based on possible restoration sites may be an effective tool. Considering this, the present work focused on exploring hydrological data (water presence frequency (WPF), hydro-period (HP) and water depth (WD)) from time-series satellite images. This exploration may resolve the hydrological data scarcity of wetland over the wider geographical areas. Using these parameters, we developed wetland restoration and conservation sites for different historical years (2008, 2018) and predicted years (2028) using ensemble machine learning (EML) models. From the analysis, it was found that water depth, hydro-period and WPF became poorer over the period, and the trend may seem to continue in predicted years. Among the applied EML models, Random Subspace (RS) predicted wetland restoration and conservation sites precisely over others. The predicted area under high-priority restoration sites is 34% in 2018, which was 14% in 2008. In 2028, 12% more areas may fall in this priority level. Wetland away from main streams (mainly ortho-fluvial wetland) and fringe wetland parts should be given more priority for restoration. These present and predicted information will effectively help to frame sustainable wetland restoration planning.

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable.

Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China.

Machine learning combined with satellite image time series can quickly, and reliably be implemented to map crop distribution and growth monitoring necessary for food security. However, obtaining a large number of field survey samples for classifier training is often time-consuming and costly, which results in the very slow production of crop distribution maps. To overcome this challenge, we propose an ensemble learning approach from the existing historical crop data layer (CDL) to automatically create multitudes of samples according to the rules of spatiotemporal sample selection. Sentinel-2 monthly composite images from 2017 to 2019 for crop distribution mapping in Jilin Province were mosaicked and classified. Classification accuracies of four machine learning algorithms for a single-month and multi-month time series were compared. The results show that deep neural network (DNN) performed the best, followed by random forest (RF), then decision tree (DT), and support vector machine (SVM) the least. Compared with other months, July and August have higher classification accuracy, and the kappa coefficients of 0.78 and 0.79, respectively. Compared with a single phase, the kappa coefficient gradually increases with the growth of the time series, reaching 0.94 in August at the earliest, and then the increase is not obvious, and the highest in the whole growth cycle is 0.95. During the mapping process, time series of different lengths produced different classification results. Wetland types were misclassified as rice. In such cases, authors combined time series of two lengths to correct the misclassified rice types. By comparing with existing products and field points, rice has the highest consistency, followed by corn, whereas soybeans have the least consistency. This shows that the generated sample data set and trained model in this research can meet the crop mapping accuracy and simultaneously reduce the cost of field surveys. For further research, more years and types of crops should be considered for mapping and validation.

Multimodal Satellite Image Time Series Analysis Using GAN-Based Domain Translation and Matrix Profile

The technological development of the remote sensing domain led to the acquisition of satellite image time series (SITS) for Earth Observation (EO) by a variety of sensors. The variability in terms of the characteristics of the satellite sensors requires the existence of algorithms that allow the integration of multiple modalities and the identification of anomalous spatio-temporal evolutions caused by natural hazards. The unsupervised analysis of multimodal SITS proposed in this paper follows a two-step methodology: (i) inter-modality translation and (ii) the identification of anomalies in a change-detection framework. Inter-modality translation is achieved by means of a Generative Adversarial Network (GAN) architecture, whereas, for the identification of anomalies caused by natural hazards, we adapt the task to a similarity search in SITS. In this regard, we provide an extension of the matrix profile concept, which represents an answer to identifying differences and to discovering novelties in time series. Furthermore, the proposed inter-modality translation allows the usage of standard unsupervised clustering approaches (e.g., K-means using the Dynamic Time Warping measure) for mono-modal SITS analysis. The effectiveness of the proposed methodology is shown in two use-case scenarios, namely flooding and landslide events, for which a joint acquisition of Sentinel-1 and Sentinel-2 images is performed.

Forest loss is significantly higher near clustered small dams than single large dams per megawatt of hydroelectricity installed in the Brazilian Amazon

