Soil Adjusted Vegetation Index Research Articles

Assessing soil productivity potential in arid region using remote sensing vegetation indices

Remote sensing techniques offer practical benefits, particularly in sensitive ecosystems or areas with limited accessibility. However, field surveys allow for more accurate and detailed information about soil properties and productivity. Therefore, it is often recommended to combine remote sensing techniques with field surveys in order to obtain comprehensive and reliable results. The underlying basis of this study involves analyzing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI), as indicators of soil productivity. By utilizing biomass density as an additional indicator, the study aims to provide valuable insights into the productivity potential of agricultural areas. The results demonstrate a positive association between the Soil Productivity Rating (SPR) and wheat yield values for the year 2022, as evidenced by a coefficient of determination (r2) value of 0.8214. This value indicates a moderately strong correlation between the SPR classes and wheat yields. Throughout the Ripening period, the NDVI and EVI indices exhibited a relatively strong correlation coefficient (r2 = 0.987 and 0.873, respectively). On the other hand, the SAVI index displayed moderate to strong accuracy in estimating crop yield, with a coefficient of determination (r2) ranging from 0.819 to 0.908. These results suggest that the NDVI index serves as the most dependable predictor of yield during all vegetation periods. This study provides a comprehensive understanding of soil productivity, but further research using controlled trial patterns with differential reference plants is needed for validation and improvement.

Consistent land use and land cover classification across 20 years of various high-resolution images for detecting soil sealing in murcia, Spain

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

Machine Learning-Based Fine Classification of Agricultural Crops in the Cross-Border Basin of the Heilongjiang River between China and Russia

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is to find an appropriate VI to analyse the spatio-temporal evolution of olive water stress using LST images and VIs derived from Landsat 5 and 8 satellites in the semi-arid region of southern Peru. For this purpose, VIs (Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2) and Soil Adjusted Vegetation Index (SAVI)) and LST were calculated. The information was processed in Google Earth Engine (GEE) for the period 1985 to 2024, with an interval of every five years for the summer season. The triangle method was applied based on the LST-VIs scatterplot analysis, a tool that establishes wet and dry boundary conditions for the Temperature Vegetation Dryness Index (TVDI). The results indicated a better appreciation of olive orchard water stress over time, with an average of 39% drought (TVDINDVI and TVDISAVI), 24% severe drought (TVDINDVI) and 25% (TVDISAVI) of the total area, compared to TVDIEVI2, which showed 37% drought and 16% severe drought. It is concluded that TVDINDVI and TVDISAVI provide a better visualisation of the water stress map of the olive crop and offer a range of options to address current and future problems in water resource management in the olive sector in semi-arid areas of southern Peru.

Response of spectral vegetation indices to Erannis jacobsoni Djak. damage in larch forests

Erannis jacobsoni Djak. (EJD), a typical pest of coniferous forests in Mongolia, has severely threatened forest areas in recent years owing to its rapid development and spread. EJD feeds on needles and leaves, killing many trees and causing severe damage to forest ecosystems，which results in substantial local economic losses. The rapid and effective monitoring of forest pests is crucial for preventing or controlling infestations in a timely manner. To this end, in this study, we calculated spectral vegetation indices using UAV multispectral data, assessed ground survey data to determine the degree of pest damage, and conducted sensitivity analysis on the spectral vegetation indices. Nine sensitive spectral vegetation indices were selected to analyze the intramonthly and intermonthly variations in the spectral vegetation indices of forests during EJD infestation: the chlorophyll red-edge parameter index (CIreg), corrected NIR/IR simple ratio (GMSR), intensity index (Int and Int2), improved NIR/red-edge simple ratio (MSRreg), normalized difference NIR vegetation index (NDSI), soil adjusted vegetation index (SAVI), and salinity index (SI2reg and SI3). The results demonstrated that the variance F values of the sensitive spectral vegetation indices after screening using the successive projection algorithm were highly significant at the α=10−10 level, suggesting that these indices are highly sensitive to the level of pest damage. The intramonthly results were as follows: in June, CIreg, GMSR, Int, Int2, MSRreg, SAVI, SI2reg, and SI3 decreased with increasing pest damage, whereas NDSI increased; in August, the difference in index values between light, medium, and heavy damage and healthy stands was not significant; and in September, most of the index differences changed to mild > moderate > severe. Regarding the intermonthly results, the magnitude of the vegetation index values for each sensitive spectrum at different hazard levels was ranked as June > September > August, and the overall difference varied as δ3>δ2>δ1. The spectral vegetation indices apparently responded to different levels of pest damage, making them suitable for quickly and accurately monitoring the occurrence and development of forest pests. These results provide a reference for the monitoring of forest pests at spatial and temporal scales.

