Normalized Difference Water Index Values Research Articles

Remote Sensing Assessment of Water Resources, Vegetation, and Land Surface Temperature in Eastern Saudi Arabia: Identification, Variability, and Trends

Saudi Arabia has one of the biggest water shortages and the least vegetation in the world, which is presumed to provoke this problem further due to climate change. Therefore, the present study investigates the water, vegetation, and temperature over Al-Asfar Lake region, Al Ahsa, Eastern province of Saudi Arabia using the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST: °C) from Landsat-8 based operational land imager (OLI) measurements for the period 2013 to 2023. This study presented annual and seasonal (dry months: June–September and wet months: December–April) spatiotemporal distribution and variations, calculated their absolute change and trends, and examined their relationship. Results showed positive NDWI values over Al-Asfar Lake, indicating waterbodies; while positive NDVI values on the lake's bank, signifying vegetation. Notably, there were significant temporal variations in water and vegetation observed on annual, seasonal, and monthly scales. The study also found an overall decrease in vegetation areas of 5.36 km2 in 2023 compared to 2013, while waterbodies increased by 8.83 km2. The trend analysis using area-averaged data demonstrated that NDWI increased on annual (0.0075/year) and seasonal (dry: 0.0083/year and wet: 0.0049/year) scales, while NDVI decreased (annual: 0.0066/year, dry: 0.0083/year, and wet: 0.0009/year). Moreover, LST was recorded least amount over waterbodies (28.23 °C) and vegetation (32.45 °C) covered areas compared to the entire lake region (38.43 °C), respectively. Remark, LST displayed decreasing trends over waterbodies (−0.05/year), followed by vegetation (−0.17/year), and the entire lake region (−0.0001/year), signifying that water and vegetation are vital components to controlling land surface temperature in this region. Finally, the LST showed a positive correlation with NDVI and negative correlation with NDWI. There may be a direct and indirect impact of climate change upon NDVI, LST, and NDWI as shown by the decreases in NDVI and LST and an increase in NDWI. This study can be considered as a base document to monitor waterbodies, vegetation cover, and temperature changes using remote sensing measurements of NDWI, NDVI, and LST, which will assist policymakers in developing water resource management, irrigation planning, and environmental monitoring strategies.

Exploring climatic variability and drought characteristics in Morocco’s Sais Plateau and Middle Atlas: A statistical and spectral analysis

Extreme climate change phenomena are a global reality with major repercussions on hydrosystems. Drought is particularly concerning in the southern Mediterranean region, threatening water availability and related behavior. Morocco, located in North Africa and the southern Mediterranean, is highly exposed to climate change and its consequences, particularly drought. This study aims to investigate the impact of climate variability and drought characteristics on the Saïs plateau and the Middle Atlas region, using statistical and spectral indices. By analyzing annual variations in precipitation and using the Standardized Precipitation Index (SPI) and normalized difference water index (NDWI) to elucidate the temporal dynamics of water, this study highlights significant irregularities in precipitation patterns between wet and dry years, offering insight into the severity and occurrence of drought at different altitudes. In addition, the study uses the normalized difference water index (NDWI) to elucidate temporal water dynamics, revealing cyclical patterns of water availability and identifying regions sensitive to hydrological activity or drought. Furthermore, thanks to a correlation analysis between the NDWI and the SPI12 index, the study highlights the complementary relationship between spectral and statistical indices in the assessment of drought phenomena. The results show a significant difference in precipitation between the Middle Atlas and the Sais Plateau, with the Middle Atlas receiving around double the precipitation. From 1980 to 1985, drought was followed by even drier conditions until 1995, with sporadic wet years such as 1995 and 2002. During the 1980–1994 period, a persistent drought was observed until 1995, when precipitation picked up again, with notable records in 2009–2010. Positive NDWI values, with marked peaks between 2010 and 2012, indicate periods of high-water content, while negative values, such as those in 1995, 2003, and 2006, denote periods of low water content or drought.

