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Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. [ABSTRACT FROM AUTHOR]

Land cover changes (LCCs) due to urbanization cause urban heat islands (UHIs), significantly affecting land surface temperature (LST) through spatiotemporal changes in compositions, parameters, and patterns. Land cover and LST have been studied in various cities; however, indicative research into heterogeneous LCC's impact on LST in less-developed cities remains incomplete. This study analyzed new Landsat images of Zhanjiang, taken from 2004 to 2022, to determine the impact of three LCC indicators (compositions, parameters, and patterns) on LSTs. The urban thermal field variance index (UTFVI) was used to describe the distribution and variation in LST. We also quantified the cooling or warming benefits of various LCCs. The results indicate that the average temperature in the land urban heat island (SUHI) area rose to 30.6 °C. The average temperature of the SUHI was 3.32 °C higher than that of the non-SUHI area, showing the characteristic of shifting to counties and multi-core development. The LST increases by 0.37–0.67 °C with an increase of 0.1 in the normalized difference building index (NDBI), which is greater than the cooling benefit of the normalized difference of vegetation index (NDVI). The impact of landscape pattern indices on impervious surfaces and water is higher than that on vegetation and cropland, with a rising influence on impervious surfaces and a decreasing impact on water. The predominant cooling patches are vegetation and water, while large areas of impervious surface and cropland aggravate UHIs for industrial and agricultural activities. These findings are intended to guide future urban layouts and planning in less-developed cities, with thermal climate mitigation as a guiding principle. [ABSTRACT FROM AUTHOR]

Drought has become more common problem and more severe that widespread over the world in recent decades, increasing their negative effects on the environment. The most vulnerable communities can be warned and prepared for the negative effects of droughts. This study aims to assess and predict the drought vulnerability using Landsat satellite images from 2014 to 2022 over El- Dammam City. Using remote sensing, drought phenomenon was simulated and predicted through Perpendicular drought index (PDI) and Normalized Difference Drought Index (NDDI). In the present study, some indices were selected to sasses vegetation as Normalized Difference Vegetation Index (NDVI), water as Normalized Difference Water Index (NDWI) and salinity as salinity index (SI) and Normalized Difference Salinity Index (NDSI) with the aid of Machine Learning (ML) techniques, Support Vector Machine (SVM) and Partial Least Square (PLS). Results showed that NDDI and PDI were negatively correlated with NDVI and NDWI; respectively where NDDI and PDI were positively correlated with NDSI and SI; respectively. Principal Component Analysis (PCA), PC-1, PC-2 and PC-3 are contributed about 98% of all data. Finally, PLS and SVM are two good multivariate analysis for prediction of drought with R2 0.95 and 0.98 respectively. The predicted model showed the severity of drought far away from the sea along the barren area in the southern part. Finally, it should be noted that NDVI, NDSI, SI, and NDWI were better indicators for tracking and assessing changes in drought. The results can be utilized to quickly evaluate data produced from remote sensing and water-related indices and make wise decisions. This study contributes to our understanding of droughts and sheds information on how ML technology could be used to monitor droughts. [ABSTRACT FROM AUTHOR]

Satellite imagery is the only feasible approach to annual monitoring and reporting on land cover change. Unfortunately, conventional pixel-based classification methods based on spectral response only (e.g., using random forests algorithms) have shown a lack of spatial and temporal stability due, for instance, to variability between individual pixels and changes in vegetation condition, respectively. Machine learning methods that consider spatial patterns in addition to reflectance can address some of these issues. In this study, a convolutional neural network (CNN) model, U-Net, was trained for a 500 km × 500 km region in southeast Australia using annual Landsat geomedian data for the relatively dry and wet years of 2018 and 2020, respectively. The label data for model training was an eight-class classification inferred from a static land-use map, enhanced using forest-extent mapping. Here, we wished to analyse the benefits of CNN-based land cover mapping and reporting over 34 years (1987–2020). We used the trained model to generate annual land cover maps for a 100 km × 100 km tile near the Australian Capital Territory. We developed innovative diagnostic methods to assess spatial and temporal stability, analysed how the CNN method differs from pixel-based mapping and compared it with two reference land cover products available for some years. Our U-Net CNN results showed better spatial and temporal stability with, respectively, overall accuracy of 89% verses 82% for reference pixel-based mapping, and 76% of pixels unchanged over 33 years. This gave a clearer insight into where and when land cover change occurred compared to reference mapping, where only 30% of pixels were conserved. Remaining issues include edge effects associated with the CNN method and a limited ability to distinguish some land cover types (e.g., broadacre crops vs. pasture). We conclude that the CNN model was better for understanding broad-scale land cover change, use in environmental accounting and natural resource management, whereas pixel-based approaches sometimes more accurately represented small-scale changes in land cover. [ABSTRACT FROM AUTHOR]

Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g. , dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. [ABSTRACT FROM AUTHOR]

Vietnam is the world's second-largest coffee exporter. Information on coffee-growing areas is thus important for production estimation. This research developed an approach for coffee mapping and change detection using Landsat and DEM data in Central Highlands, Vietnam. The data were processed for 1995, 2005, and 2020 using random forests (RF). The mapping results, verified with reference data, showed overall accuracy and Kappa coefficient higher than 86.9% and 0.74, respectively. These findings were reaffirmed by a close relationship between the satellite-based coffee area and official statistics, with the root mean squared percentage error (RMSPE) and mean absolute percentage error (MAPE) greater than 9.9% and 7.6%, respectively. From 1995 to 2020, the newly planted coffee area increased by 135,520 ha, due to the conversion of forests to coffee plantations. Such distributions of coffee areas and decadal changes in farming activities are critically useful for policymakers to formulate successful crop planning strategies. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature's flood buffers for economic advancement. • A dual-level attention based vision transformer is proposed for processing multi-spectral satellite images. • Open-source satellite imagery based land-cover classification of stream bed is proposed. • A new labeled data set is developed for three study sites in India — Bhalswa lake, Yamuna river, and Anasagar lake. • The proposed model outperforms other considered transformer based and convolution based models. [ABSTRACT FROM AUTHOR]