Quantitative Soil Quality Assessment by Using Visual Soil-Field Assessment Tools (VS-FAST)

Unplanned land use reflects the changes from agricultural to urbanization and decreasing of green spaces in cities and agricultural lands surrounding cities. The Soil Adjusted Vegetation Index (SAVI), Dry Bare - Soil Index (DBSI) and Normalized Difference Built-up Index (NDBI) indices were derived to evaluate the rate of the land use/cover changes due to uncontrolled urban sprawl for the years 2001 and 2020. Field indicators were also used to assess soil properties’ degradation by using Land Degradation Assessment in Drylands (LADA) under the FAO Global Environment Facility (GEF) project. A set of 30 soil samples were randomly collected from two locations: unplanned built-up and vegetated sites in Babil Governorate, Iraq. The results showed that soil texture and salinity were the most varied indicators. The poorer areas (unplanned urban areas) were low and assessed as poor quality, while the vegetated site was assessed as moderate quality with high total score values. The findings from spectral indices show a significant rise in the built-up land use/cover category, expanding from 10.367 square kilometres in 2001 to 50.883 square kilometres in 2020. In contrast, the areas of bare-soils and vegetated decreased from 29.29 and 35.474 square kilometres in 2001 to 12.511 and 19.601 square kilometres in 2020, respectively. Various analytical techniques proved instrumental in distinguishing the different land use/cover categories in this research.

Using vegetation and chlorophyll indices to model above ground biomass over time in an urban arboretum in subtropical queensland

As urban forests grow, they can provide a range of ecosystem services, including by sequestering carbon in the form of above ground biomass (AGB). Data from satellites is starting to be used to estimate ecological functions of urban forests including AGB. In this study, we compare individually and in combination five vegetation index and three chlorophyll specific indices based on Sentinel-2 satellite images in spring (September to November) of a newly planted multi-species arboretum over eight years in subtropical Queensland, Australia. There was variation in the eight indices within the arboretum, but some clear patterns among indices over time reflecting the growth of the forest. Using a published simple regression model of AGB as a function of Leaf Area Index (LAI), we found Modified Soil Adjusted Vegetation Index (MSAVI) and Modified Chlorophyll Absorption Ratio Index (MCARI) best predicted AGB (r-square = 0.52 and r-square = 0.84, respectively), and in combination gave the best estimate (r-square = 0.85). The performance of these chlorophyll-specific indices (MSAVI and MCARI) reinforces their benefits over traditional vegetation indices in characterizing vegetation patterns and could complement field-based sampling when modelling carbon sequestration potentials including in mixed species urban forests.

Geospatial analysis of flood risk hazard in Zambezi Region, Namibia

The recent decade has seen an increase in frequency and intensity of flood risk globally especially in less developed countries. This has been attributed to many factors such as population growth, urbanization, climate change, increasing precipitation, and poor solid waste management among others. This study used quantitative analysis to identify and characterise flood-prone areas in Kabbe and Kaltima in the Zambezi region. We estimated major factors influencing flood events in the study area. We estimated this by incorporating the analytical hierarchy process (AHP) and GIS-based multi-criteria decision-making to map and identify flood-prone areas in Kabbe and Katima. AHP was employed to ascertain the weight of each criterion taken into consideration for the susceptibility mapping. We analysed ten factors elevation, slope, distance to river, rainfall, topographic wetness index, distance to road, drainage density, land use land cover, modified soil adjusted vegetation index, and soil which are closely associated with flood occurrence in the study area. The flood factors maps were categorized into five susceptibility levels. We used field data by interviewing key informants and community members to validate the GIS analysis. The result from the flood susceptibility map indicates that 46 % of areas in Kaltima and Kabbe have a low susceptibility to flood, 56.04 % are moderate, 43.33 % are high and 17 % are very highly susceptible to flood. The areas susceptible to flooding are mostly low-laying areas in Kabbe which are gentle slopes and are sitting at approximately 921–935 m below sea level. Speaking with stakeholders in the area, they confirmed that communities such as Ihaha, Isize, Mbalasinte, and Kalumnesa are highly susceptible to flooding. The community members confirm that they experience flooding every year with devastating impacts on their lives and livelihoods. We recommend localized flood management strategies that cater to the needs of the communities and develop strategies to improve preparedness, response, and mitigation. Increasing government funding for flood risk management in the area can increase the capacity of the community for flood risk management through training and awareness

