Centennial loss of lake wetlands in the Yangtze Plain, China: Impacts of land use changes accompanied by hydrological connectivity loss

Humans have played a fundamental role in altering lake wetland ecosystems, necessitating the use of diverse data types to accurately quantify long-term changes, identify potential drivers, and establish a baseline status. We complied high-resolution historical topographic maps and Landsat imagery to assess the dynamics of the lake wetlands in the Yangtze Plain over the past century, with special attention to land use and hydrological connectivity changes. Results showed an overall loss of 45.6 % (∼11,859.5 km2) of the lake wetlands over the past century. The number of lakes larger than 10 km2 decreased from 149 to 100 due to lake dispersion, vanishing, and shrinkage. The extent of lake wetland loss was 3.8 times larger during the 1930s-1970s than that in the 1970s-1990s. Thereafter, the lake wetland area remained relatively stable, and a net increase was observed during the 2010s-2020s in the Yangtze Plain. The significant loss of lake wetland was predominately driven by agricultural activities and urban land expansion, accounting for 81.1 % and 4.9 % of the total losses, respectively. In addition, the changes in longitudinal and lateral hydrological connectivity further exacerbated the lake wetland changes across the Yangtze Plain through isolation between lakes and the Yangtze River and within the lakes. A total of 130 lakes have been isolated from the Yangtze River due to the construction of sluices and dykes throughout the Yangtze Plain, resulting in the decrease in the proportion of floodplain marsh from 28.3 % in the 1930s to 8.0 % in the 2020s. Furthermore, over 260 sub-lakes larger than 1 km2 (with a total area of 1276.4 km2) are experiencing a loss of connectivity with their parent lakes currently. This study could provide an improved historical baseline of lake wetland changes to guide the conservation planning to wetland protection and prioritization area in the Yangtze Plain.

Centennial dynamics of floodplain wetland in the largest freshwater lake in China: Implications on floodplain lake restoration

Long-term mapping of floodplain wetland dynamics is fundamental for wetland protection and restoration, but it is restricted to decadal scales using satellite observations owing to scarcity of spatial data over long-term scales. The present study concentrates on the centennial dynamics of floodplain wetland in Poyang Lake, the largest freshwater lake in China. Historical topographic maps and Landsat imagery were combined to reconstruct the centennial floodplain wetland map series. A robust random forest algorithm for the land cover classification was used to investigate the conversion of the floodplain wetland to other land cover types and quantify the magnitude of the influence of hydrological disconnection over the past century. Results show that the Poyang Lake floodplain wetland experienced a net loss of 35.7 %, from 5024.3 km2 in the 1920s-1940s to 3232.1 km2 in the 2020s, with the floodplain wetland loss occurring mostly from the 1950s to the 1970s. In addition, agricultural encroachment was identified as the predominant driver of floodplain wetland loss, with a total area of 931.0 km2 of the floodplain wetland converted into cropland. Furthermore, approximately 600 km2 of sub-lakes (larger than 1 km2) became isolated from the floodplain and thus unaffected by seasonal flood pulses, which highlights the need to account for the impact of hydrological disconnection on floodplain wetland dynamics. This study indicated the combination of historical maps and satellite observations as an effective tool to track long-term wetland changes. The resultant dataset provides an extended baseline and could shed some light on floodplain wetland conservation and restoration.

Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series

Surface water plays a crucial role in the ecological environment and societal development. Remote sensing detection serves as a significant approach to understand the temporal and spatial change in surface water series (SWS) and to directly construct long-term SWS. Limited by various factors such as cloud, cloud shadow, and problematic satellite sensor monitoring, the existent surface water mapping datasets might be short and incomplete due to losing raw information on certain dates. Improved algorithms are desired to increase the completeness and quality of SWS datasets. The present study proposes an automated framework to detect SWS, based on the Google Earth Engine and Landsat satellite imagery. This framework incorporates implementing a raw image filtering algorithm to increase available images, thereby expanding the completeness. It improves OTSU thresholding by replacing anomaly thresholds with the median value, thus enhancing the accuracy of SWS datasets. Gaps caused by Landsat7 ETM + SLC-off are respired with the random forest algorithm and morphological operations. The results show that this novel framework effectively expands the long-term series of SWS for three surface water bodies with distinct geomorphological patterns. The evaluation of confusion matrices suggests the good performance of extracting surface water, with the overall accuracy ranging from 0.96 to 0.97, and user's accuracy between 0.96 and 0.98, producer's accuracy ranging from 0.83 to 0.89, and Matthews correlation coefficient ranging from 0.87 to 0.9 for several spectral water indices (NDWI, MNDWI, ANNDWI, and AWEI). Compared with the Global Reservoirs Surface Area Dynamics (GRSAD) dataset, our constructed datasets promote greater completeness of SWS datasets by 27.01%-91.89% for the selected water bodies. The proposed framework for detecting SWS shows good potential in enlarging and completing long-term global-scale SWS datasets, capable of supporting assessments of surface-water-related environmental management and disaster prevention.

Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988-2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.

Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, the utilization of remotely sensed data to assess the effectiveness of machine learning algorithms (MLAs) for LULC classification and change detection analysis has been limited. This study monitors and analyzes LULC changes in the study area from 1991 to 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). The results showed that the RF algorithm produced the most accurate maps of the three-decade study period, accompanied by a high kappa coefficient (0.93-0.97) compared with the SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), and XGBoost (0.92-0.95) algorithms. Consequently, the RF classifier was implemented to categorize all obtainable satellite images. Socioeconomic changes throughout these transition periods were revealed by the change detection results. Rangeland and barren land areas decreased by 11.33 % (-402.03 km2) and 6.68 % (-236.8 km2), respectively. The transmission increases of 13.54 % (480.18 km2), 3.43 % (151.74 km2), and 0.71 % (25.22 km2) occurred in agricultural land, forest, and built-up areas, respectively. The outcomes of this study contribute significantly to LULC monitoring in developing regions, guiding stakeholders to identify vulnerable areas for better land use planning and sustainable environmental protection.

Analyze of spatial extent and current condition of land use land cover dynamics for the period 1990-2020 Wayu-Tuka district, western Ethiopia

LULC variation has increased in many parts of the world recent years. Analyzing LULC is valuable to ability to grasp for spatial extent, patterns and impacts of the dynamics. This research examines the magnitudes and trends of LULC dynamics of Wayu-Tuka District, Western Ethiopia for a period of 1990-2020. Data were acquired from Landsat images (i.e, TM from 1990 to 2000, ETM+ from 2010 and OLI 2020). LULC classes were classified (from Landsat images) to develop land use land cover change maps for the study area. Landsat images were grouped via supervised classification method and maximum likelihood classifier (MLC). Accuracy scores and kappa a coefficient was used to confirm the accuracy categorized for LULC classes. Forest, settlement area, cultivated area, water body, and bare land are the main land use land cover categories identified in the study area. At the study district, forest coverage decreased progressively within the past three decades (1990-2020) from 12.4% in 1990 to 2.6% in 2020. The settlements, cultivated lands and water bodies have been explained by a average rate of 0.41% per year and forest land has been reduced by 0.33% per year. The study identified the major drivers of land use/land cover dynamics such as expansion of agricultural land, extraction of fuel woodland, illegal settlements and illegal logging was the key factors of LULC changes in the field of the study. In expressions of historical and current LULC, the analysis indicated that in the three decades years' viewpoint; changes in agriculture land expansion and expansion of settlement land have had a strong impact on the LULC dynamics. The few remaining forest area coverage of the District shall be completely vanished unless measures are taken to curb these declining trends. Therefore, relevant stakeholders should take integrated actions to rehabilitate degraded landscapes through afforestation and reforestation programmes.

