Normalized Difference Moisture Index Research Articles

A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements.

Prediction of Potential Urban Hot Spots in Urban Environments Using Convolutional Neural Networks and Remote Sensing Techniques

Abstract. With the increase in built-up areas and rising urban populations, Land Surface Temperature (LST) has significantly increased, leading to the proliferation of Urban Hot Spots (UHS) in urban environments. To mitigate UHS proactively, researchers have conducted studies using various models to predict LST. However, current predictions are primarily based on data samples from isolated stations, making them unfeasible for continuous LST prediction on larger scales, such as regional levels. Therefore, this research aims to use Singapore as a case study to predict UHS on a regional scale using machine learning based on essential variables. Specifically, this research proposes training a Convolutional Neural Network (CNN) model using identified independent variables, including elevation, Normalized Difference Built-up Index (NDBI), Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), population, and Land Use and Land Cover (LULC), along with the target variable, UHS. After training, the model achieves high test accuracy and is fed projected data, subsequently producing projected UHS locations. The findings indicate that new UHS primarily appear in southwestern areas, Marina Bay, and the northwest regions of the country. This research predicts that 2.24 percent of the site could be classified as UHS by 2025, compared to the current percentage of 0.95 percent. Based on these projections, the research proposes preventative measures to proactively mitigate UHS. This research fills the gap by constructing a prediction model that can predict UHS locations on a regional scale.

Archaeological cognition of the Eastern mausoleum of Qin state using integrated space-ground observation tools

The Eastern Mausoleum of Qin State is a significant component of the Qin Dynasty's royal tombs, reflecting the social development level during the Warring States period (475 BC ~ 221 BC) in China. At the onset of our investigation, we mapped the site's boundaries utilizing Corona satellite imagery and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to identify archaeological features. Additionally, this study is the first to propose the use of thermal infrared band data from the SDGSAT-1 satellite to explore thermal archaeological traces, demonstrating their viability for archaeological site analysis. Subsequently, electromagnetic (EM) prospection was utilized to validate the presence of an ancient burial chamber passage. Landscape monitoring and analysis of the No.1 Mausoleum were performed using Corona and Google Earth images, revealing the accuracy of the Geomancy Theory of Chinese mausoleums through dynamic remote sensing of surface changes. Furthermore, our employment of space-to-ground observational modalities and resultant Digital Elevation Models (DEM) have been used to provided new insights into the application of Remote Sensing (RS) and Geomancy in archaeology, thereby emphasizing the pivotal role of site selection in heritage preservation. This research underscores the promise of synergistic space-ground observations in both exploiting the archaeological riches of cultural heritage sites and ensuring the enduring conservation of these irreplaceable patrimonial assets.

Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations

While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m3/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAIALS). We found that ALS accurately estimated LAI across 121 plots (R2 = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAIALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R2 of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R2 = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R2 = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.

Weighted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi-Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.

Agricultural drought monitoring in maligavila gn division based on normalized difference moisture index (NDMI) and vegetation health index (VHI)

Agricultural drought monitoring in maligavila gn division based on normalized difference moisture index (NDMI) and vegetation health index (VHI)

Exploring environmental factors that influence the distribution of poplar trees

AbstractPoplar is an invasive alien plant (IAP) that encroaches on fragile landscapes in South Africa, but the environmental factors that contribute to its spread are not well understood. To address this gap, we conducted a study using generalised linear models (GLM) to identify the most significant local environmental factors that drive poplar establishment and dispersion in Golden Gate Highlands National Park (GGHNP). We then ranked these variables according to their contribution to poplar occurrence using a random forests regression approach. Our GLM analysis revealed that soil properties such as soil organic carbon, sand and silt, topographical variables such as aspect and slope, normalised difference moisture index (NDMI) and poplar distance from rivers are significant factors affecting poplar survival. After ranking the variables, we found that the three most important factors influencing poplar occurrence were aspect, slope and poplar distance to rivers, followed by the sand, silt, NDMI, and soil organic carbon. Our results are consistent with previous studies, indicating that topographical variables and the availability of sufficient water supply are the primary drivers of the spread of IAPs. This study provides useful insights in managing and monitoring poplars in delicate landscapes.

