Soil Adjusted Vegetation Index Research Articles

Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices

Fire plays a critical role in nutrient cycling and species diversity but also causes severe disturbances to ecosystems. Despite remote sensing has emerged as a valuable tool to study wildfires, a lack of information regarding post-fire dynamics still remains for certain regions. This study investigated the post-fire dynamics of four burned sites in the temperate forest of Chihuahua, northern Mexico from 2009 to 2017, based on the variability of spectral indices under varied topographic conditions of aspects and slopes. The Normalized Difference Vegetation index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Burn Ratio (NBR), and Normalized Difference Water index (NDWI) were utilized to assess vegetation recovery on the burned sites. One-way ANOVAs and Tukey´s tests were performed to evaluate separately the effects of aspect and slope on the indices’ variability (p < 0.05). In general, the NDVI measured the highest values of the burned sites along the evaluated lapse while the NDWI indicated the lowest measures. Results indicated significant effects of aspect on NDVI and SAVI (p < 0.05), while slope significantly influenced the four evaluated indices (p < 0.05).That in turn showed northern aspects and steeper slopes to have the highest post-fire vegetation recoveries. Furthermore, aspect only affected the bi-annual variability of SAVI (p < 0.05). However, the NBR and NDWI showed higher magnitudes of change among periods than the SAVI (i. e., recovery and non-recovery). Besides registering a continuous recovery of the burned sites from the wildfire events to 2015, this study revealed the NDVI and SAVI as the most useful spectral indices for assessing post-fire vegetation dynamics in the region. Besides aspect and slope, future studies could focus on the inclusion of additional indicators to more effectively assess post-fire ecosystem dynamics in northern Mexico.

Artificial intelligence and remote sensing for spatiotemporal analysis and prediction of vegetation changes: a case study of El Oued, northeastern Algeria

This study investigates vegetation ecology and rangeland degradation in the arid and desert regions of northeastern Algeria from 2015 to 2023, with predictions extending to 2031. Utilizing remote sensing techniques, specifically Landsat 8 and Landsat 9 imagery, and the Modified Soil Adjusted Vegetation Index (MSAVI), we mapped and analyzed changes in vegetation cover. The QGIS MOLUSCE plugin facilitated spatiotemporal analysis, revealing significant natural and anthropogenic impacts on a study area. Less degraded rangelands have also seen an increase, from 44,110,116 hectares in 2015 to 48,481,851 hectares in 2023, due to drought, overgrazing, and socioeconomic pressures. With a slight decrease to 48,450,495 hectares projected for 2031. This suggests efforts in land rehabilitation and sustainable rangeland management are yielding positive results, though there remains room for improvement. The study recorded that Agricultural croplands have experienced a slight decrease from 1,404,153 hectares in 2015 to 1,350,693 hectares in 2023. However, a modest increase to 1,587,242 hectares is projected by 2031. The findings indicate that Oases date palm areas have increased from 1,287,342 hectares in 2015 to 1,630,197 hectares in 2023, with further growth to 1,816,296 hectares anticipated by 2031. Our research highlights the critical role of remote sensing in monitoring vegetation dynamics within arid and desert ecosystems. The methodology provides a robust framework for similar studies, emphasizing the necessity for ongoing monitoring using advanced télédétection technologies. To mitigate further degradation and preserve these fragile ecosystems, the implementation of sustainable land management practices is essential.

Exploration of the utilization of a new land degradation index in Lake Ebinur Basin in China