Temporal geomorphic modifications and climate change impacts on the lower course of the São Francisco River, Brazil

Monitoring human intervention and understanding temporal geomorphic modifications in hydrographic basins require continuous data collection. This study aimed to investigate the geomorphological and hydroclimatic changes in the lower course of the São Francisco River, Brazil from 1986 to 2020, utilizing Cloud-based satellite image processing techniques for mapping and identifying flooded areas through the calculation of Modified Normalized Difference Water Index (MNDWI). The findings reveal a significant increase in sedimented areas within the river channel, occupying 10.64 km2 in 2020, which represents a 22% increment compared to 1986. The river has experienced a territorial reduction of approximately 20%, with decreased Modified Normalized Difference Water Index values indicating shallowing due to possible sediment contributions. Rainfall and flow data exhibited a positive correlation with Modified Normalized Difference Water Index and a negative correlation with sedimentation values, indicating their influence on the river's geomorphology. Furthermore, the Alagoas bank displayed 96 transects, with an average penetration of 86.36 m into the river for each transect, highlighting hydrological and morphological changes. Overall, this study emphasizes the vulnerability of the São Francisco River.

Flood risk assessment through rapid urbanization LULC change with destruction of urban green infrastructures based on NASA Landsat time series data: A case of study Kuala Lumpur between 1990–2021

Kuala Lumpur, as one of the fastest growing cities in the world, is facing significant negative impacts on its urban ecosystem services (UESs) due to rapid urbanization. Among the classes of land use and land cover (LULC) in Kuala Lumpur, urban green infrastructure (UGI) plays a crucial role in mitigating these impacts. The present study aimed to evaluate the amount of UGIs in Kuala Lumpur for a better understanding of hydro-meteorological hazards assessment. To this end, historical data from 1990 to 2021 were collected using NASA Landsat 4 and Landsat 8 to evaluate the past and current tendencies of LULC in Kuala Lumpur. Various techniques and software, including Markov Chain, ArcGIS, and TerrSet, were utilized to examine the interactions and interpret and create maps. Six categories of land were defined, including grass, waterbodies, soil, buildings, urban forest trees, and urban trees. The study utilized Landsat's Normalized Difference Vegetation Index (NDVI) to analyze the impact of modifications on UGIs. Additionally, the Landsat Normalized Difference Water Index (NDWI) was employed to illustrate changes in urban water values. The findings revealed a significant decline in urban forest trees from 15.99% in 1990 to 6.5% in 2021, resulting in an increase in urban water values and an increase in flooding. The NDWI values in 1990 ranged between 0.383635 and −0.593184, while by 2021, they had fallen to between 0.593288 and −0.44379. These changes in NDVI and NDWI have contributed significantly to the intensity and frequency of urban flooding in Kuala Lumpur.

Estimation of Year of Construction of Bridges in Cambodia by Analyzing the Landsat Normalized Difference Water Index

Inspection data can be used to comprehend and plan effective maintenance of bridges. In particular, the year of initial construction is one of the most important criteria for formulating maintenance plans, making budget allocations, and estimating soundness. In an initial survey of bridges in Cambodia, it was concluded that the year of construction of only 54% of 2439 bridges surveyed is known, with the remaining 46% remaining unknown. In this research, Landsat satellite data is used to estimate the year of construction of these bridges. Landsat provides spatial spectral reflectance information covering more than 30 years, and for longer bridges this can be used to estimate the year of construction by visual judgement. However, limited image resolution means this is not possible for shorter bridges. Instead, a method using the Landsat Normalized Difference Water Index (NDWI) is used to estimate the year of construction. Three pixels are selected from Landsat image data in such a way that one lies on the current location of a bridge and two other reference pixels are placed on similar terrain at a certain distance perpendicular to the bridge axis. NDWI values are plotted over time for the three pixels and the difference in value between the bridge pixel and the two reference pixels is then compared. Before the bridge is constructed, all three pixels should have similar NDWI values, but after construction the value of the target bridge pixel should differ from the other two because the NDWI value of a bridge surface is different from that of the surrounding vegetation. By looking for this change, the year of construction of a bridge can be estimated. All the bridges in the Cambodian database are classified into three categories based on length (which affects their visibility in Landsat images) and year of construction is estimated. The results show that estimated year of construction has the same accuracy in all three categories.