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.

Grass curing-driven fire danger index in a protected mountainous grassland using fused MODIS and Sentinel-2

ABSTRACT This study assessed the spatio-temporal variation of the degree of curing (DoC) in the Golden Gate Highlands National Park (GGHNP) from 2016 to 2020. The estimation of the degree of grass curing was carried out with the high spatial resolution fused remotely sensed data of MODIS and Sentinel-2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using soil and vegetation moisture content indices-based grassland curing algorithm. Grassland curing indices were computed on Google Earth Engine and converted into Grassland Curing Maps (GCMs), which were then synthesized into fire danger maps. The study showed that all selected indices have the highest DoC in the month of September, with the minor time variation observed in MODIS and Sentinel 2-derived GCI estimated using Shortwave Infrared Water Stress (GCI_SIWSI) in the months of August and March, respectively. The analysis further revealed that GCI_SIWSI (44%) derived from Sentinel-2 data yielded the highest area of extremely high fire danger, followed by GCI estimated using Normalized Difference Moisture Index (GCI_NDMI) (25%) derived from fused data, MODIS-derived GCI_SIWSI and fused-derived GCI estimated using Global Vegetation Monitoring Index (GCI_GVMI) (both 16%). The GCI_SIWSI derived from Sentinel-2 data also showed that more than 95% of the entire landmass of the study area was within a high to extremely high fire danger zone, making the park’s vegetation susceptible to fire. Among all the four selected indices correlated against fire point, only SIWSI revealed a positive relationship across MODIS, Sentinel-2 and Fused data. It is also evident from the study that GCMs derived from Fused data outperformed MODIS and Sentinel-2 with the highest R2 and F values of 0.65 and 380, respectively. These results indicate that GCI derived from fused remotely sensed data is a promising GCI for accurately assessing the DoC over mountainous grassland environments. The study paved the way for increasing the spatial resolution for estimating the DoC for fire and fuel management of parks and other fire agencies within mountainous grassland environments.

Spatiotemporal characterization of the subsidence and change detection in Tehran plain (Iran) using InSAR observations and Landsat 8 satellite imagery

Urban areas worldwide are increasingly facing challenges related to land subsidence, a phenomenon exacerbated by uncontrolled groundwater extraction and urban expansion. This research focuses on the Tehran plain, Iran's capital city, where significant subsidence has been observed due to uncontrolled migrations influenced by various economic and political factors. This expansion has increased demand for energy, notably water, leading to irregular water withdrawals from underground sources and, consequently, land subsidence. Monitoring this subsidence, particularly its effects on urban infrastructure, has become a critical challenge. This research first reviewed the existing body of knowledge related to subsidence measurement in the Tehran plain with an emphasis on their findings and limitations and then used radar images to study the subsidence patterns in the Tehran plain from 2016 to the end of 2020. Finally, the results collaborated by optical imagery analysis to find the relationship between surface change detection and spatiotemporal distribution of subsidence. As a result, through processing Sentinel-1A SAR images, consistent vertical displacements (subsidence) were observed, especially in areas heavily reliant on groundwater from wells, with some areas experiencing a rate of more than −20 mm/year. Horizontal displacement, however, was approximately about ±8 mm/year. Also, our results show that the subsidence rate in this plain has decreased in recent years. Therefore, the study integrated multispectral satellite data to clarify this issue and compensate for missing groundwater level data, specifically the Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Moisture Index (NDMI). These datasets were used to monitor changes in vegetation cover distribution and moisture in response to the variations of groundwater depth over time. The results of this research can be beneficial in adequately managing groundwater resource utilization to reduce the potential damage to infrastructure and the environment.