Role of remote-sensing techniques in unveiling the spatiotemporal response of vegetation to climate change in the western Makkah Province of Saudi Arabia

Climate change is a global problem that dramatically affects natural resources, resulting in significant changes in temperature, precipitation, and humidity, which affect vegetation cover. Under this light, this study aimed to identify the potential of remote-sensing techniques to reveal the spatiotemporal response of vegetation cover to climate change in the western Makkah Province using Landsat-5 Thematic Mapper, Landsat-8 operational land imager, Global Land Data Assimilation System model, Global Precipitation Measurement, and Famine Early Warning Systems Network Land Data Assimilation System model data from 2000 to 2023. Optimised Soil-Adjusted Vegetation Index (OSAVI), classification, overlay, change detection, and correlation analysis were utilized to process data. Time series analysis of data revealed climate-related changes which were particularly intense in recent years. Specifically, temperature, precipitation, and specific humidity were found to differ depending on the landforms and season. Temperature was higher during the dry season compared to the wet season. A decrease was observed in the overall precipitation rate, which did not exceed 81.39 mm during the wet season and approximately 11.46 mm during the dry season. Additionally, precipitation increased in 2023 but decreased in 2018. Moreover, the study area was located on semi-arid lands for all years except for the wet season of 2023. OSAVI analysis, which is sensitive to climate change, revealed that vegetation coverage can be both positively and negatively affected by climate change. The most profound vegetation coverage in the study region was observed in 2023. A strong correlation was also observed between precipitation and vegetation in the study area, which showed less high-greenness in the dry season and more widespread grasses. The implications of these findings for the development of strategies for biodiversity conservation in semi-arid regions are significant.

Soil Adjusted Vegetation Index, Normalize Difference Buildup Index, and Land Surface Temperature between 1987 and 2023 in Abuja Municipal Area Council, Nigeria

Abuja Municipal Area Council (AMAC) is experiencing rapid urban expansion, which is expected to impact land surface temperatures (LST). This paper evaluates the trends in soil adjusted vegetation index (SAVI), normalize difference buildup index (NDBI), and land surface temperature (LST) between 1987 and 2023 in AMAC using Landsat 4 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery, respectively. Results show that in 1987, SAVI ranged from -0.126 to 0.477, NDBI from -0.186 to 0.678, and LST from 27.18 to 46.4 oC. In 2023, SAVI ranged from -0.253 to 0.71, NDBI from -0.308 to 0.619, and LST from 23.89 to 46.57 oC. Analysis showed an increase in vegetation in 2023 compared to 1987. Built-up and bareland areas became more concentrated in the northeast in 2023 compared to 1987, and temperature reductions were observed in areas with increased vegetation, notably in the south and southwest. Correlation analysis indicated a strong negative relationship (-0.772) between SAVI and LST in 1987, weakening in 2023 (-0.389). NDBI and LST remained moderately positively correlated (0.645 in 1987, 0.621 in 2023). Significant differences (P<0.01) were observed between 1987 and 2023 SAVI, NDBI, and LST values. These findings have important implications for environmental monitoring, and urban planning in rapidly urbanizing areas such as AMAC

A Novel Approach to Detecting the Salinization of the Yellow River Delta Using a Kernel Normalized Difference Vegetation Index and a Feature Space Model

Using the kernel normalized difference vegetation index (KNDVI) to monitor soil salinization has great advantages; however, approaches using KNDVI and a feature space model to monitor salinization have not yet been reported. In this study, the KNDVI, normalized difference vegetation index (NDVI), extended difference vegetation index (EDVI), green normalized difference vegetation index (TGDVI), modified soil-adjusted vegetation index (MSAVI), and salt index (SI) were used to establish five feature space monitoring indices for salinization. The spatio-temporal evolution pattern of soil salinization in the Yellow River Delta from 2000 to 2020 was analyzed based on the optimal monitoring index. The remote sensing monitoring index model based on KNDVI-SI’s point-to-point mode had the best applicability with R2 = 0.93, followed by EDVI-SI’s salinization monitoring index model with R2 = 0.90. From 2000 to 2020, soil salinization in the Yellow River Delta followed an exacerbating then improving trend. Soil salinization was more severe in the northern and eastern coastal areas of the Yellow River Delta. These results are conducive to salinization restoration and control in the Yellow River Delta.