Hydropower in the Brazilian Amazon is a prevalent form of development, but dams have widespread and long-term environmental impacts that include deforestation in the areas surrounding the dams. Small hydropower plants (SHPs) are often perceived as having reduced environmental impacts compared to the large ones. In Brazil, SHPs are licensed by state governments, which have less strict requirements than the federal environmental agency. Brazil’s definition of ‘small’ dams has grown with successive increases in the maximum installed capacity from 10 to 30 to 50 megawatts (MW). This expanding loophole has increased the attractiveness of investing in multiple small dams rather than a single large dam, with resulting proliferation of SHPs. Forest dynamics surrounding the clustered SHPs when compared to single large dams are not well documented. In this study, we capitalized on a dense time series of satellite images to quantify and compare forest loss in the regions (over 110 000 km2 in area) surrounding 15 SHPs and 7 large dams at multiple watershed and buffer scales in the Brazilian Amazon for nearly two decades (2000–2018). The landscapes containing SHP clusters had lower cumulative forest loss as compared to those with large dams. However, when deforestation and hydroelectric generating capacity were jointly considered (i.e. forest loss per megawatt installed), we discovered an opposite trend. The regions surrounding the SHP clusters exhibited significant impacts ranging from 1.9 to 2.5 times that of the regions surrounding large dams across 5 km to sub-basin scales. Due to the considerable consequences of SHPs on deforestation, we argue that the rapid expansion of small hydropower should be approached with caution and requires more stringent environmental assessments.

Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul

Many studies have reported that urbanization leads to a decrease in the normalized difference vegetation index (NDVI) due to the expansion of impervious cover. Some studies, however, have reported positive NDVI trends in urban areas due to warming and CO2 fertilization effects, as well as the creation of green space. Thus, we examined spatial and temporal variations of the growing season maximum NDVI in a megacity, Seoul, which has rapidly urbanized over the past decades, by analyzing a 32-year time series (1987 – 2018) of Landsat satellite images in Google Earth Engine. Continuous change detection and classification and random forest algorithms were integrated to classify Seoul land cover types annually. We found an overall increasing NDVI trend at the city scale (0.002 yr−1). Significant NDVI trends were found for approximately 46 % of Seoul, with greening and browning trends accounting for 39 % and 7 %, respectively. Greening pixels appeared mainly on impervious (23 % with a significant NDVI trend), deciduous (10 %), and evergreen (3 %) land cover as of 2018. Stable impervious, deciduous, and evergreen land cover pixels showed a greening trend over the 32 years (0.002 yr−1), which stemmed from the planting of trees in areas with impervious cover, such as streets and residential areas, and vegetation growth in forest areas. Disturbed area pixels showed fluctuating NDVI values, but there were more greening pixels (20 %) than browning pixels (5 %). Our findings indicate that a detailed knowledge of land use and land cover changes is required to understand NDVI trends in urban areas.

Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis

The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort-consuming to acquire. It is necessary to analyze SITS data with an unsupervised learning method. In this paper, we propose a new unsupervised learning method named Deep Temporal Iterative Clustering (DTIC) to deal with SITS data. The proposed method jointly learns a neural network’s parameters and the resulting features’ cluster assignments, which uses a standard clustering algorithm, K-means, to iteratively cluster the features produced by the feature extraction network and then uses the subsequent assignments as supervision to update the network’s weights. We apply DTIC to the unsupervised training of neural networks on both SITS datasets. Experimental results demonstrate that DTIC outperforms the state-of-the-art K-means clustering algorithm, which proves that the proposed approach successfully provides a novel idea for unsupervised training of SITS data.

Effects of subsidy and regulation policy on soil and water resources of cotton planted lands in Harran Plain, Turkey

Cotton (Gossypium hirsutum L.) provides essential raw material used in the textile, feed, and vegetable oil sectors. Therefore, it is a strategic crop for agricultural, economic, and political strategies around the world. Accurate monitoring of the changes in the cotton production patterns is crucial for decision-makers. Turkey is among the top ten cotton producers and ranks fourth in the world for cotton imports. One third of the total cotton production in Turkey comes from the southeastern Anatolia region of the country. This study analyzed the impact of subsidy and regulation policies on fluctuations in cotton production areas and soil and water quality in Harran Plains, southeastern Anatolia region Turkey. Time-series (between 2016 and 2021) satellite images were utilized to determine actual crop rotation in the study area. Turkish Government implemented a new rule for crop subsidies in 2020 which stated that a farmer will be eligible for subsidy for two consecutive years; however the subsidy will not be paid if the same crop is planted in the third year. Cotton was cultivated in 83 % of the Harran plains prior to new subsidy regulation, which decreased to 26 % after the new regulation. Sudden decrease in cotton cultivation area clearly indicated that farmers need public support to cover the costs in agricultural production. The Gross Production Value (BPV) for each crop was calculated to determine the impact of subsidy regulation on economic value of crops in the rotation.The GPV in 2020 was 2.903,7 $ ha−1 that was 33.6 % and 20.5 % lower than 2019 and 2021, respectively. The results indicated that crop subsidies significantly altered the crop selection behavior of farmers. Water requirement of crops in rotation between 2016 and 2021 was calculated to assess the effects of subsidy changes on water consumption. Cotton cultivation over a large part of the plains during the same season caused excessive water consumption, rise in groundwater level over time, soil salinity and severe water erosion. However, cotton cultivation area as well as water consumption in Harran plain new significantly decreased with the new subsidy regulation; therefore, water consumption in 2020 was the lowest among the time frame evaluated. Total sediment losses through surface runoff from the Harran Plain was estimated as 0.83 tons ha−1 per year. The results revealed that subsidies exert significant impacts on the gross production value in the region, amount of irrigation water consumed, and quality of soil and water resources in addition to the impact on the decision of farmers for crop selection.