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster. [ABSTRACT FROM AUTHOR]

The unplanned and uncontrolled urbanization of Indian cities has put them under different ecological and environmental threats. Urban heat island (UHI) is one such critical ecological hazard, whereby an urban area is experiencing higher land surface temperature (LST) as compared to the surrounding rural area. In the present study, the relationship of LST and surface urban heat island (SUHI) with the degree of impervious surface (IS) and green spaces (GS) in four rapidly growing Indian cities is presented. This study utilizes different geospatial techniques, including urban-rural gradient analysis, surface urban heat island estimation using Landsat OLI/TIRS data. The results signify a strong negative correlation of LST with the IS for Ahmedabad, Jodhpur, and Nagpur, while a positive correlation is seen over Guwahati. The negative correlation is the manifestation of the urban cool island, pertaining to higher LST over rural areas. On the other hand, Guwahati is surrounded by green vegetation, which provides natural cooling and thus lowers the LST, resulting in positive SUHI. The density of GS is found to be a significant contributor of SUHI in Guwahati city, whereas in the other three cities, its impact is insignificant due to its presence in very less amount in rural surroundings. [ABSTRACT FROM AUTHOR]

Land cover mapping from satellite images has progressed from visual and statistical approaches to Random Forests (RFs) and, more recently, advanced image recognition techniques such as convolutional neural networks (CNNs). CNNs have a conceptual benefit over RFs in recognising spatial feature context, but potentially at the cost of reduced spatial detail. We tested the use of CNNs for improved land cover mapping based on Landsat data, compared with RFs, for a study area of approximately 500 km × 500 km in southeastern Australia. Landsat 8 geomedian composite surface reflectances were available for 2018. Label data were a simple nine-member land cover classification derived from reference land use mapping (Catchment Scale Land Use of Australia—CLUM), and further enhanced by using custom forest extent mapping (Forests of Australia). Experiments were undertaken testing U-Net CNN for segmentation of Landsat 8 geomedian imagery to determine the optimal combination of input Landsat 8 bands. The results were compared with those from a simple autoencoder as well as an RF model. Segmentation test results for the best performing U-Net CNN models produced an overall accuracy of 79% and weighted-mean F1 score of 77% (9 band input) or 76% (6 band input) for a simple nine-member land cover classification, compared with 73% and 68% (6 band input), respectively, for the best RF model. We conclude that U-Net CNN models can generate annual land cover maps with good accuracy from proxy training data, and can also be used for quality control or improvement of existing land cover products. [ABSTRACT FROM AUTHOR]

Accurate simulations of the spatial and temporal changes in vegetation gross primary production (GPP) play an important role in ecological studies. Previous studies highlighted large uncertainties in GPP datasets based on satellite data with coarse spatial resolutions (>500 m), and implied the need to produce high-spatial-resolution datasets. However, estimating fine spatial resolution GPP is time-consuming and requires an enormous amount of computing storage space. In this study, based on the Eddy Covariance-Light Use Efficiency (EC-LUE) model, we used Google Earth Engine (GEE) to develop a web application (EC-LUE APP) to generate 30-m-spatial-resolution GPP estimates within a region of interest. We examined the accuracy of the GPP estimates produced by the APP and compared them with observed GPP at 193 global eddy covariance sites. The results showed the good performance of the EC-LUE APP in reproducing the spatial and temporal variations in the GPP. The fine-spatial-resolution GPP product (GPPL) explained 64% of the GPP variations and had fewer uncertainties (root mean square error = 2.34 g C m−2 d−1) and bias (−0.09 g C m−2 d−1) than the coarse-spatial-resolution GPP products. In particular, the GPPL significantly improved the GPP estimations for cropland and dryland ecosystems. With this APP, users can easily obtain 30-m-spatial-resolution GPP at any given location and for any given year since 1984. [ABSTRACT FROM AUTHOR]

• First long-term dynamic maps of intensive forage acreage are generated. • Dynamic monthly composite images (DMCI) improve intensive forage mapping accuracy. • DMCI- and phenology-based intensive forage mapping approach is robust and extendable. • Intensive forage is harvested twice or more a year in single-cropping regions. • Intensive forage expansion is remarkable in Arhorqin Banner during 2008–2022. The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm's efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions. [ABSTRACT FROM AUTHOR]

Nepal's forest cover nearly doubled over the last three decades. While Community Forest (CF) management and agricultural abandonment are primary drivers of forest cover expansion, the contribution of afforestation on privately managed land is not well documented. We mapped forest cover change from 1988 through 2016 in 40 privately managed sites that transitioned from agriculture to forest and assessed how agricultural abandonment influenced private land management and afforestation. We used a mixed method analysis to integrate our 29-year Landsat satellite image-based record of annual forest cover with interview data on historical land cover and land use dynamics from 65 land managers in Bagmati Province. We find that privately managed land accounted for 37% of local forest cover gain, with mean forest area within private forests growing from 9% to 59%. Land managers identified two factors driving these gains on private land: implementation of CF management in adjacent government forests and out-migration. These previously undocumented linkages between forest cover gain on private land and CF management merits further research in community forests and calls for greater policy and technical support for small-scale timber growers and rural households who rely on private forests for income generation. • 29-year remote sensing analysis links forest change to migration & community Forestry. • CF management inspired farmers to grow forest resources on their private land. • Out-migration induced agricultural abandonment was followed by smallholder forestry. • Land managers actively shifted land use in response to community forestry & migration. [ABSTRACT FROM AUTHOR]

