Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop yield and water productivity. To reduce the field damage and increase measurement accuracy under traditional canopy water interception measurement, UAVs equipped with multispectral cameras were used to extract in situ crop canopy information. Based on the correlation coefficient (r), vegetative indices that are sensitive to canopy interception were screened out and then used to develop canopy interception models using linear regression (LR), random forest (RF), and back propagation neural network (BPNN) methods, and lastly these models were evaluated by root mean square error (RMSE) and mean relative error (MRE). Results show the canopy water interception is first closely related to relative normalized difference vegetation index (R triangle NDVI) with r of 0.76. The first seven indices with r from high to low are R triangle NDVI, reflectance values of the blue band (Blue), reflectance values of the near-infrared band (Nir), three-band gradient difference vegetation index (TGDVI), difference vegetation index (DVI), normalized difference red edge index (NDRE), and soil-adjusted vegetation index (SAVI) were chosen to develop canopy interception models. All the developed linear regression models based on three indices (R triangle NDVI, Blue, and NDRE), the RF model, and the BPNN model performed well in canopy water interception estimation (r: 0.53-0.76, RMSE: 0.18-0.27 mm, MRE: 21-27%) when the interception is less than 1.4 mm. The three methods underestimate the canopy interception by 18-32% when interception is higher than 1.4 mm, which could be due to the saturation of NDVI when leaf area index is higher than 4.0. Because linear regression is easy to perform, then the linear regression method with NDVI is recommended for canopy interception estimation of sprinkler-irrigated winter wheat. The proposed linear regression method and the R triangle NDVI index can further be used to estimate the canopy water interception of other plants as well as forest canopy.

Background: Rodents severely damage the ecological environment of grasslands, and rodent mounds of different ages require distinct management strategies. Understanding the age of these mounds aids in formulating targeted restoration measures, which can enhance grassland productivity and biodiversity. Current surveys of rodent mounds rely on ground exposure and mound height to determine their age, which is time-consuming and labor-intensive. Remote sensing methods can quickly and easily identify the distribution of rodent mounds. Existing remote sensing images use ground exposure and mound height for identification but do not distinguish between mounds of different ages, such as one-year-old and two-year-old mounds. According to the existing literature, rodent mounds of different ages exhibit significant differences in vegetation structure, soil background, and plant diversity. Utilizing a combination of vegetation indices and hyperspectral data to determine the age of rodent mounds aims to provide a better method for extracting rodent hazard information. This experiment investigates and analyzes the age, distribution, and vegetation characteristics of rodent mounds, including total coverage, height, biomass, and diversity indices such as Patrick, Shannon-Wiener, and Pielou. Spectral data of rodent mounds of different ages were collected using an Analytical Spectral Devices field spectrometer. Correlation analysis was conducted between vegetation characteristics and spectral vegetation indices to select key indices, including NDVI670, NDVI705, EVI, TCARI, Ant, and SR. Multiple stepwise regression and Random Forest (RF) inversion models were established using vegetation indices, and the most suitable model was selected through comparison. Random Forest modeling was conducted to classify plateau zokor rat mounds of different ages, using both vegetation characteristic indicators and vegetation indices for comparison. The rodent mound classification models established using vegetation characteristic indicators and vegetation indices through Random Forest could distinguish rodent mounds of different ages, with out-of-bag error rates of 36.96% and 21.74%, respectively. The model using vegetation indices performed better. Conclusions: (1) Rodent mounds play a crucial ecological role in alpine meadow ecosystems by enhancing plant diversity, biomass, and the stability and vitality of the ecosystem. (2) The vegetation indices SR and TCARI are the most influential in classifying rodent mounds. (3) Incorporating vegetation indices into Random Forest modeling facilitates a precise and robust remote sensing interpretation of rodent mound ages, which is instrumental for devising targeted restoration strategies.

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 alpha = 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 delta(3) > delta(2) > delta(1). 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.

