Normalized Difference Vegetation Index Values Research Articles

Multitemporal sentinel-1 SAR data sensitivity analysis at phenological growth stages of green gram

ABSTRACT This research investigated the green gram crop by employing a time series of Sentinel-1A Single Look Complex (SLC) and Sentinel-2 datasets, which corresponded to different phenological growth stages of the crop. Field campaigns were conducted on the acquisition dates of Sentinel-1A datasets for the entire crop cycle to estimate crop height and soil moisture. In line with these observations, temporal variations in 10 salient Synthetic Aperture Radar (SAR) parameters, obtained from Sentinel-1A datasets, are systematically analysed, which elucidated the importance of dual-polarized SAR parameters in the diverse phenological growth stages of the crop. Moreover, the Normalized Difference Vegetation Index (NDVI) values from Sentinel-2 datasets, estimated at various green gram growth stages, are categorized into initial (NDVI ≤ 0.5), intermediate (0.5 < NDVI < 0.8), and maximum growth of the crop (NDVI ≥ 0.8), which are then aligned with corresponding SAR and ground-measured parameters. The inversion model development and sensitivity analysis were carried out to correlate ground-measured parameters with SAR parameters across various growth stages based on the categorization of NDVI values as well as for the entire growing season of the crop. Backscattering intensities are found to have good sensitivity to crop height at its intermediate growth (r ≥ 0.75). Among vegetation indices, the Polarimetric Radar Vegetation Index (PRVI) showed significant sensitivity to crop height in its intermediate growth stage (r ≈ 0.77). H-A-Alpha decomposition parameters displayed notable sensitivity to soil moisture in the initial and maximum growth stages of green gram. This study demonstrates the effectiveness of Sentinel-1 dual-polarized SAR parameters in monitoring green gram phenological growth stages. The parameters show substantial sensitivity to both green gram height and soil moisture.

Application of sensing techniques for quantifying CO2 flux and dynamics in environments affected by the fundão dam collapse, mariana, Brazil

The District of Mariana in Minas Gerais, Brazil, witnessed one of the largest natural disasters in history. It involved the deposition of thousands of cubic meters of mining waste on soils near tributaries of the Doce River, resulting in degraded areas and a new environmental landscape. Numerous studies have focused on changes in soil microbiota and physical-chemical attributes. However, studies quantifying and analyzing carbon flux (FCO2) dynamics in the field using proximal sensors under varying temperatures, humidity conditions, and vegetation cover are scarce and nearly nonexistent. These studies could aid in monitoring the recovery of degraded areas and in understanding FCO2 dynamics in such environments. The aim of this study was to quantify and assess FCO2 dynamics using combined sensing techniques in diverse mining-affected areas, comparing different stages of recovery and their vegetation impact, and linking the results to pedoenvironmental factors. To achieve this, four areas were carefully selected and evaluated: affected pasture (AP), pasture (P) (non-affected), mix (MIX), and native trees (NT). In each area, readings were taken using an Infrared gas analyzer (IRGA) sensor, and temperature and soil moisture were measured. Additionally, soil samples were collected for chemical, physical, and biological characterization. Normalized Difference Vegetation Index (NDVI) assessments were conducted using satellites to evaluate vegetation cover. Non-parametric tests (Kruskal Wallis and Principal Component Analyses - PCA) were employed to evaluate differences between the areas and to assess the correlation among the environmental variables. The P area had the highest FCO2 and total organic carbon (TOC), differing significantly from the other site areas. Conversely, AP had the lowest FCO2 and was distinct from other sites. The MIX and NT areas had intermediate FCO2 and TOC values, which were statistically similar to each other. PCA identified distinct patterns in FCO2, soil temperature, and moisture. FCO2 was positively correlated with soil temperature and negatively correlated with moisture. There was a relationship between the biological variables microbial quotient (qMic), metabolic quotient (qCO2), COT, and FCO2. qMic reached the highest values in the MIX area, decreasing linearly from NT to P. Conversely, AP had the lowest qMic. qCO2 had the highest value in AP and NT. The proximal IRGA sensor effectively quantified FCO2 and differentiated mining tailing-affected areas. This assessment incorporated soil temperature, moisture sensors, vegetation index, and soil attribute data. The FCO2 levels were higher in P areas and lower in AP areas. Elevated FCO2 levels were correlated with a high soil temperature and low humidity. In AP, qMic was low, and qCO2 was high, indicating lower FCO2 levels. Conversely, P exhibited a higher FCO2. Higher NDVI values were correlated with elevated FCO2 in areas with specific vegetation cover during environmental recovery, while lower NDVI areas had lower FCO2 levels.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Rice Response to Spermine Foliar Application and Its Association with Aerial Imagery Monitoring Under Water Stress Conditions

