OLI Satellite Research Articles

Local Scale Assessment of Forest-Cover in the Tropics – An Implication to Habitat Conservation

Deforestation is one of the greatest environmental concerns that the world is facing at present. As a result of human-mediated deterioration or destruction, tropical forests have altered at an unparalleled rate during the last century. Further tropical forest transition does not occur evenly throughout a region or country; rather, it is localized in a very limited area. As such, the study aimed to measure and document the deforestation and degradation on a small forested habitat in the tropics, i.e. Kaki Reserve Forest under Marat Longri Wildlife Sanctuary, northeast India using remote sensing technology. The forest conditions were observed using Landsat TM and OLI satellite images between 1991 and 2015. The current study used the Forest Canopy Density Mapping and Monitoring Model to track deforestation or degradation in the test region. Results show a significant decline in forest cover in the area. It was observed that between 1991 and 2015, the 53.3% of the total area is under pressure of deforestation and degradation. Rate of forest cover transformation under different classes ranges from 0.37 to -8.15 which are exceptionally high in comparison to other parts of the country. The study also indicated that increased human activities such as illegal-felling, agricultural development, encroachment, and collections pressure have caused huge disruptions in this forested habitat throughout the study period. Thus, it requires rapid attention in order to ensure effective forest planning and management. The study also demonstrates how integration of remote sensing data and biophysical models can be used to examine spatial forest state, which may be used for long-term forest management at the local and regional levels.

Ocean Color Remote Sensing of Suspended Sediments along a Continuum from Rivers to River Plumes: Concentration, Transport, Fluxes and Dynamics

This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.

A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network

In this paper, we aim to compare the suitability of Sentinel-2 and Landsat 8 OLI images for detecting and mapping soil salinity distribution (SSD) using a deep learning convolutional neural network (DL-CNN) approach. We first identified and selected six SSD predisposing variables to train the models. These variables are the normalized difference vegetation index (NDVI), land use, soil types, geomorphology, land surface temperature, and evaporation rate. Next, we collected 219 ground control points from the top 20 cm of the soil surface and randomly divided them into training (70%) and validation (30%) datasets. We then evaluated the different activation, loss/cost, and optimization functions and, finally, employed ReLu, Cross-Entropy, and Adam as the most effective activation function, loss/cost function, and optimizer, respectively. The results showed that the Sentinel-2 image (94.78% overall accuracy and a Kappa of 93.14%) is more suitable for detecting and mapping SSD than the Landsat 8 OLI image (91.45% overall accuracy and a Kappa of 90.45%). Our findings also demonstrated that the DL-CNN approach can support fast and reliable image analysis and classification. As such, this research is a promising step toward understanding, controlling, and managing the complex mechanisms of soil salinization.

Forest Fire Assessment Using Remote Sensing to Support the Development of an Action Plan Proposal in Ecuador

Worldwide, forest fires exert effects on natural ecosystems, contributing to economic/human losses, health effects, and climate change. Spectral indices are an essential tool for monitoring and analyzing forest fires. These indices make it possible to evaluate the affected areas and help mitigate possible future events and reduce damage. The case study addressed in this work corresponds to the Cerro of the Guadual community of La Carolina parish (Ibarra, Ecuador). This work aims to evaluate the degree of severity and the recovery of post-fire vegetation, employing the multitemporal analysis of spectral indices and correlating these with the climatological aspects of the region. The methodological process was based on (i) background information collection, (ii) remote sensing data, (iii) spectral index analysis, (iv) multivariate analysis, and (v) a forest fire action plan proposal. Landsat-8 OLI satellite images were used for multitemporal analysis (2014–2020). Using the dNDVI index, the fire’s severity was classified as unburned and very low severity in regard to the areas that did not regenerate post-fire, which represented 10,484.64 ha. In contrast, the areas classified as high and very high severity represented 5859.06 ha and 2966.98 ha, respectively. In addition, the dNBR was used to map the burned areas. The high enhanced regrowth zones represented an area of 8017.67 ha, whereas the moderate/high-severity to high-severity zones represented 3083.72 ha and 1233.49 ha, respectively. The areas with a high severity level corresponded to native forests, which are challenging to recover after fires. These fire severity models were validated with 31 in situ data from fire-starting points and they presented an accuracy of 99.1% in the high severity category. In addition, through the application of principal component analysis (PCA) with data from four meteorological stations in the region, a bimodal behavior was identified corresponding to the climatology of the area (dry season and rainy season), which is related to the presence of fires (in the dry season). It is essential to note that after the 2014 fire, locally, rainfall decreased and temperatures increased. Finally, the proposed action plan for forest fires made it possible to define a safe and effective evacuation route to reduce the number of victims during future events.