For 43 years, multi-dated satellite imagery is used to examine shifts in shorelines and potential location on shorelines for Dr. Abdul Kalam Island, in the Bay of Bengal, India. A Normalized Difference Water Index with object-based classification system and automatic shoreline extraction tool is used to retrieve the shoreline automatically. To calculate the rates of shoreline change and future positions, based on empirical observations, the simple statistical model, such as EPR and LR models are used. The erosion/accretion scenario is also analyzed for the 1976, 1989, 1997, 2008 and 2018 shoreline of Landsat imaging.Results showsnorth of Dr. Abdul Kalam Island is subjected to high deposition rates and the south part having high erosion rates in the vicinity of the Dhamara Estuary and Maipura River. The short-term (2023) and long-term (2028) shoreline positions are predicted on the basis of the delineated shoreline. Around 63% of transect's RMSE values varies between ±10 m, showing better agreement among the estimated shoreline positions and satellite-based ones. The results of the study can be very useful for the quantification of shoreline changes, shoreline forecasting and precautionary measures for anti-satellite missile launching centre. [ABSTRACT FROM AUTHOR]

This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables. The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region. [ABSTRACT FROM AUTHOR]

Urban sprawl has become a global phenomenon as an outcome of growing population and rapid urbanization. Previous studies have addressed the rising incidence of uncontrollable urban development, particularly in peri‐urban areas of cities, leading to chronic urban sprawl. The city of Guwahati, a million city in north east India, has expanded significantly in recent years. In this article, the links between population and growth of built‐up areas were examined using geo‐spatial techniques and remotely sensed datasets. The results indicate that the sprawl has accentuated in recent years. The intensity of land use remained uneven due to marked variations in the distribution of built‐up areas, plausibly an outcome of unplanned urban growth. If current trends are anything to go by, future urban sprawl could pose serious threats to the vulnerable eco‐sensitive and peri‐urban areas of Guwahati. Secondary cities have unfortunately received scant attention in urban policy research, and Guwahati, epitomizes urban woes in a developing country. [ABSTRACT FROM AUTHOR]

Impervious surfaces are the most significant feature of human settlements. Timely, accurate, and frequent information on impervious surfaces is critical in both social-economic and natural environment applications. Over the past 40 years, impervious surface areas in China have grown rapidly. However, annual maps of impervious areas in China with high spatial details do not exist during this period. In this paper, we made use of reliable impervious surface mapping algorithms that we published before and the Google Earth Engine (GEE) platform to address this data gap. With available data in GEE, we were able to map impervious surfaces over the entire country circa 1978, and during 1985–2017 at an annual frequency. The 1978 data were at 60-m resolution, while the 1985–2017 data were in 30-m resolution. For the 30-m resolution data, we evaluated the accuracies for 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Overall accuracies reached more than 90%. Our results indicate that the growth of impervious surface in China was not only fast but also considerably exceeding the per capita impervious surface area in developed countries like Japan. The 40-year continuous and consistent impervious surface distribution data in China would generate widespread interests in the research and policy-making community. The impervious surface data can be freely downloaded from http://data.ess.tsinghua.edu.cn. [ABSTRACT FROM AUTHOR]

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

Abstract As the largest Asian river and fourth world's largest river by water flow, the Changjiang River transports a considerable amount of terrigenous particulate organic carbon (POC) into ocean, which has experienced significant pressure from human activities. We conducted monthly sampling (from May 2015 to May 2016) at Datong, the most downstream non-tidal hydrological station along the Changjiang River. To monitor long-term POC variations, we developed a two-step POC algorithm for Landsat satellite data and calculated monthly POC flux during 2000–2016. Monthly POC flux ranged from a minimum of 1.4 × 104 t C in February 2016 to a maximum of 52.04 × 104 t C in May 2002, with a mean of 13.04 × 104 t C/month. At Datong, monthly POC flux was positively exponentially related to water flow (N = 118, R = 0.69, p < 0.01). In the wet season (May to October) with high water flow, a large amount of terrigenous POC was flushed into the Changjiang River. After the Three Gorges Dam (TGD), both POC concentration (R = 0.46, p < 0.01) and POC flux (R = 0.33, p < 0.01) significantly exponentially decreased during 2000–2016. After 2011 with regular operation of the TGD, POC transport at Datong showed relative stability and its seasonal difference became smaller. POC concentration from the TGD to Datong in the Changjiang mainstream (~1000 km) was also derived from Landsat data, which was spatially varied with water flow, slope gradient, and watercourse width. This study improved our understanding of the spatiotemporal variations of POC transport in the Changjiang mainstream, especially the influences of the TGD construction. Highlights • POC algorithm for the Changjiang River was developed using a bio-optical model. • Long-term riverine POC flux was estimated with the Landsat data during 2000–2016. • Weaker seasonal POC changes were found after regular functioning of the TGD. [ABSTRACT FROM AUTHOR]

This work outlines a methodological proposal to assess plastic covered greenhouses (PCG) land cover change from the combination of archival aerial orthoimages and Landsat data. In this way, landscape spatial metrics were semi-automatically derived to be used in the analysis of the spatial arrangement of PCG areas. The experimental process consisted of two main phases: (i) mapping PCG through a semi-automatic object-based image analysis (OBIA) approach relying on segmentation plus non-parametric supervised classification; (ii) processing the PCG classified objects to yield different landscape spatial metrics. The case study has focused on two high density PCG sites located in south-eastern Spain. To analyse PCG land cover evolution, each study site was composed of three multi-temporal remote sensed datasets formed by the fusion of orthoimages (O) derived from archival aerial photography and temporally corresponding Landsat images (L). In terms of PCG mapping performance, the best results were obtained when using O + L datasets as complementary data to be used in a data fusion process. In addition, a new feature called "Greenhouse Detection Index" has been successfully developed and tested, yielding excellent results at the mapping phase. Finally, the semi-automatically extracted PCG land cover metrics, though depicting some variability, have reproduced the behaviour and temporal trend of the manually obtained ones (manual digitalisation) reasonably well. These results can be translated to an exponential reduction of time and cost for analysing long-term PCG land cover change. Highlights • Analysis of plastic covered greenhouses (PCG) land cover from spatial metrics. • Object-based image analysis gave best results from Landsat and orthoimages fusion. • New Landsat-based feature (greenhouse detection index) devised for PCG mapping. • Good correlation between spatial metrics and manual digitising. [ABSTRACT FROM AUTHOR]