A quantitative analysis of multi-decadal shoreline changes along the East Coast of South Korea

South Korea's east coast is facing several issues related to coastal erosion because of sea-level rise, typhoon-induced storm surges, and various coastal development projects. In recent decades, high storm waves have frequently appeared on the east coast, causing casualty, beach erosion, and coastal infrastructure damage, drawing significant public attention. Thus, we analyzed the multi-decadal shoreline changes to understand the coastal dynamics and the forces responsible for the spatio-temporal changes along the 173 km coastline. The shorelines covering 38 years between 1984 and 2022 were derived from Landsat images and the change statistics, i.e., linear regression rate (LRR), endpoint rate (EPR), weighted linear regression (WLR), and net shoreline movement (NSM), were calculated at a 100 m alongshore intervals using Digital Shoreline Analysis System (DSAS), revealed several distinct behaviors of shoreline position. The long-period (1984-2022) assessment showed an average shoreline change rate (LRR) of 0.17 m/year with an estimated mean erosion and deposition rate of -0.57 and 2.07 m/year, respectively. The long-term surface gain and loss of the backshore region exhibited that the net surface gain of the east coast is 421.13 ha, and the net loss is 181.82 ha. The assessment of decadal shoreline changes showed a cyclic pattern of erosion (from 1984-1990 and 1999-2010) and accretion (from 1990-1999 and 2010-2022). Furthermore, a secondary level of investigation was conducted to address a wider variety of coastal behaviors by segmenting shoreline change rates based on coast types and average slopes along coastlines. It was observed that the frequent coastal deformation is associated with a flatter beach compared to a steep one. This study found that the artificial structures constructed along the east coast have not completely solved or stopped the erosion issues but shifted it from one location to another. The analysis of local and regional shoreline changes had shown that typhoon-induced storm surges, high storm waves, and anthropogenic activities like encroachment and the development of artificial coastal structures were the primary drivers of coastline changes along the east coast. Finally, we proposed a decision-making classification scheme that can be used to determine the mechanism of decision for protective and preventive measures against further coastal deterioration.

Accuracy of pixel-based classification: application of different algorithms to landscapes of Western Iran

Scenarios for monitoring land cover on a large scale, involving large volumes of data, are becoming more prevalent in remote sensing applications. The accuracy of algorithms is important for environmental monitoring and assessments. Because they performed equally well throughout the various research regions and required little human involvement during the categorization process, they appear to be resilient and accurate for automated, big area change monitoring. Malekshahi City is one of the important and at the same time critical areas in terms of land use change and forest area reduction in Ilam Province. Therefore, this study aimed to compare the accuracy of nine different methods for identifying land use types in Malekshahi City located in Western Iran. Results revealed that the artificial neural network (ANN) algorithm with back-propagation algorithms could reach the highest accuracy and efficiency among the other methods with kappa coefficient and overall accuracy of approximately 0.94 and 96.5, respectively. Then, with an overall accuracy of about 91.35 and 90.0, respectively, the methods of Mahalanobis distance (MD) and minimum distance to mean (MDM) were introduced as the next priority to categorize land use. Further investigation of the classified land use showed that good results can be provided about the area of the land use classes of the region by applying the ANN algorithm due to high accuracy. According to those results, it can be concluded that this method is the best algorithm to extract land use maps in Malekshahi City because of high accuracy.

Land use land cover change and socio-economic activities in the Burunge Wildlife Management Area ecosystem during COVID-19