Land degradation significantly impacts regional economic development and food security, particularly in arid river basins where soil and water conservation is crucial. Understanding the extent and causes of land degradation is pivotal for effectively prevention and management. This study employs the soil adjusted vegetation index (SAVI), the temperature vegetation dryness index (TVDI), and the salinization detection index (SDI), combined with the analytic hierarchy process and the entropy weight method, to construct a comprehensive land degradation index (LDI). Sen’s slope trend analysis and the Mann-Kendall significance test were used to analyze land degradation trends in the Ebinur Lake watershed from 2002 to 2022. Additionally, the optimal parameters-based geographical detector was used to examine the underlying mechanisms of land degradation. The results indicate the following: (1) From 2002 to 2012, the degree of land degradation in the Ebinur Lake watershed worsened, particularly in the eastern and southeastern parts, as well as in the southern region of Toli County. From 2012 to 2022, land degradation significantly improved, with a notable reduction in degraded land area. (2) Over the period of 2002-2022, 93.08%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.08\\%$$\\end{document} of the land in the research region exhibited a declining LDI trend, 3.95%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.95\\%$$\\end{document} showed no change, and only 2.96%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.96\\%$$\\end{document} showed an increasing LDI trend. (3) Moderate, severe, and very severe degradation mainly occurred on grassland and unused land, while light degradation and non-degradation primarily occurred on forest land and cultivated land. (4) Unreasonable land use and overgrazing were identified as the primary factors influencing land degradation, with elevation being a secondary factor. The interaction between land use and other factors was found to be most significant, followed by the synergistic effects of grazing quantity with elevation, annual average temperature, gross domestic product, soil moisture, and elevation with annual average precipitation, and temperature. The results of this study offer an empirical basis and taking decisions assistance for land degradation control in the Ebinur Lake Basin, as well as examples and references for assessing land degradation in other places.

Upaya Mengurangi Resiko Bencana di Pedesaan dengan Memanfaatkan Citra Penginderaan Jauh

The natural disaster is one of the phenomena that can occur at any time, anywhere and anytime. The most common disasters are floods, landslides, tornadoes and earthquakes, which can cause damage, loss of life and even loss of life. The conditions of Tabaroge Village and its surroundings are prone to flooding geographically, geologically, hydrologically and demographically. The condition of the East Luwu Regency area is at the confluence of several earth tectonic plates. Therefore, most of the potential disaster threats are in the Tabaroge Village area and its surroundings. Based on the occurrence of natural disasters in East Luwu Regency in recent years, which occurred in several villages. Judging from the characteristics of the Tabaroge Village area, Efforts to Reduce Disaster Risk in Rural Areas by Utilising Remote Sensing Imagery aims to provide information on locations that have disaster risk. This research uses the analysis methods of Standardised Precipitation Index (SPI), Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI) and Inverse Distance Weighted (IDW) for data exploration using Arcmap (GIS). In future development, it is expected to be able to implement disaster risk reduction by utilising remote sensing imagery in Tabaroge Village, East Luwu Regency.

Analysis of drainage morphometry and spectral indices using earth observation datasets in Palar River basin, India

In this research we analyzed the morphometric parameter of Palar River basin by using the satellite data from open sources for monitoring drainage network. Palar River basin covers 1972.27 sq.km areas with elevation of 226.9 m as highest and 38.01 m lowest above mean sea level. In this research it detected that Palar River basin having 5th order stream with drainage density of 0.40 km/km2 and drainage pattern of dendritic to rectangular. We also draw aspect map, relief map and slope map for the research area by using digital elevation model (DEM) data of 30 m resolution. In this research we also employed spectral indices like normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI) for the vegetation, water and soil characteristics of the research area. In this research it found that there are major changes in land use/land cover as water bodies and land during these periods of 15 years. Highly positive correlation show between morphometric parameter and spectral indices. In this research groundwater level data for the year 2005 and 2020 are used to validation the study. This research work is very useful in developing solutions for dealing with different types of drought and management of groundwater extraction plans.

Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

The present work aimed at the spatiotemporal analysis of Land Surface Temperature (LST) and several land-use land-cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI), and Modified Normalized Difference Water Index (MNDWI) and to develop a Convolutional Neural Network (CNN) model to predict the LST and the indices for Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. The predictions were made only for LST and two indices (NDVI and NDBI) because of their strong correlation with LST. Landsat-8 satellite imageries acquired for 2013, 2015, 2017, 2019, and 2021 were utilized, and the Sialkot district, a swiftly urbanizing and advancing industrial city, was selected as a case study area. In the study, first, the indices (NDVI and NDBI) were predicted for 2019, 2021, and 2023 following a sequential procedure, and then based on the predicted indices, the LST for the mentioned years was predicted. Ultimately, to analyze the impact of impervious area on SUHI, impervious and SUHI were extracted for 2019, 2021, and 2023 using the predicted NDBI and LST, respectively, by employing Otsu's Thresholding technique. The predicted LST and indices for 2019 and 2021 were compared with the obtained 2019 and 2021 LST and indices through several statistical measures, such as the kappa index, to evaluate the performance of the CNN model. The kappa index of different kappa statistics for 2019 and 2021 varied between 0.81 and 0.96, which resembles several previous studies, thus indicating sufficient future predictions. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The results showed that the distribution of the SUHI was highly related to the impervious area. Suppose the urbanization rate in the study area continues at its current pace. In that case, this expansion will cause a dramatic increase in SUHI distribution unless decision-makers consider any proper urban planning model for the study area.

The Use of Remote Sensing Techniques in the Analysis of the Influence of Forest Ecosystems over the Precipitation

The processing of remote sensing images and their integration into a Geographic Information System (GIS) to analyse and manage an area represents a modern approach that is increasingly used. In the present paper, a predominantly moun-tainous area was studied and analysed, located in Hunedoara County – Romania, near the city of Hateg and the Retezat Mountains. A satellite scene from 09.24.2019 from the RapidEye remote sensing system was retrieved, processed and subjected to complex remote sensing analyses. These remote sensing data were analysed and processed, and based on them a series of specific indices were calculated and interpreted, namely, for the characterisation of the vegetation: NDVI (Normalised Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normal-ised Difference Red Edge Index), SAVI (Soil Adjusted Vegetation Index), MSAVI (Modified Soil Adjusted Vegetation Index), CI Green (Chlorophyll Index Green), CI Red Edge (Red Edge Chlorophyll Index), RTVI core (Red Edge Tri-angular Vegetation Index), SR (Simple Ratio), Red Edge SR (Red Edge Simple Ratio), LAI (Leaf Area Index).

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm

Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model for flood susceptibility mapping (FSM) is challenging. Therefore, in this study, various artificial intelligence approaches have been utilized to achieve optimal accuracy in flood susceptibility modeling to address this challenge. By incorporating the grey wolf optimizer (GWO) metaheuristic algorithm into various models—including recurrent neural networks (RNNs), support vector regression (SVR), and extreme gradient boosting (XGBoost)—the objective of this modeling is to generate flood susceptibility maps and evaluate the variation in model performance. The tropical Manimala River Basin in India, severely battered by flooding in the past, has been selected as the test site. This modeling utilized 15 conditioning factors such as aspect, enhanced built-up and bareness index (EBBI), slope, elevation, geomorphology, normalized difference water index (NDWI), plan curvature, profile curvature, soil adjusted vegetation index (SAVI), stream density, soil texture, stream power index (SPI), terrain ruggedness index (TRI), land use/land cover (LULC) and topographic wetness index (TWI). Thus, six susceptibility maps are produced by applying the RNN, SVR, XGBoost, RNN-GWO, SVR-GWO, and XGBoost-GWO models. All six models exhibited outstanding (AUC above 0.90) performance, and the performance ranks in the following order: RNN-GWO (AUC: 0.968) &gt; XGBoost-GWO (AUC: 0.961) &gt; SVR-GWO (AUC: 0.960) &gt; RNN (AUC: 0.956) &gt; XGBoost (AUC: 0.953) &gt; SVR (AUC: 0.948). It was discovered that the hybrid GWO optimization algorithm improved the performance of three models. The RNN-GWO-based flood susceptibility map shows that 8.05% of the MRB is very susceptible to floods. The modeling found that the SPI, geomorphology, LULC, stream density, and TWI are the top five influential conditioning factors.