Rice is the most consumed food in the world, mainly in Asia and Africa. Malaysia is the second-largest rice importer in Southeast Asia after Indonesia. However, rice yield is limited by water stress. One alternative for a quicker strategy to mitigate water stress is through a combination of foliar spermine application and efficient rice management practices via image monitoring techniques using drone technology. The present study was aimed at evaluating the effects of spermine on rice physiological response and its association with aerial imagery and yield under reproductive during reproductive stage under water stress. The experiment was carried out under greenhouse conditions using a two-factorial randomized complete block design (RCBD), with foliar spermine treatment as the first factor and water stress as the second factor. Physiological parameters showed significantly higher tiller number per pot and photosynthesis rate by 29% and 31%, respectively. Correspondingly, the Normalised Difference Vegetation Index (NDVI) using aerial imagery monitoring showed an increased value in spermine treatments by 2% compared to control. Furthermore, NDVI readings and photosynthetic rate were positively correlated linearly with R2= 0.51. Interestingly, spermine treatments alleviated water stress effects by 40%, 17% and 12% in grain weight per pot, grain number per panicle and percentage filled grain. Biomass partitioning in roots improved by 44% in spermine treatments, even under water stress, due to an efficient translocation of assimilates. In conclusion, spermine foliar application significantly improved growth, grain filling and rice yield production, which was also supported by NDVI values using aerial imagery monitoring.

Examining the Land Use and Land Cover Impacts of Highway Capacity Expansions in California Using Remote Sensing Technology

Highway capacity expansion has wide impacts on land use and land cover (LULC). Cost–benefit analyses and environmental review processes for roadway capacity expansion and maintenance decisions do not comprehensively consider the LULC impacts. This study examined land cover changes directly associated with highway expansion in California, and the relationship between land use and the vegetation impacts of highway projects using satellite remote sensing data. The methodology involves a geospatial analysis of normalized difference vegetation index (NDVI) data in 18 sites across California before and after highway expansion project completion. We accounted for seasonality and included a set of control sites. Findings indicate that the impacts of highway expansion on changes in NDVI are diverse, stressing the importance of the environmental context around each individual project site. Sites that are located near less-developed areas with more extensive natural vegetation (e.g., sprawled areas or exurbs), show a significant decline in NDVI values. Virtually all sites with insignificant changes in NDVI after highway expansion are located in areas that already exhibit heavy urban development (e.g., Los Angeles, San José) or are otherwise located near large expanses of bare, non-vegetated earth. Also, project sites that experienced multiple types of construction (i.e., adding more lanes, widening sections, bridge renovation, etc.) were more likely to exhibit decreasing NDVI values compared with project sites that only experienced one type of construction. Decisions about highway construction and capacity expansion should consider the context and the full environmental impacts, including land use and land cover changes over time.

Meteorological drought in the upper Noteć catchment area (Central Poland) in the light of NDVI and SPI indicators

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland.