Land use and land cover change analysis using satellite images in Gua Musang, Kelantan

Abstract The use of multisensor remote sensing to characterise forest structure has significant promise for mapping and understanding forest biological processes, making remote sensing an effective technology in detecting deforestation for forest management and monitoring. The aim of this study is to detect land use and land cover (LULC) changes due to deforestation at Gua Musang, Kelantan using satellite images. The Landsat 8 OLI satellite images were obtained from United State Geological Survey (USGS) for year 2017 to 2019. The supervised classification with maximum likelihood algorithm was applied for image classification to generate land use land cover categories. Then, LULC change detection between 2017, 2018 and 2019 were conducted to analyse the change and pattern. Based on the results, it shows the percentage of each land use changes within three (3) years from 2017 to 2019 for Landsat 8 OLI. Percentage of deforestation due to logging areas for year 2017 to 2019 is 5.95%. Percentage of forest area is 5.57%. For water bodies, the percentages just only cover 0.36% in 3 years. The highest land use change is no changes which is 88.12% happened towards the land from 2017 and 2019. This shows within three years there is no great changes at the area. The overall accuracy for each map is more than 80% which are acceptable. From the analysis, there were no significant changes of LULC during this period due to the increased of timber extraction, agricultural land expansion, urban growth, and poor governance structures.

Changes in physicochemical, heavy metals and air quality linked to spot Aplocheilus panchax along Mahanadi industrial belt of India under COVID-19-induced lockdowns.

Positive effects of COVID-19-induced lockdowns on environment are well documented although pre-planned experiments for such analyses and appearance of fish species are lacking. We hypothesize that spotting the fish Aplocheilus panchax along the industrial belt of Mahanadi River near Cuttack in a never seen manner could be due to the regenerated environment. Heavy metals, water and air qualities along with spotting A. panchax in up, mid and downstream of Mahanadi River near Jagatpur industrial basins were analysed during pre-(end of March 2020) and after 60 days of lockdowns (last week of May 2020). An overall 45, 61, 79, 100, 97 and 90% reduction of Fe, Cu, Ni, Cd, Pb and Zn was recorded in the studied area after lockdowns, respectively. Similarly, dissolved oxygen and pH were elevated by 26 and 7%, respectively. Water temperature, conductivity and total dissolved solute levels were reduced by 7, 46 and 15%, respectively, which were again elevated during post-lockdowns during 2021 as observed from the Landsat-8 OLI satellite data. Air NO2, SO2, NH3, PM2.5, PM10 and CO levels were alleviated by 58.75, 80.33, 72.22, 76.28, 77.33 and 80.15%, respectively. Finally, for the first time, about 12 A. panchax fish per 100 m shore line in the area were spotted. The observed lockdown-induced environmental healing at the studied area could contribute to the appearance of A. panchax in the study site and therefore a stringent environmental audit is suggested during post-COVID-19 periods to make the regenerated environmental status long lasting in such habitats.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10653-022-01247-3.

Trophic State Assessment of a Freshwater Himalayan Lake Using Landsat 8 OLI Satellite Imagery: A Case Study of Wular Lake, Jammu and Kashmir (India)