Land use land cover change (LULCC) is among the major factors affecting the natural environment worldwide. Studying LULCC is essential as it contributes to natural resource management, biodiversity conservation, and land use planning, especially during pandemics such as COVID-19. This study aimed at assessing the trend (1995-2021) and magnitude of LULCC in the Burunge WMA ecosystem before (2015-2018) and during COVID-19 (2018-2021). The data on LULCC were collected from the satellite imagery on the USGS website, whereas the data on perceptions of local communities on LULCC from Mwada, Kakoi and Maweni villages were collected through a household questionnaire survey (HQS) of 445 randomly sampled households, focused group discussions (FGDs) and key informant interviews (KIIs). Quantitative data were analyzed using MS Excel 2019, R software (2022.02.0 + 443) and ArcGIS (Version 10.8). Qualitative data were analyzed using content analysis techniques. The findings indicated a fluctuation in agriculture, forest, and water coverage. For instance, agriculture and settlements increased significantly by 23.91% in 2015-2021 and 5.71% in 1995-2005 respectively, whereas forested land showed a maximum increase of 7.33% in 1995-2005. However, there was a pronounced increase in agricultural lands (3.99%) during the COVID-19 phase as compared to the same time frame before the pandemic. Local communities pointed to agriculture and settlements as the major activities contributing to LULCC. The findings show significant LULCC in Burunge WMA which calls for special attention from responsible authorities and other stakeholders for the achievement of biodiversity conservation and the development of livelihoods in the area.

The spatial distribution and expansion of Eucalyptus in its hotspots: Implications on agricultural landscapes

Fast coppicing plantations like Eucalyptus are becoming an ever increasingly important land use system globally, including the Eucalyptus hotspot highlands of Northwestern Ethiopia. However, comprehensive information regarding species composition is essential for proper planning and policy decisions. The current study mapped the spatial distribution of Eucalyptus globulus (hereafter referred to as Eucalyptus) and identified the key push factors for its expansion. The study used a mapping procedure that uses Landsat imagery together with ground truth data based on supervised training of a pixel-by-pixel classification algorithm within image regions to distinguish areas of Eucalyptus plantations from other classes. High-resolution multispectral and multi-temporal remote-sensing images were combined with ground truth data to produce robust features of Eucalyptus plantation distribution maps. Heckman's Two-Stage econometric model was also employed for determining the major driving factors of Eucalyptus expansion. The results of the mapping algorithm were Eucalyptus plantation distribution maps of 30 × 30 m resolution that showed temporal changes from 1999 to 2021. The findings revealed that Eucalyptus coverage increased by 55% during the period from 1999 to 2010 and the change expressively increased to 69% in 2021 with respect to the reference period. The study also found that a number of push factors influenced the size of land planted with Eucalyptus. The developed maps showing the spatial distribution and expansion of Eucalyptus will help policymakers properly manage the ecosystems and agricultural landscapes of Eucalyptus growing areas.

Variation of satellite-derived total suspended matter in large lakes with four types of water storage across the Tibetan Plateau, China

Total suspended matter (TSM), as an indicator of the concentration of fine materials in the water column including particulate nutrients, pollutants, and heavy metals, is widely used to monitor aquatic ecosystems. However, the long-term spatiotemporal variations of TSM in lakes across the Tibetan Plateau (TP) and their response to environmental factors are rarely explored. Accordingly, taking advantage of the Landsat top-of-atmosphere reflectance and in-situ data, an empirical model (R² = 0.83, RMSE = 1.08 mg/L, and MAPE = 19.49 %) was developed to estimate the average autumnal TSM in large TP lakes (≥50 km²) during the 1990-2020 period. For analyzing the spatiotemporal variability in TP lakes TSM, the examined lakes were classified into four types (Type A-D) based on their water storage changing in different periods. The results showed that the lakes in the southern and some northeastern parts of the TP exhibited lower TSM values than those situated in other regions. The assessment of TSM in each of these four lake types showed that more than half of them had a TSM value of <20 mg/L. Apart from Type D, the lakes with the TSM showing significantly decreasing trends were dominantly Types A-C. A relative contribution analysis involving five driving factors indicated that they contributed by >50 % to lake TSM interannual variation in 73 out of 114 watersheds, and the lakes area change demonstrated the greatest contribution (82.2 %), followed by wind speed (11.0 %). Further comparison between the entire lake and the non-expansive regions suggested that the expansive region played an indispensable role in determining the TSM value of the whole lake. This study can help to better understand the water quality condition and provide valuable information for policy-makers to maintain sustainable development in the TP region.