Lake Water Quality and Dynamics Assessment during 1990–2020 (A Case Study: Chao Lake, China)

Settlements along the coastlines of oceans and lakes, which are among the world’s most densely populated areas, are in immediate danger due to stressors brought on by climate change and dangers posed by human activities. This study investigates the water changes of Chao Lake during the last 30 years by using Landsat 5, 7 and 8 time-series images and water indices, including Normalized Difference Water Index (NDWI), Normalized Difference Turbidity Index (NDTI), Green Normalized Difference Vegetation Index (GNDVI) and Normalized Sea Surface Temperature (SST). The gathered data demonstrates that each estimated indicator’s value has increased with time. Thus, over the course of the 30-year research period, the NDWI, NDTI, GNDVI and SST annual average values show increases of 112.10%, 242.42%, 112.82% and 119.42%, respectively. The NDWI index underwent these fluctuations, evidenced with the biggest amount (681.8%) in the winter and the lowest amount (28.13%) in the fall. The most NDTI changes (480%) and the least (only 50%) occurred in summer and fall, respectively. The largest increases in GNDVI (180%) and SST values (537.86%) were observed in winter; the smallest changes in GNDVI (43.48%) and GNDVI (68.76%) in fall. The outcomes also demonstrated a strong link between all four estimated factors. In the majority of the analyzed months, the correlation between the 2 measures, GNDVI and NDTI, was considerably greater and near to 1. The findings of this study may be utilized by managers, decision-makers and local planners for the purpose of environmental planning and reducing water pollution in Chao Lake (and other water regions), as well as reducing the risk of environmental hazards due to water pollution.

Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

Wetland ecosystems are one of the most important areas that provides different ecosystems services as well as habitat for plant and animal species. In spite of multipurpose, wetland ecosystems are under threats. This study attempts to analyze wetland dynamics of Abay Choman and Jimma Geneti watershed in Horo Guduru Wollega Zone, Western Ethiopia using geospatial techniques. The land use land cover (LULC), the Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were investigated using Landsat 5 TM of 1991, Landsat 7ETM+ of 2003, and OLI/TIRS of 2021. In the present study, the LULC was classified using a supervised classification method with maximum likelihood algorithm. The red and infrared bands of Landsat imagery from three different time periods were used to calculate NDVI, while the NDWI was estimated using the green and near infrared (NIR) bands of multispectral Landsat images. Results show that wetland ecosystem in the study area decreased by about 125.2 km2 (8.8%) with the rate of 4.2 km2/year. In contrast, agricultural land increased by 223.4 km2 with the rate of 7.4 km2/year between 1991 and 2021. About 66.7 km2 wetland was converted to cultivated land whereas 29.3 and 24.7 km2 of grassland and shrubs land were converted into cultivated land. As a result, the maximum NDVI and NDWI values were decreased between 1991 and 2021. To minimize the rapid loss of wetland and water bodies in the study area, proper land use planning and environmental education should be promoted.

Automated versus Manual Mapping of Gravel Pit Lakes from South-Eastern Romania for Detailed Morphometry and Vegetation

In recent years, the accelerated development of the remote sensing domain and the improvement of the resolution and frequency of satellite images allowed the increase in the accuracy of the evaluation of morphometric characteristics and the spatiotemporal distribution of pit lakes, including the small ones. Our study quantitatively analyzes small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolution of the images, the uneven quality of the images exported from Google Earth, and an additional challenge was given by the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. A comparison of the obtained NDWI values using data series from Sentinel 2A and Landsat 8 highlighted the importance of resolution and also showed a larger spectral difference between the identified water bodies and the surrounding land in favor of Sentinel 2A. Regarding the vegetation-derived indices, superior leaf area index (1.8–3) was recorded in low-lying plains and mixed areas (tall shrubs, wetlands, etc.) because the river banks have increased moisture that supports taller species with denser foliage and the sparsely vegetated areas are located in agricultural crops and in/near villages. Changes in vegetation richness and abundance can be spatiotemporally monitored using indices derived from the spectral bands of satellite imagery.

Monitoring migratory birds of India's largest shallow saline Ramsar site (Sambhar Lake) using geospatial data for wetland restoration.