Flood hazard mapping using GIS‐based AHP approach for Krishna River basin

AbstractFlood hazard mapping is an essential tool for determining the risk and susceptibility of flood‐prone locations. This constitutes various criteria and factors that require a methodical and comprehensive decision‐making framework. The analytic hierarchy process (AHP), the popular multi‐criterion decision‐making (MCDM) technique, which deals with the complicated problems, including qualitative and quantitative factor, is utilized in this study for developing flood hazard maps in integration with geographic information system (GIS). Flood hazard maps are developed for the Krishna River basin using AHP‐GIS. Digital elevation model, rainfall data, soil data and Landsat images are used to extract the various hazard indicators. Nine hazard indicators are employed in this study to prepare the thematic layers, which includes Topographical Wetness Index, elevation, slope rainfall, LULC, Normalized Difference Wetness Index, distance from river, soil type and drainage density. The thematic layers are combined in Arc GIS using weighted overlay method to prepare the flood hazard zonation maps. The hazard maps are classified in three categories as low, moderate and high hazard zones. For the Krishna River basin, 30 percent area was found in high hazard. The outcomes derived from the AHP‐GIS method are validated by comparing them with situational reports pertinent to the area, showcasing a robust agreement with the available dataset.

Improving Tree Cover Estimation for Sparse Trees Mixed with Herbaceous Vegetation in Drylands Using Texture Features of High-Resolution Imagery

Tree cover is a crucial vegetation structural parameter for simulating ecological, hydrological, and soil erosion processes on the Chinese Loess Plateau, especially after the implementation of the Grain for Green project in 1999. However, current tree cover products performed poorly across most of the Loess Plateau, which is characterized by grasslands with sparse trees. In this study, we first acquired high-accuracy samples of 0.5 m tree canopy and 30 m tree cover using a combination of unmanned aerial vehicle imagery and WorldView-2 (WV-2) imagery. The spectral and textural features derived from Landsat 8 and WV-2 were then used to estimate tree cover with a random forest model. Finally, the tree cover estimated using WV-2, Landsat 8, and their combination were compared, and the optimal tree cover estimates were also compared with current products and tree cover derived from canopy classification. The results show that (1) the normalized difference moisture index using Landsat 8 shortwave infrared and the standard deviation of correlation metric calculated by means of gray-level co-occurrence matrix using the WV-2 near-infrared band are the optimal spectral feature and textural feature for estimating tree cover, respectively. (2) The accuracy of tree cover estimated using only WV-2 is highest (RMSE = 7.44%), indicating that high-resolution textural features are more sensitive to tree cover than the Landsat spectral features (RMSE = 11.53%) on grasslands with sparse trees. (3) Textural features with a resolution higher than 8 m perform better than the combination of Landsat 8 and textural features, and the optimal resolution is 2 m (RMSE = 7.21%) for estimating tree cover, whereas the opposite is observed when the resolution of textural features is lower than 8 m. (4) The current global product seriously underestimates tree cover on the Loess Plateau, and the tree cover calculation using the canopy classification of high-resolution imagery performs worse than the method of directly using remote sensing features.

Mapping long-term Transformation of Wetlands and Annual Rainfall Variability in Madhubani District (1975-2022).