Long-term water quality assessments under changing land use in a large semi-arid catchment in South Africa

Increasing nutrient loads from land use and land cover (LULC) change degrade water quality through eutrophication of aquatic ecosystems globally. The Vaal River Catchment in South Africa is an agriculturally and economically important area where eutrophication has been a problem for decades. Effective mitigation strategies of eutrophication in this region require an understanding of the relationship between LULC change and water quality. This study assessed the long-term impacts of LULC changes on nitrate (NO₃-N) and orthophosphate (PO₄-P) pollution in the lower Vaal River Catchment between 1980 and 2018. Multi-year LULC was mapped from Landsat imagery and changes were determined. Long-term trends in NO₃-N and PO₄-P loads and concentrations in river water samples were analysed, while multi-year LULC data were ingested into the Soil and Water Assessment Tool (SWAT) to simulate the impacts of LULC changes in NO₃-N and PO₄-P loads. Main LULC changes included an increase in the irrigated area by 262% and in built-up area by 33%. This occurred at the expense of cultivated dryland fields and rangelands. In situ data analysis showed that at the catchment inlet, PO₄-P concentration and loads significantly increased, while NO₃-N concentration and loads decreased between 1980 and 2018. At the catchment outlet, only PO₄-P loads increased, while NO₃-N loads and concentrations remained the same. SWAT simulations at the Hydrologic Response Unit scale showed that irrigated land was the largest contributor to NO₃-N leaching per ha. Aggregation of nutrient loads by LULC type showed increased nutrient loads from irrigated and built-up areas over time, while loads from dryland areas decreased. At catchment scale, dryland remained an important contributor of the annual nutrient loads total because of its large area. In future, research efforts should focus on crop management practices to reduce nutrient loads.

Urban morphology detection and it's linking with land surface temperature: A case study for Tehran Metropolis, Iran

Expansion of urban areas and alteration of natural land cover exacerbate the local climate change. To find out the effect of land cover changes on the local climate, in this study, the Local Climate Zone (LCZ) concept was utilized to detect urban morphology in Tehran Metropolis. LCZ and Land Surface Temperature (LST) can be identified and classified based on available remote sensing products. Firstly, LCZ maps of Tehran metropolis were extracted using Landsat imagery, and secondly, relationships between LCZ and LST were explored for three years (1990, 2004, and 2018). We found that Tehran urban structure has 13 LCZs based on imagery from Landsat 5 and 14 LCZs based on images for Landsat 7 and 8. Overall accuracy and kappa coefficient were estimated at 62% and 0.60, respectively. Results show that built-up classes including compact high-rise, compact mid-rise, and heavy industrial areas tended to increase the surface temperature, while except for bare land, all other land cover types tended to decrease the surface temperature. The findings also suggest that complementary optical and thermal remote sensing data, such as the combination of OLI with TIRS imageries, were sufficient for supervised LCZ and LST classification in a semi-arid region of Tehran metropolis.

Haiti has more forest than previously reported: land change 2000-2015

Estimates of forest cover have important political, conservation, and funding implications, but methods vary greatly. Haiti has often been cited as one of the most deforested countries in the world, yet estimates of forest cover range from <1% to 33%. Here, we analyze land change for seven land cover classes (forest, shrub land, agriculture/pasture, plantation, urban/infrastructure, barren land, and water) between 2000 and 2015 using Landsat imagery (30 m resolution) in the Google Earth Engine platform. Forest cover was estimated at 26% in 2000 and 21% in 2015. Although forest cover is declining in Haiti, our quantitative analysis resulted in considerably higher forest cover than what is usually reported by local and international institutions. Our results determined that areas of forest decline were mainly converted to shrubs and mixed agriculture/pasture. An important driver of forest loss and degradation could be the high demand for charcoal, which is the principal source of cooking fuel. Our results differ from other forest cover estimates and forest reports from national and international institutions, most likely due to differences in forest definition, data sources, spatial resolution, and methods. In the case of Haiti, this work demonstrates the need for clear and functional definitions and classification methods to accurately represent land use/cover change. Regardless of how forests are defined, forest cover in Haiti will continue to decline unless corrective actions are taken to protect remaining forest patches. This can serve as a warning of the destructive land use patterns and can help us target efforts for better planning, management, and conservation.