AbstractA new version of Trophic State Index for freshwater Himalayan Lake (TSIFHL) has been derived from Landsat 8 OLI to determine the aquatic health of the lake ecosystem. TSIFHL is based on chlorophyll‐a concentration (CChl‐a) which has been retrieved from Landsat 8 OLI data and laboratory measurements using an empirical approach. Further, in situ measurements have also been taken with Secchi disk depth (ZSD) in a freshwater Himalayan Lake (FHL). The derived CChl‐a exhibited lower and upper limit of 25.81 and 207.96 μg/L, respectively. The modeled ZSD values ranged between 0.18 and 0.66 m with an average depth of 0.50 m. The best‐fitted regression model, developed for CChl‐a with R2 = 0.89, exhibited model error of 0.77 μg/L for the standard error of estimate (SEE). The mean absolute percentage error (MAPE), and Nash‐Sutcliffe coefficient (E) values were 5.83% and 0.98 μg/L, respectively. For the ZSD, the best‐fitted model showed errors of 0.11 μg/L (SEE), 13.93% (MAPE), and 0.77 μg/L (E) with R2 = 0.84. The proposed model is useful for bio‐optical studies of Himalayan Lake ecosystems and could provide potential support for inland water management. Long term strategies like multisector monitoring and control of nutrient load entering the lake can be developed to alleviate problems of eutrophication.

Gold prospectivity mapping and exploration targeting in Hutti-Maski schist belt, India: Synergistic application of Weights-of-Evidence (WOE), Fuzzy Logic (FL) and hybrid (WOE-FL) models

Our study attempts to map gold prospectivity for deriving optimal exploration targets using data-driven, knowledge-driven and hybrid approaches. Prospectivity models viz. Weights of Evidence (WOE), Fuzzy Logic (FL) and a hybrid model that combines the data-driven and knowledge-driven components (WOE-FL) were applied to a part of the auriferous Hutti-Maski schist belt of 1352 km2 area with 20 known gold occurrences. With a pixel resolution of 500 m, 16 spatial evidential raster layers were created on a GIS platform encompassing viable predictive indicators, critical in gold exploration. Modelling inputs include essential ingredients and mappable criteria of the conceived orogenic gold mineral system in the study area. Multi-source geological data such as lithostratigraphic units, favourable litho-contacts, structural deformation sites, geochemical anomalies of selected gold pathfinder elements, and hydrothermal alteration zones derived from digital image processing of Landsat 8 OLI satellite imagery were integrated to generate prospectivity maps for delineation of future targets. A quantitative evaluation of the resulting three prospectivity maps was performed using concentration-area (C-A) fractal analysis, prediction–area (P–A) plot, fitting-rate curve (FRC) and area under curve (AUC). Comparative analysis indicates that the performance of the hybrid model (WOE-FL) stands out to be the most efficient, with a prediction rate of 87% and AUC of 91.40% compared to WOE and FL. A risk assessment was performed combining the outputs of prospectivity models that returned 10% of the study area as potential exploration targets out of which the low-risk exploration targets comprises merely 4.5% representing the optimal targets for gold exploration in the study area.

Application of the METRIC model for mapping evapotranspiration over the Sundarban Biosphere Reserve, India

This study is an attempt to estimate the spatial distribution of daily evapotranspiration (ET) in the Indian part of the Sundarban Biosphere Reserve by utilizing the (Mapping Evapo Transpiration at high resolution with internalized Calibration (METRIC) model. Landsat 8 OLI satellite image was used to run the METRIC model and calculate ET to explain the overall surface energy balance in the area under the present climatic conditions. The result obtained shows that, the ET minimum value ranges from 6.874 mm/day in cropland to a maximum of 12.321 mm/day in the forest and water bodies. We used a (ETrF) value of 1 for forests since the forest surface temperature was lower than the air temperature, resulting in a negative sensible heat flow. The use of METRIC energy balancing procedures for validation purposes using Landsat imagery shows that the per hourly, monthly, and yearly ET estimations acquired using MODIS (Moderate Resolution Imaging Spectro radiometer) data may be effectively reproduced. The research includes a novel component that verifies the efficacy of this technique for water conservation, as well as satellite-based technology, which offers effective and efficient tools for integrated evapotranspiration estimation.

Spatial analysis of the urban physical vulnerability using remote sensing and geographic information systems (case study: Yogyakarta City)

Vulnerability mapping in urban planning is very important to reduce damage caused and loss of life due to disasters. This paper discussed the mapping of urban physical vulnerability using remote sensing, and geographic information systems (GIS) approaches. Physical vulnerability parameters used were building density, distribution of public facilities, slope, and land use. Landsat 8 OLI satellite imagery used remote sensing data to obtain building density information using the Normalised Difference Built Index (NDBI) and land use interpretation approaches. With the use of GIS, the spatial distribution of urban vulnerabilities could be known through the overlay of each indicator. The results of the analysis obtained that the physical vulnerability classes of Yogyakarta City were very high (26%), high (29%), moderate (29%), low (16%), and very low (0%). Some areas categorized as vulnerable to very vulnerable are areas with high building density, namely the Sub-districts of Danurejan, Kraton, Gedongtengen, Jetis, Ngampilan, Pakualaman, Mantrijeron, parts of Gondomanan and Tegalrejo.