Impacts of mining on vegetation phenology and sensitivity assessment of spectral vegetation indices to mining activities in arid/semi-arid areas

Measuring the impact of mining activities on vegetation phenology and assessing the sensitivity of vegetation indices (VIs) to it are crucial for understanding land degradation in mining areas and enhancing the carbon sink capacity following the ecological restoration of mines. To this end, we have developed a novel technical framework to quantify the impact of mining activities on vegetation, and applied it to the Bainaimiao copper mining area in Inner Mongolia. Phenological indices are extracted based on the VI time series data of Sentinel-2, and changes in phenological differences in various directions are used to quantify the impact of mining activities on vegetation. Finally, indicators such as mean difference, standard deviation, index value distribution interval, and concentration of index value distribution were selected to assess the sensitivity of the Enhanced Vegetation Index (EVI), Green Chlorophyll Index (GCI), Global Environmental Monitoring Index (GEMI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Red-Edge Chlorophyll Index (RECI), and Soil-Adjusted Vegetation Index (SAVI) to mining activities. The results of the study show that the impact of mining activities on surrounding vegetation extends to an area three times larger than the actual mining activity area. When compared with the reference and unaffected areas, the affected area experienced a delay of approximately 10 days in seasonal vegetation development. Environmental pollution caused by the tailings pond was identified as the primary factor influencing this delay. Significant variations in the sensitivity of each VI to assess mining activities in arid/semi-arid areas were observed. Notably, GCI, GNDVI and RDVI displayed relatively high sensitivity to discrepancies in the spectral attributes of vegetation within the affected area, while SAVI reflected the overall spectral stability of the vegetation in the affected area. The research findings have the potential to provide valuable technical guidance for holistic environmental management in mining areas and hold great significance in preventing further land degradation and supporting ecological restoration in mining areas.

Vegetation cover change in Ugam Chatkal National Park, Uzbekistan, in relation to climate variables during the post-Soviet period (1991-2022)

This paper presents a comprehensive study relating to the vegetation cover change in Ugam Chatkal National Park (Uzbekistan) and its relation to climate change during the post-Soviet period (1991-2022). The study utilises remote sensing technology, specifically the Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), to monitor spatio-temporal changes in vegetation. Landsat satellite imagery and meteorological data, including temperature and precipitation records form the basis of the analysis. The research aims to understand the impact of climatic factors, such as air temperature, soil temperature and precipitation on vegetation cover. Statistical methods, for example Pearson’s correlation analysis are employed to determine the strength and direction of relationships between these variables. The study reveals that both NDVI and SAVI are strongly correlated with air and soil temperatures, indicating the significant influence of these climatic factors on vegetation health and growth. The findings suggest that changes in vegetation cover in the Ugam Chatkal National Park are closely tied to climate change, with air temperature revealing a substantial correlation with time, indicating a trend towards increasing temperatures. The study also forecasts future climatic and vegetation trends, predicting an increase in air temperature, precipitation and vegetation cover over the next four decades. In particular, the research highlights the magnitude of monitoring and understanding the complex interactions between climate change and vegetation dynamics, which are crucial for environmental management and policy-making in the region.

Spectral imaging of grass species in arid ecosystems of Namibia

Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

The landscape altered the interaction between vegetation and climate in the desert oasis of Hotan River Basin, Xinjiang, China