[Assessing fractional vegetation cover changes and ecological quality of the Wuyi Mountain National Nature Reserve based on remote sensing spatial information]

The Wuyi Mountain National Nature Reserve (WYS), established in 1979, is the largest and most intact subtropical forest ecosystem in southeastern China. No study has assessed the vegetation coverage change along with its ecological effect after the protection of the reserve for almost 40 years. In this study, the NDVI data of Landsat Image was corrected using the NDVI data of MODIS, the fractional vegetation cover (FVC) and the remote sensing based ecological index (RSEI) were calculated to assess the change of FVC and ecological quality in WYS with five Landsat images representing a period from 1979 to 2017. The results showed that after protection for nearly 40 years the FVC of the reserve had been significantly increased from 73.6% in 1979 to 89.5% in 2017, which consequently improved ecological quality from 0.801 in 1988 to 0.823 in 2017. In 2017, the area with the good and excellent ecological quality grades accounted for 98.7% of the total. Spatially, the ecologically-improved areas mainly distributed in the northeast core area and the center of the southwest core area. The ecologically-declined areas mostly occurred along roadsides and peaks. Vertically, the highest FVC and ecological quality areas distributed in the elevations between 1300-1900 m. In general, the improvement of FVC and ecological quality in the Wuyi Mountain National Nature Reserve was due largely to the effective policies and the successful protection by local government and people, except for some special year that may be affected mainly by climate conditions.

Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data

The assessment of burn severity is highly important in order to describe and measure the effects of fire on vegetation, wildlife habitat and soils. The estimation of burn severity based on remote sensing is a powerful tool that, to be useful, needs to be related and validated with field data. The present paper explores the relationships between field accessible variables and Relative Differenced Normalized Burn Ratio (RdNBR) index by using linear mixed-effects models and boosted regression trees, based on data from 28 large fires and 668 field measurements across three countries in southern Europe. The RdNBR clearly reflected the mean height of charred stem and loss of ligneous, living shrub and tree cover during the fire. The paper confirms that remote sensing indices provide an acceptable assessment of fire induced impact on forest vegetation but also highlights there are important between-fire variations due to specific contexts that modify these relationships. These variations can be effectively assessed and should be taken into account in future predictive efforts.

Shoreline change assessment using multi-temporal satellite images: a case study of Lake Sapanca, NW Turkey

The research summarized here determines historical shoreline changes along Lake Sapanca by using Remote Sensing (RS) and Geographical Information Systems (GIS). Six multi-temporal satellite images of Landsat Multispectral Scanner (L1-5 MMS), Enhanced Thematic Mapper Plus (L7 ETM+), and Operational Land Imager Sensors (L8 OLI), covering the period between 17 June 1975 and 15 July 2016, were used to monitor shoreline positions and estimate change rates along the coastal zone. After pre-possessing routines, the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and supervised classification techniques were utilized to extract six different shorelines. Digital Shoreline Analysis System (DSAS), a toolbox that enables transect-based computations of shoreline displacement, was used to compute historical shoreline change rates. The average rate of shoreline change for the entire cost was 2.7 m/year of progradation with an uncertainty of 0.2 m/year. While the great part of the lake shoreline remained stable, the study concluded that the easterly and westerly coasts and deltaic coasts are more vulnerable to shoreline displacements over the last four decades. The study also reveals that anthropogenic activities, more specifically over extraction of freshwater from the lake, cyclic variation in rainfall, and deposition of sediment transported by the surrounding creeks dominantly control spatiotemporal shoreline changes in the region. Monitoring shoreline changes using multi-temporal satellite images is a significant component for the coastal decision-making and management.