English

The northern part of the Southern rivers, which is the subject of this study, lies between 18° 54' and 14° 48' north latitude and 11° 30' and 12° 54' west longitude. It is characterized by a dense hydrographic network dominated by maritime marshes and expanses of salty lands and mangrove mudflats that have developed on a vast coastal plain. The objective of this study is to analyze spatio-temporal changes of the geomorphological units and to simulate their spatial behavior in 2035. The methodology is based on the processing of Landsat TM and Landsat OLI satellite images acquired in 1986 and 2018, respectively. Change detection is performed by post-classification comparison of the two previously validated land cover states with overall accuracies of 91% for the 1986 image and 97% for the 2018 image. The prediction made using the CA-Markov model yielded a result with 88% agreement. This made it possible to understand the spatio-temporal evolution of land use and changes in the near future (2035). The results of the land use prediction in 2035 show that bare land will have the highest gain with 93% probability while tans will decrease by 60% to other land use categories. Vegetation and mangroves will lose 47 and 44% of their areas respectively to bare soil and mudflats. A strong degradation of vegetation cover is predicted by the Markov chain by 2035. The probability of change in the area covered by water is the lowest during this period.   Key words: CA-Markov, landsat, land use change, predictive modeling, Southern rivers.

Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.

Cloud Processing for Simultaneous Mapping of Seagrass Meadows in Optically Complex and Varied Water

Improved development of remote sensing approaches to deliver timely and accurate measurements for environmental monitoring, particularly with respect to marine and estuarine environments is a priority. We describe a machine learning, cloud processing protocol for simultaneous mapping seagrass meadows in waters of variable quality across Moreton Bay, Australia. This method was adapted from a protocol developed for mapping coral reef areas. Georeferenced spot check field-survey data were obtained across Moreton Bay, covering areas of differing water quality, and categorized into either substrate or ≥25% seagrass cover. These point data with coincident Landsat 8 OLI satellite imagery (30 m resolution; pulled directly from Google Earth Engine’s public archive) and a bathymetric layer (30 m resolution) were incorporated to train a random forest classifier. The semiautomated machine learning algorithm was applied to map seagrass in shallow areas of variable water quality simultaneously, and a bay-wide map was created for Moreton Bay. The output benthic habitat map representing seagrass presence/absence was accurate (63%) as determined by validation with an independent data set.

Afforestation of Transformed Savanna and Resulting Land Cover Change: A Case Study of Zaria (Nigeria)

This paper presents the effects of actions aiming at tree planting, and thus protection of the land belonging to Ahmadu Bello University (ABU) in Zaria (Nigeria) from erosion. This area is located within the northern limits of the Guinea savanna belt, which has been considerably transformed primarily for agricultural purposes. For years this area has been subjected to the destructive surface and gully erosion processes, which were documented both by field work and the analysis of high-resolution images presented by Google Earth. Land erosion leads to the silting of an impounding reservoir (Kubanni)—the primary water source for the university campus and the basis for its existence. Since 2000, the university has been actively involved in tree-planting efforts in the campus vicinity, executed mainly through the establishment of small plantations ranging in area from approximately 1 ha to 56 ha. Today, these measures have brought tangible effects such as increment of tree-planted expanses, as evidenced in the multitemporal analysis of the Landsat 7 ETM+ and Landsat 8 OLI satellite images. The increasing planted area protects both the soil and the reservoir. A marked stand increment (almost doubled) has been recorded, which is evident from the satellite imageries, and is presented on the land-use map produced with GIS technique for this project.

Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon

We describe a new minimum extent, persistent surface water classification for reaches of four major rivers in the Peruvian Amazon (i.e., Amazon, Napo, Pastaza, Ucayali). These data were generated by the Peruvian Amazon Rural Livelihoods and Poverty (PARLAP) Project which aims to better understand the nexus between livelihoods (e.g., fishing, agriculture, forest use, trade), poverty, and conservation in the Peruvian Amazon over a 35,000 km river network. Previous surface water datasets do not adequately capture the temporal changes in the course of the rivers, nor discriminate between primary main channel and non-main channel (e.g., oxbow lakes) water. We generated the surface water classifications in Google Earth Engine from Landsat TM 5, 7 ETM+, and 8 OLI satellite imagery for time periods from circa 1989, 2000, and 2015 using a hierarchical logical binary classification predominantly based on a modified Normalized Difference Water Index (mNDWI) and shortwave infrared surface reflectance. We included surface reflectance in the blue band and brightness temperature to minimize misclassification. High accuracies were achieved for all time periods (>90%).

Detection of Urban Forest Change in Jabodetabek Megacity Using Sentinel 2 and Landsat 8 Imagery Through Google Earth Engine Cloud Computing Platform

The urban forest is a green area in urban areas overgrown with woody vegetation and provides physiological, sociological, economic, and aesthetic benefits for urban communities. Unfortunately, urban forest change is still happening everywhere, especially in Jabodetabek Megacity until now, and is a problem because the arrangement of cities is more in favor of economic interests. This is considered to be the main cause of runoff, flooding, and erosion which in turn causes reduced air supply and increased air pollution which can change the microclimate of urban areas. Research is needed to detect how many changes in urban forests in Jabodetabek Megacity are so that the changes can be controlled. This research is based on the application of the Google Earth Engine cloud computing platform with data sources in the form of Sentinel-2 MSI and Landsat-8 OLI satellite imagery. Classification of land use through the Random Forest algorithm method with the help of several indexes is carried out to obtain the area of the yard of the vegetation that is part of the urban forest. The results of this study are expected as recommendations and considerations in making policies to manage urban forests and minimize the impact of urban communities.

Determining the Effectiveness of Using Remote Sensing Indices to Deriving Sabkhas in Wadi An-Natrun Depression – Egypt

Abstract The floor of Wadi An-Natrun Depression has a high level of salinity and occupies by a shallow salt lakes, which are characteristic geomorphologic landforms. In general, Wadi An-Natrun Depression is characterized by very arid climate. The geological formations of Wadi An-Natrun Depression range from lower Miocene, lower Pliocene and Quaternary deposits. This study attempts to determine the effectiveness of using remote sensing indicators in deducing sabkhas in the Wadi El-Natrun depression utilizing Landsat 8 OLI satellite images based on field data of laboratory analyzes of sabkhas soil samples collected through the field study. To achieve this, linear and multiple regression models were applied between soil salinity and electrical conductivity (ECe) on the one hand and remote sensing indices on the other hand, where the NDSI index had the highest correlation coefficient of 0.872. This was followed by the derivation of the Enhanced soil salinity index from (NIR), (SWIR) 2 and the NDVI index, which had higher correlation than NDSI with electrical conductivity (ECe) of 0.918 . المستخلص باللغة العربية يتميز قاع منخفض وادي النطرون بمستوى عال من الملوحة وتحتله بحيرات ملحية ضحلة والتي تعد من أهم المظاهر الجيمورفولوجية بالمنخفض ، وبصفة عامة فإن مناخ منخفض وادي النطرون يتسم بشدة الجفاف ، تتراوح التکوينات الجيولوجية بالمنخفض بين الميوسين الأسفل والبليوسين الاسفل بالإضافة إلي رواسب البليستوسين والحديث . تحاول هذه الدراسة تحديد فاعلية استخدام مؤشرات الاستشعار عن بعد في استنباط السبخات بمنخفض وادي النطرون باستخدام صور القمر الصناعي لاندسات 8 OLI إرجاعا إلي بيانات فعلية للتحليلات المعملية لعينات تربة السبخات والتي تم جمعها من خلال الدراسة الميدانية . ولتحقيق ذلک ، تم تطبيق نماذج الانحدار الخطي والمتعدد بين ملوحة التربة والتوصيل الکهربائي (ECe) من ناحية ومؤشرات الاستشعار عن بعد من ناحية اخري ، حيث کان لمؤشر NDSI أعلى معامل ارتباط قدره 0.872. ، تبي ذلک اشتقاق مؤشر ملوحة التربة المحسن بالاعتماد علي النطاقات الطيفية (NIR) و (SWIR) 2ومؤشر NDVI ، و کان لهذا المؤشر ارتباط أعلى من NDSI مع التوصيل الکهربائي (ECe) بلغ 0.918.