Analysis of Interannual and Seasonal Nearshore Bar Behaviour Observed from Decadal Optical Satellite Data in the Curonian Spit, Baltic Sea

Long-term observations of nearshore bar behaviour are a vital component of coastal monitoring, management, and prediction. Optical satellite remote sensing enables the possibility of such observations over large spatial areas, but its full potential remains unexploited. This study assessed alongshore variability in cross-shore nearshore bar behaviour on a wave-dominated multi-bar coast of the Curonian Spit (south-eastern Baltic Sea) between 2011 and 2021, using satellite-derived bar data. Nearshore bars were extracted from a time series of PlanetScope and RapidEye satellite images with an automated GIS-based algorithm, previously proposed by the study authors. The cross-shore behaviour of a multiple bar system was analysed by adapting traditional bathymetry-based analysis techniques to satellite-derived data that included bar crestlines and images of multi-scale Relative Bathymetric Position Index (RBPI). The analysis was performed on 1071 shore-perpendicular transects. Multi-bar onshore and offshore migration rates were quantified on interannual and seasonal timescales. The results show that, on an interannual timescale, bars migrated offshore at rates up to 9.7 m/month, while the rates of onshore migration reached up to 11 m/month. During the months of low wave energy, bars moved offshore at rates up to 6.2 m/month, and during the months of high wave energy, up to 12.9 m/month. However onshore migration rates, during the months of low and high wave energy, reached up to 7.0 and 13.4 m/month, respectively. A complex empirical orthogonal function (CEOF) analysis was performed on RBPI-derived cross-shore profiles, and cyclic offshore directed bar behaviour was examined. For the first time, the net offshore migration (NOM) cycle with bar cycle return periods of 1.8 to 13.5 years was investigated on the south-eastern Baltic Sea coast. Bar cycle return periods increased and rates of bar cross-shore migration decreased from north to south along the Curonian Spit. Similar nearshore bar behaviour regions were identified using clustering analysis based on quantified temporal and morphological characteristics of the bars. Factors controlling alongshore variability in bar cross-shore behaviour were determined. The study results suggest that small alongshore variations in nearshore hydrodynamics, caused by the local wave climate and its interplay with the shoreline orientation, determine the morphological and temporal variability of the multi-bar system in the Curonian Spit.

Monitoring of water resources and vegetation in the Helmand Basin using satellite image time-series and border hydro-political challenges

Abstract Helmand is the most important river in Afghanistan and an indispensable water resource for southeastern Iran. Recent water depletions in the river, however, have caused environmental issues in the region with further repercussions on long-term hydro-political debates between the countries of Iran and Afghanistan. The primary reasons underlying depletions in the river's water levels remain, however, unclear, and are the main objective of this study.Therefore, this study proposes a hydro-political analysis of the Helmand Basin by analyzing precipitation through Global Precipitation Measurement (GPM) data, Gravity Recovery and Climate Experiment (GRACE) land data for groundwater analysis, and Landsat 5 and 8 Images from 1991 to 2020 to classify vegetation and waterbodies using a Support Vector Machine classifier and identify the prime cause of downstream water depletions. Despite severe droughts, the preliminary findings indicated increases in rainfall, groundwater sources, water bodies, and vegetation in the river upstream, which conveys the inconsequential share of droughts to the overall water shortage as opposed to human interventions and water usage which have also shown to increase in the river upstream. Further findings suggest that the severe downstream water depletions are primarily on account of upstream water consumptions that have triggered environmental degradations and are a threat to human habitations.

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.