Wildfires have caused natural environmental damage that has contributed to deforestation, consequently demonstrating a significant influence on atmospheric emissions. Wildfires occur frequently in South Korea, especially during the spring season. This study assessed post-wildfires areas in Gangneung, South Korea, on April 11, 2023, which were generated by implementing remote sensing technology and statistical analysis. Remote sensing and classification techniques, including PlanetScope, have been developed for identifying wildfire-damaged areas. The method for classifying post-wildfire mapping estimation includes the utilization of deep learning approaches, especially using the U-Net architecture. Therefore, the assessment of wildfire severity can be conducted using Sentinel-2 and Sentinel-5P imagery in addition to an analysis of the vegetation type and air pollutant within the affected region. In the present study, Sentinel-2 imagery was to generate spectral indices, including the differenced normalized burn ratio (dNBR), differenced normalized difference moisture index (dNDMI), differenced soil adjusted vegetation index (dSAVI), and differenced normalized vegetation index (dNDVI). Sentinel-5P imagery was utilized to produce carbon monoxide (CO) column number densities. The estimation of wildfire areas was conducted using a PlanetScope classified image with the U-Net classifier, which was evaluated based on the overall accuracy value of 95% and kappa accuracy of 0.901. The wildfire severity level was shown by dNBR, which was correlated with the parameters, including RBR, dNDMI, dSAVI, dNDVI, and CO. The statistical analysis demonstrated a significant and positive correlation between the wildfire severity and the parameters. Moreover, the average of vegetation indices (NDMI, SAVI, and NDVI) before and after a wildfire were found to decrease by vegetation type, including 17.55% in mixed barren land areas, 17.49% in other grasses, 24.71% in mixed forest land, 22.48% in coniferous land, 13.48% in fields, and 4.29% in paddy fields. On the basis of the results, these estimates can be employed to identify the level of damage caused by wildfires to vegetation and air quality.

Land cover/land use is one of the main factors influencing the development of soil erosion. It has been included in the calculation and modelling of erosion and sediment transport in many studies. In the current research NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index) are used for quantifying the cover management factor (C-factor). They are calculated on the base of Sentinel 2 multispectral images. Taking into account the vegetation phenology two time points were analyzed: end of May - June - active vegetation and September (beginning of October) - late vegetation. The changes in the values of the indices were considered for 2018, 2021 and 2022. The study area is the watershed of the river Sarayardere, located in the southern part of Bulgaria. This is a hilly to low-mountain area, prone to erosion due to rare vegetation, high slope gradients and a relatively long dry period followed by intensive rainfall. The calculated values of the C-factor are indicators for higher susceptibility to erosion in September than it is in June. The spatial distribution of the C-factor shows different patterns. The results, received on the base of the image of September 2021, show increasing the areas with C-factor 0.5, in comparison with the results of September 2018. C-factor values calculated on the image of October 2022 indicate the highest susceptibility to erosion. Using NDRE instead NDVI results in slightly higher values of the C-factor. The advantage of the NDRE index is that it provides information on the content of chlorophyll in the vegetation during the end of the vegetation period and allows a more accurate assessment of the state of the separate plants, regarding the determination of diseased or damaged plants. In addition to the vegetation indices, an expert evaluation of the state of vegetation was done. The results of the current study show that the watershed of the river Sarayardere is in a relatively good condition regarding the development of erosion processes. The attention should be directed to the possible increase of erosion on deforested slopes and the availability of loose materials, in case of intense rainfall.

Rapid climate warming has widely been considered as the main driver of recent increases in Arctic tundra productivity. Field observations and remote sensing both show that tundra greening has been widespread, but heterogeneity in regional and landscape-scale trends suggest that additional controls are mediating the response of tundra vegetation to warming. In this study, we examined the relationship between changes in vegetation productivity in the western Canadian Arctic and biophysical variables by analyzing trends in the Enhanced Vegetation Index (EVI) obtained from nonparametric regression of annual Landsat surface reflectance composites. We used Random Forests classification and regression tree modelling to predict the trajectory and magnitude of greening from 1984 to 2016 and identify biophysical controls. More than two-thirds of our study area showed statistically significant increases in vegetation productivity, but observed changes were heterogeneous, occurring most rapidly within areas of the Southern Arctic that were: (1) dominated by dwarf and upright shrub cover types, (2) moderately sloping, and (3) located at lower elevation. These findings suggest that the response of tundra vegetation to warming is mediated by regional- and landscape-scale variation in microclimate, topography and soil moisture, and physiological differences among plant functional groups. Our work highlights the potential of the joint analysis of annual remotely sensed vegetation indices and broad-scale biophysical data to understand spatial variation in tundra vegetation change.