Urbanization experiences different speeds and forms under diverse development stages across the globe. However, urbanization-induced impacts on long-term surface urban heat island intensity (I s) trends across global cities and the regulators of such impacts remain understudied Here we estimate interannual trends in daytime I s (i.e., urban-rural differences in surface temperatures) across 511 major cities for 1985–2020 using annual averages calculated using reconstructed land surface temperature data derived from >250,000 Landsat thermal images. Our study reveals that the global mean I s growth rate is 0.156 °C/decade. We further examine I s change associated with per 1% impervious land growth (denoted as β) in each city throughout the research period and during different periods. The global mean β is 0.018 ± 0.025 °C/% (mean ± 1 standard deviation) for the whole period, with greater values in humid than in arid climates; and the β may change during different periods, e.g., it has more than tripled when urban impervious land exceeds 30%, indicating the spatiotemporally divergent impacts of urbanization on I s trends across global cities. The spatial variations in β across global cities are well correlated with rural vegetation abundance and precipitation but not with urban population. Among these three factors, rural vegetation abundance possesses the greatest standardized regression coefficient of partial least-squares model, signifying the critical role of biome background in regulating β. The finding implies that future urbanization over densely vegetated regions should be more carefully and strategically planned due to the greater urbanization-induced surface warming effect. • Landsat land surface temperatures across global 511 cities were reconstructed. • Global surface urban heat island trends since the 1980s were analyzed. • Urbanization-induced impacts on surface urban heat island trends were evaluated. • Urbanization-induced impacts are greater in cities with humid climates. • Rural vegetation abundance regulates urbanization-induced impacts. [ABSTRACT FROM AUTHOR]

Estimation of high-resolution terrestrial evapotranspiration ( ET ) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large uncertainty among existing ET products from Landsat that limit their application. This study presents a simple Taylor skill fusion ( STS ) method that merges five Landsat-based ET products and directly measured ET from eddy covariance ( EC ) to improve the global estimation of terrestrial ET . The STS method uses a weighted average of the individual ET products and weights are determined by their Taylor skill scores ( S ). The validation with site-scale measurements at 206 EC flux towers showed large differences and uncertainties among the five ET products. The merged ET product exhibited the best performance with a decrease in the averaged root-mean-square error ( RMSE ) by 2–5 W/m 2 when compared to the individual products. To evaluate the reliability of the STS method at the regional scale, the weights of the STS method for these five ET products were determined using EC ground-measurements. An example of regional ET mapping demonstrates that the STS -merged ET can effectively integrate the individual Landsat ET products. Our proposed method provides an improved high-resolution ET product for identifying agricultural crop water consumption and providing a diagnostic assessment for global land surface models. [ABSTRACT FROM AUTHOR]

An indicator of quantifying eco-environmental vulnerability was established by synthesizing 12 variables, mainly retrieved from satellite data with incorporation of analytical hierarchy process (AHP). Six vulnerability levels of potential, slight, light, medium, heavy , and very heavy were graded to depict changes of vulnerability over temporal and spatial scales. The proposed approach was employed to study spatiotemporal eco-environmental vulnerability with Landsat data acquired in 1989, 2003, and 2014 for the Thua Thien – Hue Province, Vietnam. Over the time periods of 1989–2003 and 2003–2014, both heavy and very heavy vulnerability levels exhibit an increasing trend in both magnitude and spatial size: The former raised from 5.9% in 1989, to 7.9% in 2003, and 15% in 2014; and the later increased from 1.2% in 1989, to 3.2% in 2003, and 7.3% in 2014. Both levels mainly appeared on urbanized area, bare land, semi-bare land, agricultural land, and sparse forests. In contrast, there was a significant decline in potential vulnerability level with 36.4% in 1989, 30.9% in 2003, and 19.2% in 2014, while the remaining vulnerability levels slight, light, and medium fluctuated slightly, increased in 2003 and decreased in 2014. Supporting reasons for such changes include: (1) deforestation, agriculture intensification, construction of three hydro-electric projects during the period 2003–2014; and (2) significant expansion of urbanized area leading to differences in thermal signatures in urban areas as compared with rural areas. The findings demonstrate that eco-environmental vulnerability is primarily exaggerated by anthropogenic activities through land use/land cover (LULC) changes and further enhanced by natural processes including disasters in the Thua Thien – Hue Province of Vietnam. The correlation between land surface temperature (LST) and Normalized Difference Built-up Index (NDBI) is found to be positively correlated with 0.87, 0.89, and 0.84 for 1989, 2003, and 2014, respectively. In contrast, LST-Normalized Difference Vegetation Index (NDVI) is found negatively correlated with respect to the spatiotemporal trend of environmental vulnerability with −0.81, −0.81, and −0.76 in 1989, 2003, and 2014, respectively. In addition, areas having potential, slight, and medium thermal environmental levels are decreased from 1989 to 2003 to 2003–2014. At the regional scale, increased anthropogenic activities through land’s modification have intensified the eco-environmental vulnerability in the study area. The currently proposed methodology is feasible for evaluating long-term eco-environmental changes processes by using remote sensing data, and valid for the other regions. [ABSTRACT FROM AUTHOR]

Currently, the burned area statistics of Thailand use only fire suppression data from forest fire control station reports. However, forest fire phenomena are varied and some places are difficult to assess with ground surveys. The specific objectives of the study are: (1) to identify burned areas using burned area mapping software (BAMS) and assess the accuracy and (2) to quantify and analyze the characteristics of the burned areas of the Upper Northern region of Thailand. BAMS is supervised burned area mapping software based on Landsat data and two-phase supervised classification by using multi-temporal differences (pre- and post-fire scenes) of spectral indices. The research methodology consisted of 4 components comprising (1) data collection and preparation, (2) burned area extraction using BAMS, (3) accuracy assessment, and (4) overlay analysis. The results showed that in 2014, March had the most burned areas (1550.83 km²), followed by April (509.14 km²) and February (296.06 km²). Overall accuracy for 2 Landsat scenes, Path 130 Row 47 (Date 13 March 2014) and Path 131 Row 47 (Date 20 March 2014), with Department of National Parks, Wildlife and Plant Conservation (DNP) ground fire records in March 2014 were 93.23% and 90.72%, respectively. Based on overlay analysis, Chiang Mai had the highest total of burned areas (511.96 km²) followed by Lampang (466.92 km²) and Mae Hong Son (400.28 km²). In addition, there were 31 of 32 national parks affected by forest fires in 2014, the exception being Si Satchanalai National Park. The top 3 national parks which were most affected by forest fires were Op Luang, Mae Ping, and Mae Yom. Likewise, there were 21 of 22 wildlife sanctuaries affected by forest fires in 2014, the exception being Lamnam Nan Fang Khwa Wildlife Sanctuary. The top 3 wildlife sanctuaries which were most affected by forest fires were Lumnam Pai, Salawin, and Tham Chao Ram. According to the forest land use data in 2000 of the Royal Forest Department (RFD), the burned areas in 2014 inside the forest areas totaled 1569.52 km² or 66.62% and outside the forest areas totaled 786.51 km² or 33.38%. The types of forest most affected by fire were mixed deciduous forest and dry dipterocarp forest. The results from the study demonstrate that BAMS is suitable for burned area mapping in Thailand. Therefore, the DNP and the RFD under the Ministry of Natural Resources and Environment should apply BAMS as an operational tool to obtain annual burned area maps from Landsat data after the end of the fire season. [ABSTRACT FROM AUTHOR]

Land-use and land-cover (LULC) changes have been observed in Sierra Leone, but to what extent they impact rural livelihoods, amidst investment by multinational companies in non-food crops and mining, remains a grey area in the scientific discourse. Against this backdrop, this study attempts, for the first time, to investigate the spatio-temporal dynamics of LULC changes and their impacts on household livelihoods in a rural area in eastern Sierra Leone, using remote sensing and ancillary socio-economic and livelihood data. For this purpose, temporal Landsat images at 1986, 2000 and 2020 were subjected to the Maximum Likelihood classification algorithm to map and detect the change of the six most informative LULC classes in the test site. Ground-truth LULC and socio-economic and household livelihood surveys were conducted to obtain ancillary data. Results indicate a substantial (51%) decrease in Wetland area (29,525 ha to 14,554 ha) but an increase in Dense Forest of 24% (8,873 ha to 11,036 ha) over the 34-year period. While the natural assets of households are on the decline, physical and financial assets are on the increase. However, household livelihood outcomes are less favourable and households are therefore vulnerable to food insecurity as changes in LULC persist. • Impacts of land-use and land-cover change on rural livelihoods investigated. • Household natural assets decline with land-use and land-cover change. • Physical and financial assets increase with land-use and land-cover change. • Household livelihood outcomes become less favourable with land-use and land cover change. • Increase in household vulnerability to food insecurity as changes in land-use and land-cover persist. [ABSTRACT FROM AUTHOR]

Kakroodi, A.A.; Kroonenberg, S.B.; Goorabi, A., and Yamani, M., 2014. Shoreline response to rapid 20th century sea-level change along the Iranian Caspian Coast. The Caspian Sea, the largest lake in the world, is characterized by rapid sea-level changes. This provides a real physical model of coastal response to rapid sea-level change in a period of just a few years, which might take a millennium along oceanic coasts. Between 1929 and 1995, the Caspian sea level experienced the last cycle, with a range of ±3 m. This caused disastrous effects along the coast and destroyed many buildings, roads, farms, and other human property. During the preceding 48 years of sea-level fall, a large area of the sea bottom emerged, which was then used for the development of residential zones. That area had to be abandoned when sea level rose by almost 3 m in a period of 18 years. With the use of LANDSAT data, we calculated total shoreline shifts in 22 littoral cells, each cell containing three transects over a 3-km distance. Both landward and seaward shifts occur during rapid sea-level rise between 1977 and 2001. [ABSTRACT FROM AUTHOR]

Abstract: This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from the Enhanced Thematic Mapper (ETM+) sensor on board at the Landsat 7 satellite platform is used for this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, there was an increasing tourist activity and a high growth in the construction sector of the study area. The land-use changes were identified, examining several vegetation indices and band combinations, along with the implementation of different well-known classification techniques. The Normalized Difference Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification techniques. The best overall accuracy for the study area was achieved with the SVM classifier and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The classification results depicted notable urbanization, small deforestation and important LULC changes in the agriculture sector, indicating a rapid coastal environment change in the region of interest. [Copyright &y& Elsevier]