Spatial change detections and inventory of wetlands in Yenagoa Urban Area: Bayelsa State, Nigeria

Yenegoa Town has in recent years witnessed rapid City growth and Urban development and much of these developments are unplanned and unregulated. This has seriously impacted on wetlands in several locations of the town as persistent Wetlands reclamations are being witnessed in study area. This prompted the need for the study which is aimed to map wetlands location in Yenagoa’s urban area using GIS and Remote Sensing approach. The study analyzes land use/land cover changes (LULC) using LANDSAT(5) TM, LANDSAT(5) ETM and LANDSAT(7) OLI satellite imageries of 1990, 2000, 2010 and 2020 respectively. Through this study, the pattern of urban expansion for Thirty years were been studied. The satellite imageries covering the area were acquired and analyzed using ArcGIS 10.1 and ENVI 5.0 software. The supervised image classification method was adopted and the classification results were validated using the Kappa Index of Agreement (KIA) yielding an accuracy of 0.69m for year 1990, 0.62m for year 2000, 0.58m for year 2010 and 0.73m for 2020. A total area of 13,741.4 hectares was delineated in the study area which is identified as Yenagoa’s urban area. After processing the imageries, four land use/land cover (LULC) classes where considered, and the results shows that Built-up area continuously increased in land area from 1990 -2020 with total percentage change of 273.31% (4,178.7ha) and total annual rate of change of 25.33. Vegetation have total percentage change of 38.55% (974.34Ha) and total annual rate of change of 3.85, wetland cover loss with total percentage Change of 61.96% (-51,44.99ha) and total annual rate of change of -6.19ha, and the water body have loss of total percentage of -2.16% (-8.05Ha) and total annual rate of change of -0.22ha wetland at Yenegwe loss by Total %change of -29.918% ( -197.95ha), and wetland at Igbogene loss by total percentage change of -36.028% (-358.7ha). The research findings also revealed that the wetlands in Anyama, Swali, Kpansia and Opolo Towns were completely lost from the third Epoch of 2010, this may be as a result of persistence reclamation of wetland in this parts of the study area. The Markov Chain predicted model were utilized for predicting the likely changes in land use land cover for a period of thirty years. The predicted results also indicates that wetland size of 32.47,%, 30.68% and 28.99% may likely be lost by the year 2030, 2040 and 2050 respectively in study area if no action is taking by concerned authorities to forestall the factors responsible for the lost in wetland. The study justified the dynamics of remote sensing and GIS techniques in modeling wetlands changees over these periods, wise use of wetland resources and improvement of institutional arrangement were recommended so that wetland policies can be fully integrated into the planning process across all disciplines.

Stability evaluation of tidal flats based on time-series satellite images: A case study of the Jiangsu central coast, China

Systematic evaluation of tidal-flat stability is vital for coastal protection and management. However, due to the complex geomorphology and variable sediment dynamics of tidal flats, there are few studies on this topic. In this study, the stability of tidal flats along the Jiangsu Province's central coast in China was analyzed using a cumulative change frequency (CCF) method. Tidal creeks were extracted from 131 images at low tidal levels registered by Landsat-8 OLI, Sentinel-2 MSI, GF-1 WFV, and HJ-1 CCD satellites between 2009 and 2019. The results indicated that MSI images could identify five levels of tidal creeks, and the number of tidal creeks extracted was the greatest. The extraction results from OLI and WFV images were similar, and they can identify four, whereas only large-scale and medium-scale tidal creeks can be identified from CCD images. The area of stable tidal flats was estimated to be ∼202 km2, mainly near-shore and the inner part of several major tidal channels, representing ∼23.0% of the total area. In comparison, the unstable tidal flats were estimated to be 75 km2, accounting for ∼8.5% of the total area, mainly located in the middle and lower sections with frequent migration in tidal creeks. The tidal-creek system oscillation period increases from land to sea, roughly two years for the salt-marsh zone and three years for the lower intertidal zone. These findings are essential to promote the understanding of tidal-flat evolution and support the practical guidelines for coastal and offshore management.

Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration.