Normalized Difference Moisture Index Research Articles

Remotely Sensed Spectral Indices as Proxies of the Structure of Urban Bird Communities

Abundant and diverse urban bird communities promote ecosystem and human health in cities. However, the estimation of bird community structure requires large amounts of resources. On the other hand, calculating remotely sensed spectral indices is cheap and easy. Such indices are directly related to vegetation cover, built-up cover, and temperature, factors that also affect the presence and abundance of bird species in urban areas. Therefore, spectral indices can be used as proxies of the structure of urban bird communities. We estimated the abundance, taxonomic, functional, and phylogenetic diversity of the bird community at each of 18 50 m radius survey stations in the urban core area of Kavala, Greece. We also calculated eight spectral indices (means and standard deviations, SDs) around survey stations at 50 m, 200 m, and 500 m spatial scales. The land surface temperature SD (LST) was the most important proxy, positively related to bird abundance at the 50 m and 200 m spatial scales. At the same time, the mean green normalized difference vegetation index (GNDVI) was the most important proxy, negatively related to abundance at the 500 m spatial scale. Means and SDs of vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI2), soil-adjusted vegetation index (SAVI), and atmospherically resistant vegetation index (ARVI), were the most important proxies, positively related to taxonomic and functional diversity at all the spatial scales. The mean and SDs of LST, normalized difference moisture index (NDMI), and normalized difference built-up index (NDBI) variously affected taxonomic and functional diversity. The mean and SDs of LST were the best proxies of phylogenetic diversity at the 50 m and 500 m spatial scales, while the SDs of NDBI and NDMI were the best proxies at the 200 m spatial scale. The results suggest that several spectral indices can be used as reliable proxies of various facets of urban bird diversity. Using such proxies is an easy and efficient way of informing successful urban planning and management.

Effects of land consolidation on moisture and surface temperature regime of agricultural soil blocks using remote sensing data in the Czech Republic

Abstract The Czech Republic has experienced collectivization, transforming individual land ownership into state ownership through agricultural land consolidation after World War II. Land consolidation has led to the expansion of agricultural soil blocks as well as the disappearance of vital landscape features such as shrubs, hedges, lone trees, and wetlands, all of which serve a variety of ecological functions. The disappearance of the landscape elements causes some important problems, such as soil degradation and erosion. The Czech Ministry of Agriculture has enforced a regulation that was developed by the European Union to improve agriculture by enforcing cross-compliance requirements, which encompass the observance of Good Agricultural and Environmental Condition (GAEC) standards. The regulation disincentivizes single-crop cultivation on areas larger than 30 hectares since 2020 to reduce the negative effects of land consolidations on agriculture and soil quality. In this context, the study examines the relationship between land surface temperature (LST) and moisture of agricultural plants and soil blocks in the South Bohemia, Czech Republic, using Landsat-8 remote sensing data from 2018 to 2021. The mono-window algorithm (MWA) was used to obtain LST, and the precision of Landsat LST data was evaluated using MODIS daily LST data. The NRMSE ≤ 0.15 and RMSE ≤ 3.5 C were found with a strong positive relationship r ≥ 0.65 between datasets. Additionally, the density of vegetation and the moisture index of soil and plants were calculated using spectral remote sensing indices such as the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and soil moisture index (SMI). The statistical relationships among those spectral indices with LST were examined. The analyzes showed that the implementation of GAEC 7d improved the relationship between NDVI and NDMI, with the correlation increasing from r = 0.80 in 2019 to 0.92 in 2021. Meanwhile, the inverse relationship between NDMI and LST changed from − 0.71 to -0.67. These changes in correlation suggest that plant water retention in the region increased following the regulation, and the soil structure may have also improved.

A new index for extracting built-up land features using Landsat satellite imagery

Land features mapping in urban regions are changing rapidly over time as a result of continuous urbanization. Spatial remote sensing allows continuous monitoring of this change using multi-date image data and providing a synoptic view. In light of this, a new spectral index based on pre-existing indices is suggested for the purpose of extracting built-up features in the Beni Mellal study area utilizing two Landsat-5 TM and Landsat-8 OLI images from 2013 and 2023. This time range of ten years enables the spatial and temporal monitoring of built-up surfaces. The accuracy of the results obtained is confirmed by using Pearson's correlation to land surface temperature and the two spectral indices, the Normalized Difference Moisture Index (NDMI) and Normalized Difference Vegetation Index (NDVI).

Monitoring wetland cover changes and land surface temperatures using remote sensing and GIS in Göksu Delta.