Abstract: Landsat data constitute the longest record of global-scale medium spatial resolution earth observation data. As a result, the current methods for large area monitoring of land cover change using medium spatial resolution imagery (10–50m) typically employ Landsat data. Most large area products quantify forest cover change. Forests are a comparatively easy cover type to map as well as a current focus of environmental monitoring concerning the global carbon cycle and biodiversity loss. Among existing change products, supervised or knowledge-based characterization methods predominate. Radiometric correction methods vary significantly, largely as a function of geographic/algorithmic scale. For instance, products created by mosaicking per scene characterizations do not require radiometric normalization. On the other hand, methods that employ a single index or classification model over an entire study area do require radiometric normalization. Temporal updating of cover change varies between existing products as a function of regional acquisition frequency, cloud cover and seasonality. With the Landsat archive opened for free access to terrain-corrected data, future product generation will be more data intensive. Per scene, interactive analyses will no longer be viable. Coupling free and open access to large data volumes with improved processing power will result in automated image pre-processing and land cover characterization methods. Such methods will need to leverage high-performance computing capabilities in advancing the land cover monitoring discipline. Robust validation efforts will be required to quantify product accuracies in determining the optimal change characterization methodologies. [Copyright &y& Elsevier]

Recent technological advances in the field of radar remote sensing have allowed the deployment of an increasing number of new satellite sensors. These provide an important source of Earth observation data, which add to the currently existing optical data sets. In parallel, the development of robust methods for global forest monitoring and mapping is becoming increasingly important. As a consequence, there is significant interest in the development of global monitoring systems that are able to take advantage of the potential synergies and complementary nature of optical and radar data. This paper proposes an approach for the combined processing of Landsat and ALOS-PALSAR data for the purpose of forest mapping and monitoring. This is achieved by incorporating the PALSAR data into an existing operational Landsat-based processing system. Using a directed discriminant technique, a probability map of forest presence/absence is first generated from the PALSAR imagery. This SAR classification data is then combined with a time series of similar Landsat-based maps within a Bayesian multitemporal processing framework, leading to the production of a time series of joint radar-optical maps of forest extents. This approach is applied and evaluated over a pilot study area in northeastern Tasmania, Australia. Experimental outcomes of the proposed joint processing framework are provided, demonstrating its potential for the integration of different types of remote sensing data for forest monitoring purposes. [ABSTRACT FROM AUTHOR]

This article presents the use of multitemporal remote sensing data for monitoring land cover changes in Acre State, western Brazilian Amazonia. For this study, the 2000 Landsat ETM+ (ETM+, Enhanced Thematic Mapper Plus), the 1990 Landsat TM (TM, Thematic Mapper) and 1980 Landsat MSS (MSS, Multispectral Scanner System) were used. The 2005 and 2007 Terra MODIS (MODIS, Moderate Resolution Imaging Spectroradiometer) images were also used to map deforestation that had occurred during the recent years and to map burned areas that occurred in the 2005 dry year. The ETM+, TM, MSS and MODIS images were converted to vegetation, soil and shade fraction images, using linear spectral mixing model, in order to reduce the data volume for digital processing. Then land cover maps were produced by digital classification of these fraction images. The results showed that deforestation increased by 7100 km2 from 1980 to 1990, by 5100 km2 from 1990 to 2000 and by 3300 km2 from 2000 to 2007. About 2100 km2 of vegetation regrowth areas were observed in the 2000 ETM+ images. The analysis of 2005 MODIS images showed that 3700 km2 of deforested areas and 2800 km2 of forested areas were burned in Acre State in 2005. This kind of information is important for regional and global environmental studies and for efforts to control such burning and deforestation activities in the future. [ABSTRACT FROM AUTHOR]

Kansas is one of the most productive agricultural states in the United States, where agricultural irrigation is a primary user of underground and surface water. Because of low precipitation and declining groundwater levels in western and central Kansas, sustainable management of irrigation water resources is a critical issue in the agricultural productivity of the state. The objective of this study is to analyze and characterize the water use and water balance in the croplands of Kansas using satellite observations, meteorological data, and in situ irrigation water use records. We used actual evapotranspiration (ETa), precipitation, soil moisture, and irrigation water use to calculate water balance for Kansas in 2015 at scales of counties, climatic divisions, and groundwater management districts (GMD). The Operational Simplified Surface Energy Balance model was implemented to estimate 30-m resolution ETa. Results showed that the seasonal (May – September) precipitation, soil water storage change, and ETa are 528 mm, 80 mm, and 555 mm, respectively, on average of all croplands in the state. The annual net irrigation water consumption was 293 mm for irrigated croplands, indicating that irrigation water constitutes an substantial portion of the water supply in the state. The total volumetric irrigation water use was 3.24 km3 for all croplands within five GMDs in western and south-central Kansas, while only 0.38 km3 was outside of GMDs. The multiple regression models of ETa against precipitation and irrigation water use were statistically significant with R 2 values of 0.71 and 0.87, respectively, at county and climate division scales. Regression models also indicated a higher rate of ETa response to irrigation water use than that to precipitation. Our study demonstrated the spatial patterns of crop water use and water balance in Kansas, which could provide useful information for management of irrigation agriculture and water resources for the state. • Water use and water balance were analyzed and evaluated for croplands of Kansas. • Water balance maps were created using geospatial data and irrigation water records. • Irrigation water use constitutes a substantial portion of water supply in Kansas. • Water use was more efficient in western Kansas due to dry climate and low runoff. • Evapotranspiration was 65% more responsive to irrigation water than to precipitation. [ABSTRACT FROM AUTHOR]