In addition to supporting a rich biodiversity, wetlands serve as ecosystems that connect aquatic and terrestrial settings. They also play a critical ecological role by providing essential supplies. According to the National Wetland Atlas (2010), Bihar has a total of 21,988 wetlands with a total area of 4,03,209 hectares. These wetlands exhibit great range of floral and faunal diversity with large number of endemic species. The present paper endeavors to map the natural wetlands of Madhubani district of North Bihar and assess their evolving hydro-physiographic characteristics. The study area is endowed with numerous small wetlands as it receives high average annual rainfall in Bihar. This study primarily emphasizes on identification of wetlands and their spatial-temporal assessment during the period 1975-2022 by utilizing datasets from various sources like topographical maps, satellite imageries, and other ancillary data. It uses geospatial tool like Arc Hydro and indices like Topographic Wetness Index (TWI) and Normalized Difference Wetness Index (NDWI) for identification and spatial-temporal assessment of wetlands during pre-monsoon and post-monsoon season. The study finds that despite being one of the rainiest districts of Bihar, the annual rainfall trends in Madhubani is decreasing. This has impacted upon the water quality, productivity, biodiversity and annual recharge of wetlands of Madhubani. Its entire geographic region has thus been significantly reduced as a result. Other important factors like accelerated anthropogenic intervention, land use changes and process of urbanization have contributed towards rapid transformation of floodplains of Madhubani District. These changes may affect future water potential of the region and thus threaten sustainability in the region.

Normalised difference moisture index in water stress assessment of maize crops

Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.

ASSESSMENT OF FOREST DISTURBANCES USING REMOTE SENSING: CASE OF KALIWA RIVER FOREST RESERVE (KRFR), PHILIPPINES

Abstract. The Kaliwa River Forest Reserve (KRFR) is located in the southwestern part of the Sierra Madre Mountain Range, Philippines. KRFR is one of the most important forest reserves in the country that is adjacent to urban areas and is very much affected by various forest disturbances. Forest disturbances can be caused by either anthropogenic or natural phenomena (e.g., typhoons, drought, and pathogen outbreaks) and may have negative effects on the forest structure and function. The study used the Normalized Difference Moisture Index (NDMI) in detecting forest disturbances for the period of 2010, 2015, and 2020. Results showed that the deforestation rate has significantly decreased from 6.22% in 2010 to 3.40% in 2020. The National Greening Program (NGP) and intensified forest protection activities of the Department of Environment and Natural Resources (DENR) may have contributed to the observed decreasing trend in deforestation rate. However, forest disturbance in the closed and open forest areas of KRFR gradually increased from 0.11% to 0.40%. Based on the accuracy assessment, the NDMI performed well in determining forest disturbances with mean values of 0.91 and 0.93 for completeness and correctness, respectively. The main contributor to forest disturbances in KRFR was mainly anthropogenic factors such as kaingin, illegal logging, urban sprawl, and upland farming. Outputs of this study can aid in the formulation of policies for better forest resource management, rehabilitation, and protection in the protected area.

Pixel-Based Spatio-Statistical Analysis of Landslide Probability in Humid and Seismically Active Areas of Himalaya and Hindukush

The Hindukush and Himalaya regions of Pakistan are chronically prone to several geological hazards such as landslides. Studying landslides in these regions is crucial for risk assessment and disaster management, as well as for determining the effects of adverse climatic conditions, infrastructure management, and increasing anthropogenic activities. High-relief mountains in these regions face severe challenges because of frequently occurring landslides and other natural hazards, especially during intensive rainfall seasons and seismic activity, which destroy infrastructure and cause injuries and deaths. Landslides in the Alpuri Valley (Hindukush) and the Neelum Valley (Himalaya) have been activated through high magnitude earthquakes, intensive rainfalls, snowfall, floods, and man-made activities. Landslide susceptibility mapping in these areas is essential for sustainable development as it enables proactive risk management, up-to-date decision-making, and effective responses to landslide hazards, ultimately safeguarding human lives, property, and the environment. In this study, the relative effect method was applied for landslide susceptibility modeling in both study areas to determine the capability to reduce the effects of landslides, and to improve the prediction accuracy of the method. The relative effect is a statistical model that has only been used for very limited time for landslide susceptibility with effective results. A total of 368 (Neelum Valley) and 89 (Alpuri Valley) landslide locations were identified, which were utilized to prepare the reliable landslide inventory using GIS. In order to evaluate the areas at risk for future landslides activities and determine their spatial relationship with landslide occurrences, the landslide inventory was developed with 17 landslide causative factors. These factors include slope gradient, slope aspect, geology, plan curvature, general curvature, profile curvature, elevation, stream power index, drainage density, terrain roughness index, distance from the roads, distance from the streams, distance from fault lines, normalized difference wetness index, land-use/land-cover, rainfall, and normalized difference vegetation index. Finally, the performance of the relative effect method was validated using the success and prediction curve rate. The AUC-validated result of the success rate curve in the Alpuri Valley is 74.75%, and 82.15% in the Neelum Valley, whereas, the AUC-validated result of the prediction rate curve of the model is 87.87% in the Alpuri Valley and 82.73% in the Neelum Valley. These results indicate the reliability of the model to produce a landslide susceptibility map, and apply it to other landslide areas. The model demonstrated a more effective result in the Alpuri Valley, having a smaller area. However, the results are also desirable and favorable in Neelum Valley, with it being a large area. It will assist in general landslide hazard management and mitigation, and further research studies related to future landslide susceptibility assessments in other parts of the region.