Wetlands provide necessary ecosystem services, such as climate regulation and contribution to biodiversity at global and local scales, and they face spatial changes due to natural and anthropogenic factors. The degradation of the characteristic structure signals potential severe threats to biodiversity. This study aimed to monitor the long-term spatial changes of the Göksu Delta, a critical Ramsar site, using remote sensing techniques. It seeks to analyze the relationship between these changes and land surface temperature (LST) and predict future land use patterns through machine learning (ML) methods. In this context, the normalized difference vegetation index, modified normalized difference water index, normalized difference bareness index, and normalized difference moisture index remote sensing spectral index analyses and LST maps were generated using Landsat 8 Operational Land Imager (OLI) satellite imagery for 1985, 2000, 2015, and 2023. Kappa accuracy assessments demonstrated a high level of agreement between the generated maps and ground truth data. Pearson correlation analysis was used to assess the consistency of the relationship between spectral index analyses and LST, revealing a statistically significant correlation at the 0.01 level. The study revealed that Lake Akgöl lost 58.85% of its water body over the 38 years of monitoring the delta. This loss was primarily attributed to increased LST and human activities. The land use land cover model for the year 2031, developed using artificial neural networks and cellular automata from ML methods, projected a 7.50% decrease in total water bodies, a 46.94% reduction in vegetated areas, and a 36.85% increase in nonvegetated areas. In conclusion, it was emphasized that the adverse land use trends within the Göksu Delta are expected to persist, degrading its ecosystem services and values. In this context, the study's findings can be utilized to identify strong strategies for protecting the delta.

Monitoring Environmental Degradation and Spatial Changes in Vegetation and Water Resources in the Brazilian Pantanal

Diagnosing climate variability and environmental change in floodable regions is essential for understanding and mitigating impacts on natural ecosystems. Our objective was to characterize environmental degradation in the Brazilian Pantanal by identifying changes in vegetation and water cover over a 30-year period using remote sensing techniques. We evaluated surface physical–hydric parameters, including Land Use and Land Cover (LULC) maps, Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Moisture Index (NDMI), and precipitation data. There was a decrease in the area of water bodies (−9.9%), wetlands (−5.7%), and forest formation (−3.0%), accompanied by an increase in the area of pastureland (7.4%). The NDVI showed significant changes in vegetation cover (−069 to 0.81), while the MNDWI showed a decrease in water surface areas (−0.73 to 0.93) and the NDMI showed a continuous decrease in vegetation moisture (−0.53 to 1). Precipitation also decreased over the years, reaching a minimum of 595 mm. Vegetation indices and land use maps revealed significant changes in vegetation and loss of water bodies in the Pantanal, reinforcing the need for sustainable management, recovery of degraded areas, and promotion of ecotourism to balance environmental conservation and local development.

A new landslide detection approach using the combination of the normalized difference moisture index and slope classification analysis

ABSTRACT Landslides are the most common natural disasters that are often found in tropical and mountainous areas. This study aimed to identify the potency of the combination of the normalized difference moisture index (NDMI) and slope analysis in the Cameron Highlands region of Pahang State, Malaysia. Thus, four years (2014, 2017, 2020, and 2022) of time series analysis of Landsat 8 images were applied to generate NDMI, normalized difference vegetation index (NDVI), and land surface temperature (LST) for landslide detection. The extraction of NDMI and slope values was carried out in a GIS environment. Finally, the accuracy of the model was measured using the kappa statistic based on the ground truth data. The results revealed that landslides were detected in around 2,234 ha of the study area. Most of the landslides occurred in the northern and western parts, which corresponded with steep slopes (>55%) and low NDMI (<0.1). The LST had a notable impact on slope stability, where landslide areas tended to have a high temperature. There was an increase in LST of about 3°C in the study period. The change from forest areas to built-up areas was one of the contributors to the land temperature increasing in the Cameron Highlands area.

Soil Moisture Content Prediction in Loam Soil with RFR Model

Soil moisture content (SMC) is an important factor in agricultural productivity; it has an impact on crop growth, water use efficiency, and soil health. However, accurately predicting SMC, especially at deeper soil layers, remains challenging due to high variability and limited spatiotemporal data resolution. This study developed and evaluated a Random Forest Regression (RFR) model to predict SMC in loam soil at five different depths (5, 20, 40, 60, and 80 cm) utilizing meteorological data (temperature, humidity, precipitation, wind speed, and solar radiation) and vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Data were collected during the maize vegetation season in 2023 in Mosonmagyaróvár, Hungary. The results showed that the mean SMC ranged from 12.61% to 16.19%. Correlation analysis demonstrated that precipitation and NDMI had the strongest positive correlation with SMC, especially at shallower depths r = 0.78 at 5 cm depth, Solar radiation had a moderate correlation with SMC, especially at the deeper depths. The RFR model performed well at all depths, achieving an R² of 0.86 at 5 cm depth; the model accuracy enhanced at deeper layers, achieving R² values of 0.91 and 0.94 at 60 and 80 cm depths, respectively. The most significant predictors according to the feature importance analysis were precipitation, humidity, and NDMI, with NDMI playing a crucial role in subsurface moisture retention at deeper depths. These findings highlight the potential for machine learning algorithms to optimize irrigation approaches and improve water management in precision agriculture.