Mapping tree species of wetlands using multispectral images of UAVs and machine learning: A case study of the Dong Rui Commune

Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. The classification of mangrove species allows the determination of the habitat of each species, thereby serving as a basis for determining protection solutions and planning plans for mangrove conservation and restoration according to each environmental condition. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the object-based image analysis method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11 % and a kappa coefficient (K) of 0.87. The overall accuracy for Rhizophora stylosa was the highest (94.23 %), followed by Kandelia obovata (93.61 %) and Bruguiera gymnorrhiza (85.50 %). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands, and normalized difference vegetation index values were constructed, resulting in an overall accuracy of 78.22 % and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby serving as a basis for determining the distribution of each mangrove species according to natural conditions and contributing to the formulation of policies for afforestation and mangrove conservation in Dong Rui commune.

Assessing Golden Tides from Space: Meteorological Drivers in the Accumulation of the Invasive Algae Rugulopteryx okamurae on Coasts

Massive accumulations of invasive brown algae Rugulopteryx okamurae are exacerbating environmental and socio-economic issues on the Mediterranean and potentially Atlantic coasts. These golden tides, likely intensified by global change processes such as changes in wind direction and intensity and rising temperatures, pose increasing challenges to coastal management. This study employs the Normalized Difference Vegetation Index (NDVI), with values above 0.08 from Level-2 Sentinel-2 imagery, to effectively monitor these strandings along the coastline of Los Lances beach (Tarifa, Spain) in the Strait of Gibraltar Natural Park from 2018 to 2022. Los Lances beach is one of the most affected by the R. okamurae bioinvasion in Spain. The analysis reveals that wind direction determines the spatial distribution of biomass accumulated on the shore. The highest average NDVI values in the western patch were observed with south-easterly winds, while in the eastern patch, higher average NDVI values were recorded with south-westerly, westerly and north-westerly winds. The maximum coverage correlates with elevated temperatures and minimal rainfall, peaking between July and October. Leveraging these insights, we propose a replicable methodology for the early detection and strategic pre-shore collection of biomass, which could facilitate efficient coastal cleanup strategies and enhance biomass utility for biotechnological applications. This approach promises cost-effective adaptability across different geographic areas impacted by golden tides.

Effect of uncontrolled industrialization on environmental parameter: A case study of Mongla EPZ using machine learning approach

Unplanned and uncontrolled industrialization leads to environmental pollution, which ends up impacting human life and destroying the economy. Especially in the era of global warming, coastal regions worldwide are the most vulnerable and hold significant ecological importance for human habitation. In 1998, the establishment of the Mongla Export Processing Zone (MEPZ) in the coastal town of Mongla Thana, which is already famous for its seaport, led the area to the challenges of salinity intrusion and the shrinking of agricultural land and its fertility. Unplanned industrialization in the area causes vegetation loss, severe droughts, and other environmental challenges, threatening local biodiversity and agricultural sustainability. In this paper, the effects of unplanned industrialization inside the Mongla EPZ on the area land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and urban heat island (UHI) spanning from 2007 to 2023 have been investigated. Along with that, a machine-learning-based artificial neural network (ANN) model was employed to forecast the situation in 2027 and 2031. Our industrial settlement analysis reveals that a substantial rise in industrial building was seen in 2015 in the EPZ area, whereas the EPZ area was almost settlement-free before 2011. With this increase in 2015, above 2% of the total municipal area faced drought, which will become over 30% by 2023. The NDVI values are decreasing year-wise, which reveals that the area is becoming less vegetation-rich. Also, the increasing industrial activities in the EPZ led to an LST increment. Our CA-ANN algorithm-based future prediction shows that about 30% of the whole municipality will face LST 27 °C by 2031. Along with that, the area's UHI value, over 2 °C higher than the rural surrounding area, will reach 6.5% by 2031. Our findings indicate that the municipal area will face a devastating future, including vegetation loss, a high probability of severe drought, and ultimately, environmental degradation. This study will help raising awareness and decision-making process to mitigate the environmental risks and supporting sustainable development.