In the arid region, the oasis is characterized by a desert substance and a mosaic of vegetation. The biophysical processes and interactions between vegetation and the local climate in this kind of region are determined by the macroscopic structure of the ecosystems, i.e. landscape patterns. To understand how these landscape patterns impact regional hydro-heat coupling across space and evolve over time, we utilized remote sensing observational data and methods to examine the relationships among these factors. In this case, we focused on the oasis along the upstream of the Hotan River Basin in the Taklamakan Desert in Xinjiang of western China and employed the satellite imagery datasets of Landsat from 1993 to 2019 to investigate the dynamics of vegetation–climate factors. Based on the land use and cover change datasets, the landscape pattern metrics, including patch density (PD), contagion index (CONTAG), fractal dimension (FRAC), were calculated to measure the landscape features on the different aspects, i.e. the fragmentation, the connectivity, and the complexity. With the algorithm of land surface energy balance, the land surface indicators, including the soil-adjusted vegetation index (SAVI), albedo, surface irradiance temperature (Ts), and evapotranspiration (ET), were calculated to represent the key process in the interaction of vegetation–climate. The temporal-spatial dynamics of the landscape patterns and the vegetation–climate metrics were mapped and demonstrated in a quantitative manner. The statistical results revealed that during the past decades, the agricultural land in the study area had significantly increased by 17 %. Grassland and shrubs had also expanded, while the desert area decreased from 82.57 % to 78.82 % of the total area, with an overall reduction rate of 1.4 %/10a. It was found the study area was getting warmer and dryer based on the general trends of Ts and ET observed during the period of 1993–2019. The agricultural land had the highest PD and CONTAG, and the lowest FRAC. The agricultural land had the lowest Ts and the highest ET. The results of structural equation model identified the decoupling effects of PD and CONTAG on the regional hydro-heat environment, while confirmed that FRAC had positive impact on the coupling between Ts and ET. Our study bridged the landscape pattern with the regional vegetation–climate interaction and provided the suggestions for the landscape planning and management for a more sustainable arid region.

Human intervention caused massive destruction of the second largest mangrove forest, Chakaria Sundarbans, Bangladesh.

Mangroves provide essential ecosystem services including coastal protection by acting as coastal greenbelts; however, human-driven anthropogenic activities altered their existence and ecosystem functions worldwide. In this study, the successive degradation of the second largest mangrove forest, Chakaria Sundarbans situated at the northern Bay of Bengal part of Bangladesh was assessed using remote sensing approaches. A total of five multi-temporal Landsat satellite imageries were collected and used to observe the land use land cover (LULC) changes over the time periods for the years 1972, 1990, 2000, 2010, and 2020. Further, the supervised classification technique with the help of support vector machine (SVM) algorithm in ArcGIS 10.8 was used to process images. Our results revealed a drastic change of Chakaria Sundarbans mangrove forest, that the images of 1972 were comprised of mudflat, waterbody, and mangroves, while the images of 1990, 2000, 2010, and 2020 were classified as waterbody, mangrove, saltpan, and shrimp farm. Most importantly, mangrove forest was the largest covering areaa total of 64.2% in 1972, but gradually decreased to 12.7%, 6.4%, 1.9%, and 4.6% for the years 1990, 2000, 2010, and 2020, respectively. Interestingly, the rate of mangrove forest areadegradation was similar to the net increase of saltpan and shrimp farms. The kappa coefficients of classified images were 0.83, 0.87, 0.80, 0.87, and 0.91 with the overall accuracy of 88.9%, 90%, 85%, 90%, and 93.3% for the years 1972, 1990, 2000, 2010, and 2020, respectively. By analyzing normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and transformed difference vegetation index (TDVI), our results validated that green vegetated area was decreased alarmingly with time in this study area. This destruction was mainly related to active human-driven anthropogenic activities, particularly creating embankments for fish farms or salt productions, and cutting for collection of wood as well. Together all, our results provide clear evidence of active anthropogenic stress on coastal ecosystem health by altering mangrove forest to saltpan and shrimp farm saying goodbye to the second largest mangrove forest in one of the coastal areas of the Bay of Bengal, Bangladesh.