Coastal wetlands in the Mekong Delta (MD), Vietnam, provide various vital ecosystem services for the region. These wetlands have experienced critical changes due to the increase in regional anthropogenic activities, global climate change, and the associated sea level rise (SLR). However, documented information and research on the dynamics and drivers of these important wetland areas remain limited for the region. The present study aims to determine the long-term dynamics of wetlands in the south-west coast of the MD using remote sensing approaches, and analyse the potential factors driving these dynamics. Wetland maps from the years 1995, 2002, 2013, and 2020 at a 15 m spatial resolution were derived from Landsat images with the aid of a hybrid classification approach. The accuracy of the wetland maps was relatively high, with overall accuracies ranging from 86–93%. The findings showed that the critical changes over the period 1995/2020 included the expansion of marine water into coastal lands, showing 129% shoreline erosion; a remarkable increase of 345% in aquaculture ponds; and a reduction of forested wetlands and rice fields/other crops by 32% and 73%, respectively. Although mangrove forests slightly increased for the period 2013/2020, the overall trend was also a reduction of 5%. Our findings show that the substantial increase in aquaculture ponds is at the expense of mangroves, forested wetlands, and rice fields/other crops, while shoreline erosion significantly affected coastal lands, especially mangrove forests. The interaction of a set of environmental and socioeconomic factors were responsible for the dynamics. In particular, SLR was identified as one of the main underlying drivers; however, the rapid changes were directly driven by policies on land-use for economic development in the region. The trends of wetland changes and SLR implicate their significant effects on environment, natural resources, food security, and likelihood of communities in the region sustaining for the long-term. These findings can assist in developing and planning appropriate management strategies and policies for wetland protection and conservation, and for sustainable development in the region. [ABSTRACT FROM AUTHOR]

The ecological restoration of mining areas is very important, and repeated field surveys are inefficient in large-scale vegetation monitoring. The coal mining industry is currently facing the challenge of the lack of appropriate methods for monitoring restoration processes. This study used an open pit coal mine in Dongsheng District, Inner Mongolia, China as an example, and used the 2011–2018 Landsat TM/ETM+ and OLI images to monitor and evaluate vegetation restoration activity of the coal mine. The average value of the monthly maximum value of vegetation index in the growing season was selected as the basic indicator for studying vegetation and bare soil changes. The growth root normalized differential vegetation index (GRNDVI) and GRNDVI anomaly method indicated that the constructed land type change factor was used to study the growth of mine vegetation and change of the range of bare land in the entire mining region. We found that westward mining activities started from 2012, and vegetation was restored in the eastern original mining region from 2013. The restoration vegetation areas from 2015 to 2016 and from 2017 to 2018 were larger than those in the other restoration years. Moreover, areas of expanded bare land from 2011 to 2012, and from 2017 to 2018 were larger than those in the other expansion years. The restoration vegetation growth changes were compared with those of the natural vegetation growth. Results showed that the restoration vegetation growth trend was considerably similar with that of the natural vegetation. Inter-annual restoration effects were analyzed by constructing the effect of the area-average factor and using vegetation growth data. Accordingly, the restoration vegetation effects were best in 2014 and 2016. Comprehensive restoration effect was analyzed using the weighted evaluation method to obtain the overall restoration effects of the coal mine. Results showed that the comprehensive restoration effect is inclined to the inferior growth state. This study conducted a preliminary evaluation of mine restoration vegetation, thereby providing a promising way for the future monitoring and evaluation of such processes. [ABSTRACT FROM AUTHOR]

Tianjin is the largest open city along the coastline in Northern China, which has several important wetland ecosystems. However, no systematic study has assessed the water body changes over the past few decades for Tianjin, not to mention their response to human activities and climate change. Here, based on the water change tracking (WCT) algorithm, we proposed an improved water change tracking (IWCT) algorithm, which could remove built-up shade noise (account for 0.4%~6.0% of the final water area) and correct omitted water pixels (account for 1.1%~5.1% of the final water area) by taking the time-series data into consideration. The seasonal water product of the Global Surface Water Data (GSWD) was used to provide a comparison with the IWCT results. Significant changes in water bodies of the selected area in Tianjin were revealed from the time-series water maps. The permanent water area of Tianjin decreased 282.5 km2 from 1984 to 2019. Each time after the dried-up period, due to government policies, the land reclamation happened in Tuanbo Birds Nature Reserve (TBNR), and, finally, 12.6 km2 of the lake has been reclaimed. Meanwhile, 488.6 km2 of land has been reclaimed from the sea along the coastal zone in the past 16 years at a speed of 28.74 km2 yr−1 in the Binhai New Area (BHNA). The method developed in this study could be extended to other sensors which have similar band settings with Landsat; the products acquired in this study could provide fundamental reference for the wetland management in Tianjin. [ABSTRACT FROM AUTHOR]

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. [ABSTRACT FROM AUTHOR]

The Great Rift Valley system is home to many volcanic and tectonic lakes including some of the world's oldest and deepest lakes. These lakes host a rich heritage of biodiversity that is endangered by recent drastic hydrologic changes due to multiple natural and anthropogenic stressors in the catchment areas of some of the lakes. This study utilized Landsat TM, ETM+, and OLI data to conduct a systematic investigation of the relationship between hydrological dynamics in the basins of four Rift Valley lakes (Nakuru, Baringo, Bogoria, and Elementaita) and recent land cover and land-use change. The Modified Normalized Difference Water Index (MNDWI) proved to be more accurate and robust for delineating water surface areas when compared to the output of Normalized Difference Vegetation Index (NDVI) and classification algorithms. NDVI was successful when delineating water surface at Lake Baringo but not in Lakes Bogoria, Nakuru, and Elementaita, whose surfaces were dominated by algae. All the lakes expanded substantially after 2010 submerging surrounding areas leading to disruption of livelihoods, property damage, and displacement of thousands of people. The recent drastic hydrologic changes have multiple causations including land cover and land use change, increase in rainfall, and possible change in geogenic water input due to tectonic activity. The rapid rise in water levels appears to have altered the biogeochemical balance of the hypersaline lakes with severe ramifications on the rich biodiversity that is supported by the lakes. Unlabelled Image • Surface area change in four Rift Valley lakes was derived from MNDWI for 1984–2018. • NDVI used to estimate lake surface area covered by algae for 1984–2018. • Land cover change provides information on primary causes of change in surface area. • Remarkable change in surface area after 2010 linked to multiple causations. [ABSTRACT FROM AUTHOR]