Globally, saline lakes occupy about 23% by area, and 44% by volume. Importantly, these lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, or modify due to pollution, and invasive species. India’s largest saline lake, Sambhar is currently shrinking at a phenomenal rate of 4.23% every decade due to illegal saltpan encroachments. This study aims to identify the trend of migratory birds and monthly wetland status. Birds’ survey was conducted for 2019, 2020 and 2021, and combined it with literature data of 1994, 2003, and 2013, for understanding their visiting trends, feeding habits, migratory and resident birds ratio, along with ecological diversity index analysis. Normalized Difference Water Index (NDWI) was scripted in Google Earth Engine. Results state that lake has been suitable for 97 species. Highest NDWI values was 0.71 in 2021 and lowest 0.008 in 2019. Notably, the decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If these causal factors are not checked, it might completely desiccate. Authors recommend a few steps that might help conservation. Least, the cost of restoration might exceed the revenue generation.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11273-022-09875-3.

Semi-automated mapping of glacial lakes – a study in Sikkim Himalayas, India

In the Himalayas, rising number of glacial lakes with increase in area of existing lakes is a matter of serious concern and correct mapping is an important step towards regular monitoring of these glacial lakes. This study used Double Window Flexible Pace Search method to identify an optimal threshold of Normalized Difference Water Index value on multi-spectral Sentinel-2 image of 31st October 2020. A threshold value of 0.30 is obtained that resulted in identification of 419 glacial lakes with an estimated total area of about 29.86 ± 6.85 km2. The results are found to be in close agreement with previously published research article on Sikkim Himalayas and boundaries of classified lakes are validated by overlaying on high resolution PlanetScope dataset for the same date. Hence, this study presents an effective and simple way for mapping of glacial lakes that may further be extended for regular monitoring of glacial lakes in the region.

Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs

ABSTRACT Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.

Comparison of Normalized Difference Water Index (NDWI) and Sobel Filter Methods in Landsat 8 Imagery for Coastline Extraction

For a country, the existence of a coastline has an important value because it acts as a protection for marine resources and determines maritime boundaries between countries. There are various methods used for coastline analysis, both manual by digitizing and automatic by edge detection. This study compares the Normalized Difference Water Index (NDWI) method with the Sobel filter for coastline extraction in the South Coast of Sampang, Madura using Landsat 8 imagery. The best approach is then applied to the image to determine changes in coastlines from 2015 to 2020. This research shows that visually, the NDWI method produces better edges than the Sobel filter because the resulting lines are close to the original conditions in Landsat 8 or Basemap World Imagery . Sobel filter, the resulting accuracy is not very good. It does not approach field conditions, but this filter has the advantage of a relatively fast processing time because it can use a single band. Then the NDWI value generated in this study has a range of -0.497121 to 0.377046. The first class, which is a non-water body object, has a value of -0.497121 to 0. Then the second class, which is a body of water object, has a value of 0 to 0.377046. The coastline change for five years shows a shift in the coastline with a range of 0.62 to 2.75 meters. The Landsat 8 pixel size is 30 meters, while the shift is only <3 meters. So that this experiment does not show any significant coastline changes. Suggestions for further research: It is necessary to conduct a study using high-resolution imagery to confirm changes in the coastline accurately.

Spectro-temporal analysis of the Paraopeba River water after the tailings dam burst of the Córrego do Feijão mine, in Brumadinho, Brazil.

Remote sensing is an important tool for environmental assessment, especially in the event of disasters such as the tailings dam burst at the Córrego do Feijão mine, located in the Paraopeba River basin, Brazil. Thus, this study aimed to carry out a spectro-temporal analysis of the Paraopeba River water given the dam burst, using multispectral images from the MSI sensor onboard Sentinel-2 satellites. For this analysis, sections along the river were defined by the creation of buffers, with 10-km intervals each, starting from the origin of the burst. For each section, the average visible to near-infrared (NIR) reflectance values per band and the Normalized Difference Water Index (NDWI) were obtained. We found that the red edge and NIR bands (B5, B6, B7, B8, and B8A) showed higher reflectance values when compared to the visible bands in the months immediately after the disaster, especially in the first 20km. In these months, negative NDWI values were also found for almost all sections downstream, demonstrating the large volume of mining tailings in the Paraopeba River. The seasonal variation of the observed values indicates the resuspension of the material deposited at the river bottom with the beginning of the rainy season. Finally, we highlight the usefulness of the MSI/Sentinel-2 red edge and NIR bands for further studies on the monitoring from space of water bodies subjected to contamination by large amounts of mud with iron ore tailings and contaminants, as occurred in the state of Minas Gerais, southeastern Brazil.

Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

Earth Observations Based Assessment of Impact of COVID-19 Lockdown on Surface Water Quality of Buddha Nala, Punjab, India

The novel coronavirus disease (COVID-19) halted almost all the industrial scale anthropogenic activities across the globe, resulting in improvements in water and air quality of megacities. Here, using Sentinel-2A data, we quantified impact of COVID-19 lockdown on the water quality parameters in one of the largest perennial creeks i.e., the Buddha Nala located in District Ludhiana in India. This creek has long been considered as a dumping ground for industrial wastes and has resulted in surface and ground water pollution in the entire lower Indus Basin. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Chlorophyll Index (NDCI), Nitrogen Content Index (NI), Normalized Difference Turbidity Index (NDTI), and Total Suspended Matter (TSM) were compared prior (2019) and during (2020) lockdown in the creek. There was a significant enhancement in NDVI, NDWI, NDCI, and NI values, and reduction in NDTI and TSM values during the lockdown period. When compared with prior year (2019), the values of indices suggested an improvement in water quality and an indicative change in aquatic ecology in the creek. The impact of the COVID-19 lockdown on the improvement in water quality of Buddha Nala was more evident in the upstream and downstream sections than the middle section. This is intriguing since the middle section of the creek was continually impacted by domestic household effluents. The earth observation inspired methodology employed and findings are testament to the discriminatory power to employ remote sensing data and to develop protocols to monitor water quality in regions where routine surveillance of water remains cost prohibitive.

Detecting Offshore Drilling Rigs with Multitemporal NDWI: A Case Study in the Caspian Sea

Offshore drilling rigs are the foundation of oil and gas exploitation in water areas. Their spatial and temporal distribution, state attributes and other information directly reflect the exploitation level of regional oil and gas resources. Therefore, it is very important to build an automatic detecting method for offshore drilling rigs with good performance to accurately capture the temporal and spatial distribution and state of oil and gas exploitation activities. At present, there are two main groups of methods for offshore drilling rigs: invariant feature-based methods and nighttime firelight-based methods. Methods based on invariant location are more subjective in terms of their parameter settings and require intensive computation. Nighttime light-based methods are largely unable to identify offshore drilling rigs without associated waste gas ignition. Furthermore, multiple offshore drilling rigs in close proximity to one another cannot be effectively distinguished with low spatial resolution imagery. To address these shortcomings, we propose a new method for the automatic identification of offshore drilling rigs based on Landsat-7 ETM+ images from 2018 to 2019, taking the Caspian Sea as the research area. We build a nominal annual cloud and cloud shadow-free Normalized Difference Water Index (NDWI) composite by designing an optimal NDWI compositing method based of the influence of cloud and cloud shadow on the NDWI values of water, bare land (island) and offshore drilling rigs. The classification of these objects is simultaneously done during the compositing process, with the following rules: water body (Max_NDWI &gt; 0.55), bare land (island) (Min_NDWI &lt; −0.05) and offshore drilling rig (0 &lt; Mean_NDWI &lt; 0.4). A threshold segmentation and postprocessing were carried out to further refine the results. Using this method, 497 offshore platforms were automatically identified using a nominal annual cloud and cloud shadow-free NDWI composite image and Google Earth Engine. Validation using Sentinel-2 Multispectral Imager (MSI) and Google Earth images demonstrated that the correct rate of offshore drilling rig detection in the Caspian Sea is 90.2%, the missing judgment rate is 5.3% and the wrong judgment rate is 4.5%, proving the performance of the proposed method. This method can be used to identify offshore drilling rigs within a large water surface area relatively quickly, which is of great significance for exploring the exploitation status of offshore oil and gas resources. It can also be extended to finer spatial resolution optical remote sensing images; thus small-size drilling rigs can be effectively detected.