Spatiotemporal estimation of air temperature patterns at the street level using high resolution satellite imagery

Although meteorological monitoring stations provide accurate measurements of Air Temperature (AT), their spatial coverage within a given region is limited and thus is often insufficient for exposure and epidemiological studies. In many applications, satellite imagery measures energy flux, which is spatially continuous, and calculates Brightness Temperature (BT) that used as an input parameter. Although both quantities (AT-BT) are physically related, the correlation between them is not straightforward, and varies daily due to parameters such as meteorological conditions, surface moisture, land use, satellite-surface geometry and others. In this paper we first investigate the relationship between AT and BT as measured by 39 meteorological stations in Israel during 1984-2015. Thereafter, we apply mixed regression models with daily random slopes to calibrate Landsat BT data with monitored AT measurements for the period 1984-2015. Results show that AT can be predicted with high accuracy by using BT with high spatial resolution. The model shows relatively high accuracy estimation of AT (R²=0.92, RMSE=1.58°C, slope=0.90). Incorporating meteorological parameters into the model generates better accuracy (R²=0.935) than the AT-BT model (R²=0.92). Furthermore, based on the relatively high model accuracy, we investigated the spatial patterns of AT within the study domain. In the latter we focused on July-August, as these two months are characterized by relativity stable synoptic conditions in the study area. In addition, a temporal change in AT during the last 30years was estimated and verified using available meteorological stations and two additional remote sensing platforms. Finally, the impact of different land coverage on AT were estimated, as an example of future application of the presented approach.

Assessing and monitoring the risk of land degradation in Baragan Plain, Romania, using spectral mixture analysis and Landsat imagery

The fall of the communist regime in Romania at the end of 1989 and the ensuing transition to the market economy brought about many changes in the use of agricultural land. These changes combined with the action of climatic factors led, in most cases, to negative effects increasing the risk of degradation of agricultural land. This study aims to assess and monitor the risk of land degradation in Baragan Plain, Romania, for the period 1988-2011 using Landsat Thematic Mapper (TM) and Spectral Mixture Analysis (SMA). Each satellite image was classified through the Decision Tree Classifier (DTC) method; then, on the basis of certain threshold values, we obtained maps of land degradation and maps showing the passage from various classes of land use/land cover (LULC) to land degradation. The results indicate that during the intermediary periods there was an ascending and descending trend in the risk of land degradation determined by the interaction of climatic factors with the social-economic ones. For the entire period, the overall trend was ascending, the risk of land degradation increasing by around 4.60 % of the studied surface. Out of the climatic factors, high temperatures and, implicitly, drought were the most significant. The social-economic factors are the result of the changes which occurred after the fall of the communist regime, the most important being the fragmentation of agricultural land and the destruction of the irrigation system.

Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey

Mapping land use/land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively effect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. In this study, LULC changes are investigated by using of Remote Sensing and Geographic Information Systems (GIS) in Rize, North-East Turkey. For this purpose, firstly supervised classification technique is applied to Landsat images acquired in 1976 and 2000. Image Classification of six reflective bands of two Landsat images is carried out by using maximum likelihood method with the aid of ground truth data obtained from aerial images dated 1973 and 2002. The second part focused on land use land cover changes by using change detection comparison (pixel by pixel). In third part of the study, the land cover changes are analyzed according to the topographic structure (slope and altitude) by using GIS functions. The results indicate that severe land cover changes have occurred in agricultural (36.2%) (especially in tea gardens), urban (117%), pasture (-72.8%) and forestry (-12.8%) areas has been experienced in the region between 1976 and 2000. It was seen that the LULC changes were mostly occurred in coastal areas and in areas having low slope values.