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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]

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]

• 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]

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]

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]

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]

Urbanization has increasingly become a prevailing issue confronting scientists worldwide due to its effects on temperature and environment. This study explored effects of decadal urbanization on local temperature from Landsat data in Cantho city, Vietnam from 1996 to 2016. The mapping results of built-up areas achieved from the random forests (RFs) indicated the overall accuracies and Kappa coefficients higher than 89.5% and 0.79. Cantho city had been drastically expanded with the built-up areas increasing from 1,274.0 ha in 1996 to 2,932.7 ha in 2006, and 3,428.8 ha in 2016, respectively. The urbanization by transforming opened lands to artificial surfaces had led to effects on the city temperature. The results of temperature retrieved from Landsat data revealed the temperature obtained for 1996 in range of 25.7–37.1 °C, while the values for 2006 and 2016 falling between 26.3–37.3 °C and 24.1–40.2 °C, respectively. The average temperature of urban areas had increased 0.8 °C from 31.7 °C in 1996 to 32.5 °C in 2016. Considering 32 °C as an uncomfortably hot threshold that may affect the human health and environment, only 637.7 ha was observed for 1996, while those for 2006 and 2016 increased to 1,131.9 ha and 2,155.4 ha, respectively. [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]

Urbanization has a great impact on the local climate of a city, thereby triggering the urban heat island (UHI) effect and affecting the quality of human life. Information on past and present changes of urban landscapes and temperature could be of interest to urban planners in an attempt to shape the urban planning process and mitigate effects of UHI. This study aimed to develop an approach for assessing the urbanization and UHI effects in Ho Chi Minh City (HCMC), Vietnam using Landsat data during the periods 1996–2007–2016. The data were processed through four main steps: (1) data pre-processing, (2) impervious surface (IS) extraction, (3) land surface temperature (LST) retrieval, and (4) accuracy assessment. The results of IS extraction compared with the ground reference data indicated the overall accuracies and Kappa coefficients generally higher than 86.1% and 0.72, in all cases. During 1996–2016, the city was drastically urbanized into multiple directions, with the IS area increasing from 10,488.5 ha (1996) to 27,142.7 ha (2016). The results of retrieved LST revealed the radiant temperature for 1996 ranging from 22.4–35.8 °C, while that for 2016 remarkably higher from 25.3–40.4 °C. The relationship between LST and a newly developed normalized difference composite index (NDCI) was also examined to investigate the genesis of temperature due to decadal urbanization, indicating a strong correlation with the correlation coefficients ( r ) of higher than 0.71 ( p -value < 0.001), in all cases. To further understand UHI impacts due to the urbanization, the proportion of IS and averaged LST over IS areas for each district was calculated. The results showed that only few central districts located in the city higher than 30 °C in 1996, while most of districts were heavily influenced by urban surface structures with the LST higher than 31 °C in 2007 and 32 °C in 2016. [ABSTRACT FROM AUTHOR]

Seagrass beds comprise a unique marine ecosystem that acts as a biofilter in marine environments and serves as a spawning ground and nursery for various species of fish. Long-term monitoring of seagrass beds is critical to understanding the dynamic relationships between the ecosystems and the stresses from natural systems and society. This study investigated temporal changes of seagrass beds in Cam Ranh Bay (CRB), Vietnam using multi-temporal Landsat data from 1996 to 2015. The data were processed through 5 main steps including: (1) image preprocessing to convert Landsat data to the top of atmosphere reflectance (TOA) and to correct atmospheric effects, (2) water column correction to eliminate effects on remotely sensed data of aquatic environments, (3) image classification using a linear mixed model, (4) accuracy assessment using the ground reference data, and (5) change detection of seagrass beds. The classification results compared with the ground reference data indicated that the overall accuracies and Kappa coefficients were higher than 91.7% and 0.8, respectively, in all cases. From 1996 to 2015, the total area of seagrass beds had declined by approximately 25% (66 ha), mainly attributed to coastal development and infrastructure construction. [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]

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]

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]

Information on urban growth associated with surface temperature is crucial for urban planning. This study evaluated the urban growth and its impacts on land surface temperature (LST) from Landsat imageries in San Salvador City, El Salvador during periods 1986–2001–2016. The results of urban classification were verified with the reference data, indicating close agreement between these two datasets. The overall accuracy and Kappa coefficient were generally higher than 89.8% and 0.8, respectively. The built-up area had expanded from 1778.2 ha during 1986–2001 to 876.5 ha during 2001–2016. Due to rapid urbanization, the area, where the LST was higher than 36 °C, had remarkably increased from 31.6% (1986) to 39.7% (2001), and 51.4% (2016), respectively. These findings were reflected by the increased proportion of very hot spots from 32% (1986) to 37.7% (2001), and 39.9% (2016), respectively. The decadal impacts of urbanization on local temperature were confirmed by significant relationship of LST with the normalized difference vegetation index (NDVI) and normalized difference built-up area index (NDBI) (R 2 > 0.72). Such an information on the trend of urban expansion and local climate change could be valuable to planners to formulate successful planning strategies to regulate impacts of urban heats on the city's livability. • Multi-temporal Landsat data for urban growth monitoring. • Decadal impacts of urban expansion on local thermal environment. • Relationship of LST with vegetation indices. • Information of urban growth associated with LST is crucial for urban planning. [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]

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]