Using multisource remotely sensed data and cloud computing approaches to map non-native species in the semi-arid savannah rangelands of Mpumalanga, South Africa

ABSTRACT Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study’s objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer’s accuracy (PA) and user’s accuracy (UA) ranging from 67% to 95%.It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.

Improving the accuracy of mangrove forest mapping using sentinel image and developed classification algorithm based on machine learning

ABSTRACT The national mangrove rehabilitation required mangrove spatial information with very high accuracy. This study successfully developed the classification algorithm measurable for mapping mangrove forest change based on machine learning. We also modified the recursive feature elimination (RFE) method to obtain the most optimal feature importance (FI). The results showed that the developed classification algorithm was built based on the five most important features including digital elevation model (DEM), near-infrared (NIR), normalized difference moisture index (NDMI), normalized difference water index (NDWI), and distances from brackish water river (DBWR). The developed algorithm increased overall and kappa accuracy by 0.48% and 0.01, respectively.

Выявление повреждений хвойных насаждений на основе комплексного анализа результатов дистанционного зондирования Земли и наземных обследований

Damage to forest stands and their death as a result of outbreaks of destructive insects and diseases occur over significant areas and represent an important forestry problem. To solve this problem, it is advisable to use materials from remote sensing of the Earth, since damaged and dead forests can occupy significant areas, from remote еach other. The article presents a methodology for assessing the state of forest stands and the impact of forest pests and diseases on them based on the joint processing of data from a regular grid of sample plots and materials from remote sensing of the Earth via geoinformational technologies, variance and regression analyses. The sample plots have been laid according to the ICPForests methodology and have characterized the general background of the state of the forests in the study area of the North-Western and central parts of the Leningrad Region, where the foci of forest pests and diseases reproduction have been identified at the same time. The applicability of 2 vegetation indices – normalized difference (NDVI) and short–wave (SWVI), as well as the normalized difference moisture index (NDMI), the normalized burn ratio (NBR) and the spectral characteristics of the corresponding channels of the Sentinel-2B image to identify the foci of forest damage by pests and diseases has been assessed. Statistically significant relationships between the state of the stands and the values of the listed indices have been established. It has been shown that the foci of pests and diseases reproduction are reliably detected using the materials from remote sensing of the Earth against the general background of the state of the forests represented by the regular grid of sample plots. A thematic map of the stands has been developed dividing them into 2 groups of state classes: healthy and weakened stands; suppressed stands and deadwood. Its accuracy was assessed via the support vector machine (SVM) on the basis of the combination of ground and remote sensing data. To assess the adequacy of the automatic classification, the error matrix and the calculation of the Cohen’s Kappa coefficient have been used. The coefficient has turned out equal to 0.878, which indicates a high quality of the classification. It has been shown that the developed thematic map is applicable for detecting potential foci of forest pests and diseases reproduction in the study area.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.