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

IntroductionOld-growth forests (OGFs) are vital for global biodiversity, acting as irreplaceable carbon sinks, fostering ecosystem stability, and preserving unique ecological and cultural heritage. There are three old-growth forests in the Republic of Srpska/Bosnia and Herzegovina (Lom, Janj, and Perućica). They are dominated by mixed forests of beech, fir, and spruce (Piceo-Abieti-Fagetim illyricum). The satellite imagery results confirm that the vegetational indices are crucial for monitoring old-growth forests. Vegetation indices provide significant information for monitoring forest ecosystems. Based on Copernicus Sentinel-2 images, remote sensing is enabled, providing data in various spatial and spectral resolutions. The analysis of images related to old-growth forests allows for monitoring the spatial dynamics of changes in vitality and the health status of forests. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests.MethodsThe research methodology includes the application of Sentinel 2A satellite imagery, which is taken from the European Space Agency (ESA) database. The Sentinel 2 mission consists of two identical satellites (Sentinel 2A and Sentinel 2B) that have multispectral sensors with 13 spectral channels (bands) at spatial resolutions of 10, 20, and 60 m. The analysis included the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and visible atmospheric resistance index (VARI). For determining the significance of differences in vegetation index magnitudes between old-growth forests, a one-way analysis of variance with equal repetitions was employed.Results and discussionThe parts of old-growth forests are located in different stages of development, and the trees in them are characterized by different degrees of vitality and have different values of vegetation indices. It is assumed that parts of old-growth forests in the terminal phase of development with a large number of diseased and rotting trees have the smallest sizes of vegetation indices. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests. Using satellite imagery and their analysis of the obtained sizes of vegetation indices, it is observed that there is a significant difference in their sizes between old-growth forests. The Janj old-growth forest stands out as the area with the highest NDVI values, followed by the Lom old-growth forest, while the Perućica old-growth forest has the lowest NDVI values. However, the Perućica old-growth forest has significantly higher values of EVI and SAVI vegetation indices, while the other two old-growth forests have approximately similar values. The VARI index values are highest in the Lom old-growth forest, slightly lower in the Perućica old-growth forest, and lowest in the Janj old-growth forest. The results of these studies indicate statistically significant differences in vegetation index magnitudes between old-growth forests. The application of Copernicus satellite data enables the monitoring of changes in vegetation index values, which is of great assistance in spatially defining the developmental stages in old-growth forests (OGFs). Considering the relatively small number of sample areas covered by these studies, similar research needs to be conducted on a larger number of sample areas (replications), especially during the vegetation period.

Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data

The accurate estimation of above ground biomass (AGB) is valuable in grasping the status of potato growth and assessing yield. Spectral analysis techniques play an important role in AGB estimation by virtue of its non-destructive and rapid advantages. However, the models constructed based on spectral features at the entire growth stages due to differences in phenological stages, such as vegetation indices (VIs) and locations at fixed wavelengths, can be not applicable to single growth stages. Therefore, the main objective of this study is to improve the applicability of the AGB estimation model to mitigate the impacts of phenological stages by mining depth features from hyperspectral data. The canopy hyperspectral and AGB data of potato tuber formation, tuber growth and starch accumulation stages were measured in 2017–2019. The acquired canopy hyperspectral reflectance was smoothed by Savitzky-Golay (SG) filter, and the green edge (GE), the red edge (RE), red edge chlorophyll index (CIred-edge), optimized soil adjusted vegetation index (OSAVI), green normalized difference vegetation index (GNDVI), and normalized difference vegetation index (NDVI) were extracted. The spectral changes were analyzed at different growth stages of each year. To obtain features in response to AGB changes, a new approach is proposed to analyze the hidden depth features between sensitive wavelengths by combining a successive projections algorithm (SPA) and a long-short-term memory network (LSTM). The AGB estimation models were constructed by partial least squares regression (PLSR) based on traditional VIs + GE + RE, full spectrum, sensitive wavelengths obtained by SPA, and depth features obtained by SPA-LSTM. The results showed that GE, RE and VIs alone explained only 24 %-36 % of the variation in AGB. The accuracy of estimating AGB at sensitive wavelengths obtained after SPA was higher (R2 = 0.70, RMSE = 406 kg/hm2, and NRMSE = 27.14 %) than that of conventional VIs + GE + RE (R2 = 0.51, RMSE = 536 kg/hm2, and NRMSE = 35.81 %) and full spectrum (R2 = 0.63, RMSE = 452 kg/hm2, and NRMSE = 30.19 %) in the three-year dataset. The depth features extracted by the SPA-LSTM method proposed in this study achieved the best AGB estimation (R2 = 0.82, RMSE = 318 kg/hm2, and NRMSE = 21.25 %). The applicability of the model was validated in different years and growth stages. The R2 of each year was in the range of 0.48–0.77, the RMSE was in the range of 290–367 kg/hm2, and the NRMSE was in the range of 16.69 %-25.81 %. The R2 of each growth stage within three year was in the range of 0.48–0.78, the RMSE was in the range of 184–386 kg/hm2, and the NRMSE was in the range of 14.91 %-29.45 %. The method proposed in this study mitigates the impacts of phenological stage and improves the robustness and accuracy of the AGB estimation model, which provides remote sensing technical support for realizing potato growth monitoring and yield assessment in the field.

Aboveground Biomass Mapping and Analysis of Spatial Drivers in the Qinghai-Xizang Plateau Permafrost Zone: A Case Study of the Beilu River Basin.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.

Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.