Satellite monitoring of a pine forest affected by mountain pine beetle

The spread of bark beetles poses a significant threat to forest ecosystems, causing substantial damage to coniferous forests. The mountain pine beetle (Dendroctonus spp.) is considered to be one of the most harmful bark beetles, infesting pine trees and leading to severe consequences for forest ecosystems. This article aims to detect and analyze the dynamics of mountain pine beetle infestation in a pine forest stand, determine its affected area, and evaluate the effectiveness of sanitary and restoration measures. The study utilized satellite data from the Sentinel-2 mission and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to monitor the spread of mountain pine beetle infestation. The analysis was conducted for a pine forest stand in the Ostriv Forestry of the Vyshchedubechanske Forestry Enterprise in Kyiv Oblast, Ukraine. The results demonstrated the effectiveness of using satellite data and vegetation indices for monitoring mountain pine beetle infestation. The combined application of NDVI and NDMI enabled the detection of infested areas in the early stages by capturing changes in the spectral characteristics of tree crowns and vegetation moisture content. The satellite data analysis confirmed the complete infestation of a 0.01 km2 pine forest stand by the mountain pine beetle in 2017. A link between the activation of beetle spread and climate changes, particularly rising temperatures and decreasing precipitation, was observed, leading to the weakening of pine stands. The study revealed that in 2018, sanitary felling of the infested pine stand was carried out, as evidenced by the decrease in NDVI values. In 2019, a project to restore the forest ecosystem through the establishment of a new pine plantation was initiated. Satellite observations in April 2024 demonstrated successful growth of the young pine forest, with NDVI values indicating an increase from 0.25 in April 2020 to 0.45 in April 2024, reflecting active vegetation growth and successful ecosystem restoration. The obtained results highlight the importance of using satellite data for timely detection of pest outbreaks in forest ecosystems, determining the extent of infestation, monitoring the effectiveness of sanitary measures, and assessing the recovery of forest stands after restoration efforts. The study underscores the potential of satellite remote sensing techniques in forest ecosystem monitoring and supports decision-making processes for sustainable forest management.

Research on the Temporal and Spatial Changes and Driving Forces of Rice Fields Based on the NDVI Difference Method

Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing’s Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields’ dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography.

The link between the normalized difference vegetation index in major crops and meteorological factors

Normalized difference vegetation index (NDVI) is frequently used in monitoring of meteorological events. The goal of this study was to establish the relationship between the spatial NDVI with air temperature and precipitation amounts in major crops cultivated in the steppe zone of Ukraine, namely, winter wheat, winter rapeseed, grain corn, soybeans, and sunflower. The study included the croplands of eight regions: the Crimea, Kherson, Mykolaiv, Odesa, Zaporizhzhia, Dnipropetrovsk, Kirovohrad, and Kharkiv. The yield data were retrieved from the official bodies of the State Statistical Service of Ukraine. The meteorological data on monthly air temperature and rainfall were retrieved from regional hydrometeorological centers. The NDVI values were retrieved from the GIMMS Global Agricultural Monitoring System, which provides Terrain MODIS NDVI 8-Day smoothed time series with 250 m resolution. The study was performed for the 2021-2022 for winter rapeseed and sunflower; 2017 and 2020 for grain corn; 2022 for winter wheat; and 2017 for soybeans. We found almost no or weak correlation between monthly NDVI values of the studied crops and rainfall amounts. A stronger correlation was found for air temperature, with the greatest values of the correlation coefficient of 0.76, 0.72, –0.72 for sunflower, soybeans, and winter wheat, respectively. The linear regression models, developed to predict NDVI based on the air temperature for the mentioned crops, provided good prediction accuracy with the relative error within 10–20%. The best overall fit and accuracy was produced by the model of sunflower’s NDVI. Only winter crops were observed to have a negative correlation with air temperature, suggesting that the cultivated varieties of these crops are heat-intolerant.