Characterisation of Two Vineyards in Mexico Based on Sentinel-2 and Meteorological Data

In Mexico, viticulture represents the second source of employment in the agricultural area after the fruit and vegetable sector. In developed countries, remote sensing is widely used for vineyard monitoring; however, this tool is barely used in the developing countries of Iberoamerica. In this research, our overall objective is to characterise two vineyards in the state of Queretaro (Mexico) using Sentinel-2 and meteorological data, specifically spectral and thermal indices. Results show that spectral indices obtained from Sentinel-2 bands have adequately characterised the phenological dynamics of the different varieties of the vineyards. The Modified Soil-Adjusted Vegetation Index (MSAVI) was adequately used to discriminate between the first stages of vineyards, while the Normalized Difference Vegetation Index (NDVI) was useful for monitoring vineyards during the rest stages of vineyards. Thermal indices have shown that the best grape varieties are those that can adapt to both cooler and warmer temperatures, have a reasonable ripening period, and can produce wines with balanced acidity and flavours. In conclusion, the combination of meteorological (including thermal indices) and remote sensing data (NDVI and MSAVI) provide information for choosing a suitable grape variety for this region.

On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks.

The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.

Drone-based early detection of bark beetle infested spruce trees differs in endemic and epidemic populations

IntroductionEuropean forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is the European Spruce Bark Beetle (Ips typographus L.). Effective management of I. typographus outbreaks necessitates the timely detection of recently attacked spruce trees, which is challenging given the difficulty in spotting symptoms on infested tree crowns. Bark beetle population density is one of many factors that can affect infestation rate and symptoms development. This study compares the appearance of early symptoms in endemic and epidemic bark beetle populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery.MethodsIn spring of 2022, host colonization by bark beetles was induced on groups of spruce trees growing in 10 sites in the Southern Alps, characterized by different population density (5 epidemic and 5 endemic). A multispectral sensor mounted on a drone captured images once every 2 weeks, from May to August 2022. The analyses of a set of vegetational indices allowed the actual infested trees’ reflectance features and symptoms appearance to be observed at each site, comparing them with those of unattacked trees.ResultsResults show that high bark beetles population density triggers a more rapid and intense response regarding the emergence of symptoms. Infested trees were detected at least 1 month before symptoms became evident to the human eye (red phase) in epidemic sites, while this was not possible in endemic sites. Key performing vegetation indices included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Vegetation Index, with a correction factor of 0.44), and NDRE (Normalized Difference Red Edge index).DiscussionThis early-detection approach could allow automatic diagnosis of bark beetles’ infestations and provide useful guidance for the management of areas suffering pest outbreaks.

Longitudinal study of changes in greenness exposure, physical activity and sedentary behavior in the ORISCAV-LUX cohort study

BackgroundGreenness exposure has been associated with many health benefits, for example through the pathway of providing opportunities for physical activity (PA). Beside the limited body of longitudinal research, most studies overlook to what extent different types of greenness exposures may be associated with varying levels of PA and sedentary behavior (SB). In this study, we investigated associations of greenness characterized by density, diversity and vegetation type with self-reported PA and SB over a 9-year period, using data from the ORISCAV-LUX study (2007–2017, n = 628).MethodsThe International Physical Activity Questionnaire (IPAQ) short form was used to collect PA and SB outcomes. PA was expressed as MET-minutes/week and log-transformed, and SB was expressed as sitting time in minutes/day.Geographic Information Systems (ArcGIS Pro, ArcMap) were used to collect the following exposure variables: Tree Cover Density (TCD), Soil-adjusted Vegetation Index (SAVI), and Green Land Use Mix (GLUM). The exposure variables were derived from publicly available sources using remote sensing and cartographic resources. Greenness exposure was calculated within 1000m street network buffers around participants’ exact residential address.ResultsUsing Random Effects Within-Between (REWB) models, we found evidence of negative within-individual associations of TCD with PA (β = − 2.60, 95% CI − 4.75; − 0.44), and negative between-individual associations of GLUM and PA (β = − 2.02, 95% CI − 3.73; − 0.32). There was no evidence for significant associations between greenness exposure and SB. Significant interaction effects by sex were present for the associations between TCD and both PA and SB. Neighborhood socioeconomic status (NSES) did not modify the effect of greenness exposure on PA and SB in the 1000 m buffer.DiscussionOur results showed that the relationship between greenness exposure and PA depended on the type of greenness measure used, which stresses the need for the use of more diverse and complementary greenness measures in future research. Tree vegetation and greenness diversity, and changes therein, appeared to relate to PA, with distinct effects among men and women. Replication studies are needed to confirm the relevance of using different greenness measures to understand its’ different associations with PA and SB.

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.