Evaluating Impacts of Irrigation and Drought on River, Groundwater and a Terminal Wetland in the Zayanderud Basin, Iran

The Zayanderud Basin is an important agricultural area in central Iran. In the Basin, irrigation consumes more than 90 percent of the water used, which threatens both the downstream historical city of Isfahan and the Gavkhuni Wetland reserve—the final recipient of the river water. To analyze impacts of land use changes and the occurrence of metrological and hydrological drought, we used groundwater data from 30 wells, the standardized precipitation index (SPI) and the streamflow drought index (SDI). Changes in the wetland were analyzed using normalized difference water index (NDWI) values and water mass depletion in the Basin was also assessed with gravity recovery and climate experiment (GRACE)-derived data. The results show that in 45 out of studied 50 years, the climate can be considered as normal in respect to mean precipitation amount, but hydrological droughts exist in more than half of the recorded years. The hydrological drought occurrence increased after the 1970s when large irrigation schemes were introduced. In recent decades, the flow rate reached zero in the downstream part of the Zayanderud River. NDWI values confirmed the severe drying of the Gavkhuni Wetland on several occasions, when compared to in situ data. The water mass depletion rate in the Basin is estimated to be 30 (±5) mm annually; groundwater exploitation has reached an average of 365 Mm3 annually, with a constant annual drop of 1 to 2.5 meters in the groundwater level annually. The results demonstrate the connection between groundwater and surface water resources management and highlight that groundwater depletion and the repeated occurrence of the Zayanderud River hydrological drought are directly related to human activities. The results can be used to assess sustainability of water management in the Basin.

Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms

Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance.

Variations in Surface Albedo Arising from Flooding and Desiccation Cycles on the Bonneville Salt Flats, Utah

AbstractDesert playas, such as those in northern Utah, form a landscape often in stark contrast to surrounding mountain ranges due to their minimal topographic relief, lack of vegetation, and saline soils. Dry highly reflective halite surfaces, which make up many of the desert playas in northern Utah, are generally characterized by a surface albedo over 40%. However, their albedo can be reduced abruptly to less than 20% by flooding due to rainfall, runoff from surrounding higher terrain, or surface winds transporting shallow water across the playas. A weather station installed during September 2016 to study the Bonneville Salt Flats (BSF) in northern Utah provides estimates of surface albedo that can be related to cycles of flooding and desiccation of the halite surface. The normalized difference water index (NDWI) derived from the MODIS MOD09A1 land surface reflectance product estimates the fractional coverage of surface water over the BSF. NDWI values computed over 8-day periods from 2000 to 2018 highlight year-to-year and seasonal variations in playa flooding events over the BSF. Periods of playa flooding were observed with both ground-based observations and NDWI with sharp reductions in albedo when the surface is flooded.

Applicability of Remote Sensing-Based Vegetation Water Content in Modeling Lightning-Caused Forest Fire Occurrences

In this study, our aim was to model forest fire occurrences caused by lightning using the variable of vegetation water content over six fire-dominant forested natural subregions in Northern Alberta, Canada. We used eight-day composites of surface reflectance data at 500-m spatial resolution, along with historical lightning-caused fire occurrences during the 2005–2016 period, derived from a Moderate Resolution Imaging Spectroradiometer. First, we calculated the normalized difference water index (NDWI) as an indicator of vegetation/fuel water content over the six natural subregions of interest. Then, we generated the subregion-specific annual dynamic median NDWI during the 2005–2012 period, which was assembled into a distinct pattern every year. We plotted the historical lightning-caused fires onto the generated patterns, and used the concept of cumulative frequency to model lightning-caused fire occurrences. Then, we applied this concept to model the cumulative frequencies of lightning-caused fires using the median NDWI values in each natural subregion. By finding the best subregion-specific function (i.e., R2 values over 0.98 for each subregion), we evaluated their performance using an independent subregion-specific lightning-caused fire dataset acquired during the 2013–2016 period. Our analyses revealed strong relationships (i.e., R2 values in the range of 0.92 to 0.98) between the observed and modeled cumulative frequencies of lightning-caused fires at the natural subregion level throughout the validation years. Finally, our results demonstrate the applicability of the proposed method in modeling lightning-caused fire occurrences over forested regions.