Estimation of Soil Moisture from Multispectral Remote Sensing Data

Soil moisture is an important parameter in agricultural and environmental studies and affects the exchange of water and energy at the boundary between the earth’s surface and the atmosphere. Accurate estimation of spatial-temporal changes in soil moisture is critical for numerous studies in the agricultural sciences. Vegetation indices, based on combinations of digital data of reflected light in the visible and near-infrared range, are suitable for the detection of water stress in plants. Therefore, they are widely used to monitor and detect drought conditions, but their accuracy is dependent on different vegetation types. The main objective of this research was to develop a novel procedure for the estimation of soil moisture using multispectral remote measurements. This study has been done using a multispectral camera Survey 3W Red+Green+NIR, which records images in three spectral channels: green (550 nm), red (660 nm) and near-infrared (850 nm). Eleven soil samples with different water content were investigated. SNAP (Sentinel Application Platform) software was used to process the captured multispectral images. It allows an easy calculation of various vegetation indices. The suitability of the normalized difference vegetation index (NDVI) for the assessment of soil moisture was evaluated. The average NDVI values did not indicate a well-established trend in relation to the SWC. A new simple vegetation index NIR/Green was successfully used for the assessment of soil moisture. The new NIR/Green index gives consistent results in relation to the real soil water content and could be used for mapping of the soil moisture with multispectral cameras.

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.

Urban land use, land cover change and urban microclimate dynamics in Addis Ababa, Ethiopia

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

Indigenous people doing citizen science to assess water quality using the BMWP in rivers of an arid semi-arid biosphere reserve in Mexico

Arid and semi-arid areas are among the most threatened ecosystems on the planet. The Tehuacán-Cuicatlán Biosphere Reserve (TCBR), in southeastern Mexico, is an arid and semi-arid area with high biological diversity and human settlements of eight ethnic groups. Two rivers drain the reserve, Río Grande (RG) and Río Salado (RS), which are not subject to water quality monitoring by government agencies; however, measures of water quality of these rivers are needed to focus conservation actions on this resource. This work aimed to test the effectiveness of participatory water quality monitoring with the participation of three actors: Reserve management leaders, local communities, and academics, to monitoring water quality in the rivers of the TCBR. Ninety-two residents were trained to carry out water quality biomonitoring using the Biological Monitoring Working Party (BMWP) index calibrated for the reserve. The BMWP uses macroinvertebrate families to display numerical and categorical water quality scores. Additionally, the Water Quality Index (WQI) was assessed and the Normalized Difference Vegetation Index (NDVI) of the riparian zones was estimated in each study site. The mean WQI scores were 69.24 for RS (no treatment necessary for most crops and necessary treatment for public water supply) and 75.16 for RG (minor purification for crops requiring high-quality water and necessary treatment for public water supply). The BMWP showed five water quality categories (Excellent, Very Good, Good, Fair, and Poor), showing higher water quality scores in the upper portion of the basins and capable of discriminating study sites with lower scores close to human settlements. At one study site, data from participatory monitoring impelled actions taken to address a pollution source and influenced policy focus, reaching the maximum level of participatory-based monitoring. This led to avoid the discharge of wastewater into the river to conserve and protect the water resource. WQI is closely related to BMWP; however, the latter was far more sensitive to detecting areas affected by domestic water discharges. The NDVI presented low values for the TCBR, being lower in RS (the driest area). Although the NDVI showed a weak relationship with BMWP values, areas with higher NDVI values generally achieved higher BMWP values. The results of this study highlight the high sensitivity of the BMWP to detect several water quality conditions in the rivers running through the TCBR when compared to WQI. In addition, the usefulness of biomonitoring using the BMWP index was evident, as well as the importance of the participation of local inhabitants contributing to the knowledge of water quality in biosphere reserves and carrying out timely measures that allow the rivers in these reserves to be maintained in good condition.