Assessment of Forest Fire Severity for a Management Conceptual Model: Case Study in Vilcabamba, Ecuador

Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions.

Agricultural Expansion and Landscape Heterogeneity Explain Vegetation Degradation in Brazilian Atlantic Forest Landscapes

ABSTRACTSeveral anthropogenic activities in tropical forests, including deforestation and fragmentation, have led to intensive forest degradation. In fact, forest edges adjacent to anthropogenic land‐uses can be more exposed to abiotic changes and consequently lead to vegetation deterioration in forest interiors. However, there is still a lack of information on how temporal changes in land use can impact the vegetation degradation of tropical forests. Here, we investigate the influence of landscape context in explaining patterns of forest vegetation degradation over 35 years in two regions exhibiting different deforestation patterns in the threatened Atlantic Forest. Using the Normalized Difference Moisture Index (NDMI) and the Enhanced Vegetation Index (EVI), we assessed forest degradation in the center of 50 forest remnants. In particular, we estimated these indices and landscape metrics in a time series from 1985 to 2020, at a 5‐year interval, based on MapBiomas land‐use classification and Landsat imagery. Then, we used generalized linear mixed models to assess the influence of landscape metrics over time on NDMI and EVI. Our results indicated that forest degradation was intensified according to the increase in agricultural cover, although sites inserted in more heterogeneous landscapes showed reduced degradation (i.e., an increase in NDMI and EVI). We also observed that forest remnants surrounded by greater agricultural cover showed a decline in NDMI, especially in the initial years of the time series. We therefore emphasize the importance of curbing the agricultural expansion to reduce the long‐term forest degradation in Atlantic Forest remnants and suggest long‐term monitoring of forest quality using vegetation spectral indices.

Dataset Construction for Monitoring the Effects of Ecosystem Restoration Projects

Climate change have increased the necessity for developing climate adaptation strategies. Ecological restoration projects have been implemented to restore the structure and function of degraded natural environments, thus enabling adaptation to the climate crisis. However, due to the lack of subsequent monitoring and evaluation, the effectiveness and success of these restoration efforts remain unverified. Verification of restoration effectiveness is essential for establishing ecological restoration and adaptation policies. Therefore, this study catalogs 338 cases of ecosystem restoration projects (including Ecosystem Conservation Fund Return Projects, Jayeon Madang Restoration Projects, and Urban Ecological Corridor Restoration Projects) conducted between 2010 and 2023 to effectively select monitoring sites. Using satellite-based spatial data, we quantified the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference moisture index (NDMI) and land surface temperature (LST). This study shows that higher NDVI values significantly lower LST and NDMI, indicating that the more vegetation restored in ecosystems, the more effectively it reduces surface temperatures. The NDVI across all land cover types averaged above 0.2, corresponding to a high vegetation cover density. Specifically, forests exhibited significantly higher NDVI and NDMI, whereas bare land and used area showed significantly higher NDBI and LST. Over the time series, NDVI and NDMI increased, and NDBI decreased, suggesting the positive effects of restoration. The ranges of NDVI, NDBI, NDMI and LST values by land cover type for the 338 restoration project sites provided in this study can be utilized for selecting specific monitoring sites and verifying effectiveness.

Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan

This research offers a fresh understanding of desertification in Turkmenistan by utilizing satellite remote sensing data and machine learning techniques. With 80% of its area covered by desert, Turkmenistan has particular difficulties as a result of the harsh effects of desertification, which are made worse by climate change and irresponsible land use. Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). These indicators include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Bare Soil Index (BSI), Enhanced Vegetation Index (EVI), and land surface temperature (LST). Based on Sentinel-2 satellite data processed by the Google Earth Engine (GEE) platform, the findings show that the country’s northern, central, and eastern regions are undergoing severe desertification. Moreover, RF and XGBoost performed better than the straightforward models like NB and KNN in terms of accuracy (96% and 96.33%), sensitivity (both 100%), and kappa (0.901 and 0.9095). By concentrating on Turkmenistan, this study fills a significant gap and provides a framework for tracking desertification in similar regions around the world.

Identification of Anthropogenic Impact and Indicators of Landscape Transformation in the Fatala River Basin (Republic of Guinea)

The aim of this study is to identify the main factors of anthropogenic impact and indicators of landscape transformation in the Fatala River Basin in the Republic of Guinea. Our fieldwork in the Boke and Kindia regions was the main source of materials and data. The landscape and ecological situation of nine key study plots were characterized. These key plots make up a representative series of transformed and natural landscapes. We complemented our fieldwork with Landsat satellite image analysis. We learned that the main factors of anthropogenic impact in the Fatala River Basin are the systematic burning of vegetation, mechanical disturbances of soil and vegetation cover, the depletion of fertile topsoil, grazing, and the littering of the landscape with household waste. The indicators of landscape transformation are deforestation, changes in the natural vegetation cover, and mechanically disturbed lands. We identified five main stages of agro-landscape development, starting from the clearing of a plot by burning vegetation (stage I) and ending with the completion of the agricultural activity in the plot and its abandonment to restore the topsoil (stage V). The limiting factors of nature management are elevation differences, the rapid restoration of vegetation cover, and rocky/gravelly substrate. It is possible to identify transformed landscapes in large or hard-to-reach regions using satellite images. Thus, natural or quasi-natural landscapes can be identified based on the lower surface temperature relative to the surrounding lands. The normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) could be useful for identifying agricultural pasture plots within a tropical forest using long-term satellite data series. We revealed a tendency for landscape deterioration in the middle and upper parts of the Fatala River Basin, while vegetation cover is being restored in the lower part of the basin. Finally, we propose some measures to rehabilitate transformed landscapes and increase the efficiency of agricultural production in the study region.

Multilayer optimized deep learning model to analyze spectral indices for predicting the condition of rice blast disease

Rice blast disease is one of the most destructive infectious diseases that affects world food security. Proper monitoring and an accurate decision-making process can assist in disease management strategy. Ground surveys and sampling are the less accurate, expensive, and time-consuming processes that are ineffective to check epidemic. Satellite data-driven approach might be an ideal cost and time-efficient technique that can provide an accurate result due to its revisit across farmland. Temperature variation is a salient feature of this disease trajectory. Hence, land surface temperature can be a cardinal property for disease risk estimation. Spectral indices-based analysis can be more efficient for tracking the disease density. In this study, the MODIS satellite-based Land Surface Temperature (LST) parameter is used to indicate the disease in the field. The indicated risk estimation is also examined using ground truth observation to provide less erroneous labeling. Various spectral combination based remote sensing indices were accumulated to audit the disease states. Remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Moisture Stress were obtained from the Sentinel-2 archive. These images, depicting the various indices, are processed through a novel optimized deep learning model to predict the disease condition of farmland. The model is developed using various residual networks with L2 regularization and batch normalization to enhance the performance of the model. A combination of convolution layers is used to extract crucial spectral information from the remote sensing images and processed through fully connected layers to prognosticate the state of the disease. The model can predict with 89.67% accuracy using the EVI parameters for different geographical positions compared with other remote sensing parameters and has less chance of erroneous possibilities. The proposed system will lead to improved agricultural monitoring management for the incidence of leaf blast disease in real-time.

Spatial and Temporal Analysis of Thermal Comfort Before the Pandemic, Lockdown, and the Covid-19 Pandemic Transition Period in Yogyakarta City, Indonesia Using Modified Temperature-Humidity Index

Yogyakarta as a city of education and a tourism city has the potential to experience a very significant change in the concentration of anthropogenic activity between before the pandemic and when the lockdown policy was implemented. Also, during the pandemic transition period, it allows for changes in fluctuating thermal comfort. This study aims to analyse thermal comfort before the pandemic, lockdown, and transition of the Covid-19 pandemic in Yogyakarta City. This study utilised Landsat 8 images before the pandemic, lockdown, and the transition to the Covid-19 pandemic. Next, the land surface temperature (LST) and Normalized Difference Moisture Index (NDMI) were extracted. The LST and NDMI values are then processed to obtain thermal comfort in the form of the Modified Temperature-Humidity Index (MTHI). There was no clear pattern regarding MTHI (as a proxy of thermal comfort) before the pandemic, during the lockdown, and during the Covid 19 pandemic transition in the City of Yogyakarta. This study confirmed that there is a need of a better spectral index that reflects thermal comfort condition of a region.

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)