Global urban growth leads to a great increase in the impervious surface area (ISA) such as roads, plazas, airports, and parking lots, and consequently reshapes hydrological regimes in urban basins. Beijing, the capital of China, has experienced rapid urban growth since the 1980s. However, the spatial-temporal variability of the ISA and its impact on flooding risk are unclear. This study monitored urban growth (i.e., the evolution of the ISA) in Beijing for the period of 1980–2015 based on Landsat data, and identified the response of surface runoff yield using a land-surface hydrological model. The modeling at a relatively high spatial resolution (~6 km) was driven with retrieved long-term ISA dynamics, Global LAnd Surface Satellite (GLASS) product, and climate forcings. The results show that the impervious surface fraction (ISF) in Beijing increased from 8.73% (1448.16 km2) in 1980 to 22.22% (3685.92 km2) in 2015. With a demarcation at around the year 2000, the ISA growth presents a new pattern with a northeast-southwest direction from the Core Functional Zone (Core-Zone). Due to the ISA expansion, the simulated runoff coefficient in 2010 is approximately doubled compared to that of 1980. We identified an ISF threshold of approximately 6%, beyond which every 1% increase in the ISF may increase the surface runoff by approximately 5.51 mm/year, and thereby poses a high potential flooding risk even under a moderate rainfall event. In four typical historical storms, the sensitivity coefficients of surface runoff to precipitation and ISF were 0.97 and 0.63, respectively, indicating impervious surfaces dramatically enhanced the potential flooding risk. Our findings have implications for urban planning and the construction of sponge city in Beijing. Unlabelled Image • Urban growth in Beijing was monitored from 1980 to 2015 based on Landsat data. • Surface runoff yields were identified using a land-surface hydrological model. • The impervious surface fraction increased from 8.73% in 1980 to 22.22% in 2015. • The simulated runoff coefficient in 2010 was approximately doubled compared to 1980. • The impervious surface fraction of 6% is the detectable threshold in response to potential flooding risk. [ABSTRACT FROM AUTHOR]

Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization. [ABSTRACT FROM AUTHOR]

Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn. • We improved the performance of "Exclusion/Inclusion" approach in arid regions. • We mapped global artificial impervious areas (GAIA) with Google Earth Engine. • The mean overall accuracy over multiple years is higher than 90%. • GAIA reached 797,076 km2 by 2018, more than 2.5 times that of 1990. • The top five countries are China, US, India, Russia, and Brazil. [ABSTRACT FROM AUTHOR]

Africa's unprecedented, uncontrolled and unplanned urbanization has put many African cities under constant ecological and environmental threat. One of the critical ecological impacts of urbanization likely to adversely affect Africa's urban dwellers is the urban heat island (UHI) effect. However, UHI studies in African cities remain uncommon. Therefore, this study attempts to examine the relationship between land surface temperature (LST) and the spatial patterns, composition and configuration of impervious surfaces/green spaces in four African cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia). Landsat OLI/TIRS data and various geospatial approaches, including urban–rural gradient, urban heat island intensity, statistics and urban landscape metrics-based techniques, were used to facilitate the analysis. The results show significantly strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban–rural gradients of the four African cities. The study also found high urban heat island intensities in the urban zones close (0 to 10 km) to the city center for all cities. Generally, cities with a higher percentage of the impervious surface were warmer by 3–4 °C and vice visa. This highlights the crucial mitigating effect of green spaces. We also found significant correlations between the mean LST and urban landscape metrics (patch density, size, shape, complexity and aggregation) of impervious surfaces (positive) and green spaces (negative). The study revealed that, although most African cities have relatively larger green space to impervious surface ratio with most green spaces located beyond the urban footprint, the UHI effect is still evident. We recommend that urban planners and policy makers should consider mitigating the UHI effect by restoring the urban ecosystems in the remaining open spaces in the urban area and further incorporate strategic combinations of impervious surfaces and green spaces in future urban and landscape planning. [ABSTRACT FROM AUTHOR]

The Baltoro Glacier is one of the largest glaciers in the Karakoram mountain range. Long-term monitoring of glacier dynamics provides key information on glacier evolution in a changing climate, which is essential for regional water resource and natural hazard management. On large glaciers, detailed field based mass balance is not feasible. Ice dynamic variations quantify changes in mass transport and possibly the influence of environmental parameters on the evolution of the glacier. Although velocity variations of Baltoro Glacier during winter and summer are linked to seasonally enhanced basal sliding, little is known about differences in timing and magnitude of (intra-)seasonal velocity variations and their determining mechanisms. We present time series of annual, seasonal, and intra-seasonal glacier surface velocities by means of intensity offset tracking applied on multi-mission Synthetic Aperture Radar (SAR) data for a period of 25 years from 1992 to 2017. Supraglacial lakes forming on the downstream glacier surface in summer were mapped from 1991 to 2017 based on the Normalized Difference Water Index (NDWI), calculated from multi-spectral Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery. Additionally, precipitation data of the Tropical Rainfall Measurement Mission (TRMM) and temperature data of ERA-Interim were used to derive the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) from 1998 to 2017. Linking surface velocities to the SPI confirmed a strong correlation between heavy precipitation events in winter and the magnitude and the timing of glacier acceleration in summer. Downstream extensions of summer acceleration that have been found since 2015 may be explained by additional water draining from an increased number of supraglacial lakes through crevasses that have been formed in consequence of higher initial velocities, evoked by strong winter precipitation. The warmer melt seasons observed in the years 2015 to 2017 additionally affects the formation of a supraglacial lake, so stronger summer acceleration events in recent years may be indirectly related to global warming. [ABSTRACT FROM AUTHOR]