Post-mined reclamation condition assessment by Normalized Difference Vegetation Index (NDVI)

Post-mined reclamation is an essential phase in coal mining operations, ensuring that the ex-mining area can function again according to its intended purpose in the future. This study aimed to elucidate the differences in planting years of post-mined reclamation at a coal mine in the Paringin area, South Kalimantan, Indonesia, and compared them to undisturbed areas for the years 2003 and 2023. NDVI was used for land cover analysis based on the Ministry of Energy and Mineral Resource Regulation, and the ultimate criterion for 100% success in post-mined reclamation is vegetation canopy coverage. The NDVI results showed an improvement in NDVI values for the post-mined reclamation area from 2003 to 2023, indicating an improvement in land cover due to the revegetation process. In 2023, the NDVI results of the post-mined reclamation for eight and twenty years of tree planting showed NDVI values of 0.6-0.7 (moderate vegetation). However, the results of field observations of the twenty-year post-mined reclamation tree condition indicate the non-survival of fast-growing tree species, such as Albizia chinensis. The composition of fast-growing and local tree species, with a minimum proportion of 60:40, and systematic tree distribution across the post-mined reclamation area are required to maintain canopy coverage of the post-mined reclamation area in the long term. Fast-growing tree species, as pioneers, have short to medium life periods. Therefore, the ultimate criterion for 100% success in post-mined reclamation for canopy coverage should be based on local tree species, serving as the basis for releasing the reclamation bond.

USG matrix analysis and power interest to improve community environmental awareness: A case study of mangrove land cover to support community and environmental education

This study employed a mixed-method approach to analyze the composition of mangrove forests. The transect method and remote sensing through supervised classification using Google Earth Engine (GEE) were utilized to assess changes in mangrove areas in 2017, 2019, and 2021. The findings suggest that this study should be included in population and environmental education courses. The results revealed that Avicennia marina mangroves had the highest importance index (INP) values at three different locations. Between 2017 and 2019, mangrove areas decreased from 30.62 hectares to 27.98 hectares. However, from 2019 to 2021, the mangrove area increased from 27.98 hectares to 29.18 hectares, largely due to reforestation efforts in the Pulau Dua Nature Reserve. The NDVI (Normalized Difference Vegetation Index) values indicated "bushy" criteria, ranging from 0.43 to 1.00. The Normalized Difference Mangrove Index (NDMI) values fell into the "Rare" (-1.00 to 0.32) and "Medium" (0.33 to 0.43) categories. The Urgency, Seriousness, and Growth (USG) matrix analysis and Power Interest assessment identified illegal logging, erosion, and waste as significant causes of mangrove decline. Stakeholders, including village chiefs, religious leaders, traditional leaders, and youth leaders, must focus on preserving the mangrove ecosystem in the CAPD. The study's results are vital for educational purposes, particularly in population and environmental education courses. These courses should address environmental issues, prevention strategies, and conservation activities, which can be integrated into the curriculum. This will enable youth to contribute effectively to environmental awareness programs.

Global Normalized Difference Vegetation Index forecasting from air temperature, soil moisture and precipitation using a deep neural network

The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stability, which is vital for ensuring sustainable food production. This study aims to develop a global-scale NDVI forecasting model based on deep learning algorithms that consider climate variables. The model was trained using three years of global data, including NDVI, temperature, precipitation, and soil moisture. The results of this study demonstrate the effectiveness of the deep learning model for forecasting NDVI. The model accurately predicted NDVI values, as evidenced by the high coefficient of determination (R2) values and the negligible average disparity between predicted and observed NDVI values. The study conducted an analysis of the model’s performance both temporally and spatially. The performance of the model was examined for each month and the overall performance of the model for months presented as the model’s temporal performance overall. Additionally, the model’s performance was analyzed at different latitudes, categorized as mid-latitude and low-latitude performance. The temporal analysis of the model demonstrated an overall R2 value of 0.85 and an RMSE of 0.096. Meanwhile, the spatial analysis of the model showed that it performed well at low-latitude, with an R2 value of 0.84 and an RMSE of 0.098, and at mid-latitude, with an R2 value of 0.82 and an RMSE of 0.095. This suggests that the model’s forecasted NDVI values showed a small average difference compared to actual values in both temporal and spatial analyses. Overall, the study supports the idea that deep learning models can effectively forecast NDVI using climate variables across various geographical zones and throughout different months of the year.