Vegetation Index Variables Research Articles

Effects of Habitat Loss on Tenebrionidae in Gravel–Sand Mulching Areas of Desert Steppe in Ningxia, China

The desert steppe in Ningxia is the largest natural steppe type in the region, characterized by a fragile ecological environment and low carrying capacity. Gravel–sand mulching, a local agricultural practice, involves using a sand and gravel covering layer to maintain soil warmth and moisture. However, this method has led to ecological problems, such as habitat loss and biodiversity loss. Tenebrionidae, one of the most widely distributed beetle families, is commonly used to indicate habitat degradation and is a significant steppe pest in desert steppe areas. This study, conducted in the gravel–sand mulching areas of Shapotou District, Zhongwei City, Ningxia, classified the habitat loss from low to high in these areas into five grades (I-V) and examined the effects of habitat loss on Tenebrionidae. We collected 6565 Tenebrionidae, with Blaps femoralis, Anatolica nureti, and Pterocoma vittata being the dominant species. The findings reveal that habitat loss grade I had the highest abundance, diversity index, and evenness index of Tenebrionidae, significantly higher than those of grades II–IV. Habitat loss had a significant negative effect on Tenebrionidae abundance, a significant positive effect on the richness index, no significant effect on the vegetation diversity index, a significant positive effect on soil available potassium (APP), and a significant negative effect on soil total phosphorus (TP). Redundancy analysis indicated a positive correlation between Tenebrionidae abundance and the vegetation diversity index; a negative correlation between Tenebrionidae richness, the diversity index, and vegetation indices; a positive correlation between Tenebrionidae abundance and soil TP; and a negative correlation between the Tenebrionidae diversity index and soil TP and soil APP. These findings will contribute to biodiversity conservation and ecological restoration and provide a theoretical basis for steppe management, sustainable agricultural development, and pest monitoring in desert steppe environments.

Distribution Patterns of Urban Spontaneous Vegetation Diversity and Their Response to Habitat Heterogeneity: A Case Study of Five Cities in Heilongjiang Province, China.

Spontaneous vegetation is an important component of urban biodiversity and an excellent agent for exploring the mutual feedback mechanism between urbanization and urban ecosystems. Rapid urbanization has had a significant impact on the composition, structure, and distribution patterns of urban spontaneous vegetation diversity. Studying the diversity distribution patterns and causes of urban plant communities is beneficial for understanding the formation and maintenance mechanisms of plant diversity in specific urban habitats. This study selected five cities in different climate subregions of Heilongjiang Province as research targets and conducted field research using uniform sampling and typical sampling methods. The composition, distribution pattern, and driving factors of spontaneous vegetation were analyzed. The results showed the following: (1) A total of 633 examples of spontaneous vegetation were recorded, belonging to 93 families and 341 genera, mainly consisting of herbaceous plants and native plants. (2) The diversity index and similarity index of spontaneous vegetation in gravel-type abandoned land habitats are higher than those in other habitat types, while the diversity index of spontaneous vegetation in trees and shrubs is lower, and there is no significant difference in regards to different habitats. (3) Urban population density is a key factor affecting the diversity of native plants, while woody plant coverage, patch area, and landscape trait index are key factors affecting non-native plants. (4) The results of canonical correspondence analysis (CCA) showed that the total explanatory power of environmental characteristic factors in regards to the distribution pattern of spontaneous vegetation was 7.5%. The closest distance between adjacent patches, the coverage of woody plants in patches, the distance from the city edge, the patch area, and the surface impermeability of the buffer zones were key factors affecting the distribution of dominant species in spontaneous vegetation communities. The research results will provide an important reference for the conservation of urban biodiversity and the construction of low-maintenance urban green space plant landscapes in Heilongjiang Province.

Using UAV RGB Images for Assessing Tree Species Diversity in Elevation Gradient of Zao Mountains

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower.

Characteristics of Spatial and Temporal Vegetation Index Variability and Its Responses to Temperature and Precipitation in Mongolia

Characteristics of Spatial and Temporal Vegetation Index Variability and Its Responses to Temperature and Precipitation in Mongolia

218 Comparison between vegetation indices to estimate biomass in cereal rye

Abstract Accurate forage mass estimation can lead to improved pasture management, but it requires a significant investment of time on the ground sampling. The use of satellite images has potential to reduce the time needed and thus, expediting decision-making processes. Satellite measure various spectral wavelengths of light reflected by plants (i.e., reflectance), and using satellite images, various vegetation indices (VI) can be calculated, such as the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Spectral Feature Depth Vegetation Index (SFDVI). Each of these indices shows different vegetation properties via mathematical combinations of reflectance in two or more spectral wavelengths. For example, NDVI is calculated by reflectance in red and near infrared wavelengths, which is an effective VI to quantify vegetation greenness and vigor. This study aimed to compare the mentioned VI to evaluate their ability in estimating cereal rye (Secala cereale) mass and to compare them with a traditional method of biomass measurement, which relies on the standing height of the forage. From May 2 to 23, 2023, forage mass was measured in 18 paddocks (1.6 ha/paddock). Mass in the paddock was estimated by clipping four randomly located frames (0.49 m2) to ground level. The standing height of the forage was collected from 20 random points, measured in 0.625 cm increments, within each paddock. The forage mass of the paddocks measured from the clipped forage ranged from 212 to 3,382 kg/ha. PlanetScope satellite images from the day of forage mass sampling were obtained using the Planet Explorer platform. This satellite data was chosen due to its high spatial resolution (3 x 3 m2/pixel) and highly available data (theoretically daily). The four VI were calculated from images in QGIS software. A linear regression of each index against the forage mass estimated from the average of the clipped forage, was conducted in SAS using PROC REG. There was a significant (P < 0.01) correlation between each VI with the forage mass. The adjusted R2 was 0.60, 0.58, 0.41, 0.78 and 0.76 for NDVI, EVI, GNDVI, SFDVI and standing height, respectively. The root mean square error (RMSE) was 518, 528, 629, 379, and 396 for NDVI, EVI, GNDVI, SFDVI, and standing height, respectively. All VI exhibited moderately promising results in cereal rye mass estimation. In particular, SFDVI proved to be superior with the greatest adjusted R² and least RMSE, followed by the traditional method of estimating forage mass by standing height. This comparable result between the use of SFDVI from satellite images and traditional ground measurement indicates that the use of VI, SFDVI, specifically in our study, from satellite images can be an effective means for a fast cereal rye mass estimation.

Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.

Mangrove Species Diversity and Its Use as Medicinal Plant by Coastal Communities of Lampung Timur Indonesia

Coastal communities have empirically utilized and proven the medicinal properties of mangrove plants. The objective of the study was to determine the species diversity of mangrove plants and their use as herbal medicine on the coast of Lampung Timur, Sumatra, Indonesia. Mangrove plant diversity was obtained through vegetation analysis and diversity index calculation. The use of mangroves as medicine was obtained through interviews and descriptive comparative analysis. The study revealed that the mangrove forest located on Lampung Timur Coastal comprises 22 species from 12 families. Out of these 22 species, 12 are true mangroves, and the remaining 10 are mangrove associates. Among the 12 true mangroves, the majority (10 species) are major mangroves, while the other two are minor. The diversity index of the Lampung Timur mangrove forest is categorized as moderate (H'= 1.9580). The use of mangrove plants as herbal medicine persists in the Lampung Timur Coastal community. Among the users of mangrove herbal medicine, the majority (55.71%) are from the older generation. Additionally, 64% of users are male, 57.37% are categorized as low-income, and 60.12% identify as Javanese based on ethnicity. The community uses various plant species for medicinal purposes. Avicennia marina is used as an antiseptic, anti-histamine, and source of vitamins. Acanthus ilicifolius is used as an anti-rheumatism and muscle ache medicine/analgesic. Rhizophora apiculata is used as an antipyretic, R. mucronata is used to increase stamina/vitality, and Soneratia caseolaris is used as an anti-cholesterol and anti-oxidant.

Late Holocene vegetation diversity change and potential response to climate variations on the northern Qinghai-Tibetan Plateau

Biodiversity change under current global warming scenario has attracted wide attentions, representing general stability and balance in ecosystems. Understanding the temporal patterns and potential driving mechanisms of biodiversity changes would provide significant knowledge for ecosystem sustainability, which requires necessary investigations on long-term records during the late Holocene. The high elevation and specific environment on the Qinghai-Tibetan Plateau (QTP) promote the development of fragile alpine ecosystems, responding sensitively to regional climate variations. Based on a high-resolution fossil pollen record retrieved from Lake Kusai on the northern QTP, late Holocene vegetation diversities were estimated based on the Hill number indices, which were subsequently evaluated at three time scales, i.e., millennial, centennial and multi-decadal time scales. The results indicate that Hill number indices could represent vegetation diversity changes on the northern QTP. Reconstructed vegetation diversity indices indicate gradual variations on millennium scale in each diversity index, along with different responses to environmental factors of regional moisture and total solar irradiance. On the centennial time scale, high consistencies were identified among the vegetation diversity indices as well as correlations with environmental factors, revealing overall positive responses between diversity variations and environmental changes. Rather complicated correspondences to environmental factors appeared on the multi-decadal scale, showing different patterns in diversity indices, which illustrates variations through time as well. Accordingly, vegetation diversity changes of the alpine communities on the northern QTP experienced serious variations during the late Holocene, and revealed complicated responses to regional environmental changes, that further investigations particularly at different time scales would be necessary in the future.

The low diversity of vegetation seedling and sapling threatening the tree existence at the university green space of Universitas Sumatera Utara, Indonesia

University of Sumatera Utara (USU) located in the densely populated urban area and committed to maintain the vegetations of green space which are fundamental to the key indicator in green campus ranking. Green open spaces are a vital element in urban areas providing the comfortable-healthy spaces for urban communities and numerous of environmental services. Specifically, the quality of environmental services depends on the extensive of green spaces, climate characteristic, and existing vegetation as the natural element at the area. Vegetation analysis is the method to study the composition, structure, and form of plant communities that is a prerequisite for describing diverse ecological processes and dynamics of species community in a forest. This study aims to analyze the important value, diversity, evenness, and richness index of USU green space vegetation based on the growth levels from seedlings to trees. The sampling plot was quantified using 2% sampling intensity and positioned with a simple random method. Species name, individual and species number, diameter at breast height (DBH), and stand height were recorded in the designated plots and subplots for each life stage. This research documented 21 species belonging to 12 families at USU green space. Swietenia mahagoni was the dominant species at all growth level with the IVI of 141.07 % (trees), 144.21 % (poles), 133.33 % (saplings), and 135.37 % (seedlings). The species diversity of trees and poles was moderate (H’= 1.6 – 1.7), whereas of saplings and seedlings was low (H’= 0.6 – 0.7). Vegetation evenness at all growth levels was high (E= 0.6 – 1.0). The species richness of trees was identified as moderate (R= 3.5) and of seedlings, saplings, and poles was low (R= 0.2 – 2.5). The seedlings and saplings were inadequate indicating the poor regeneration threatening the tree existence at USU green space.

Quantifying Forest Cover Loss as a Response to Drought and Dieback of Norway Spruce and Evaluating Sensitivity of Various Vegetation Indices Using Remote Sensing

The Norway spruce is one of the most important tree species in Europe. This tree species has been put under considerable pressure due to the ongoing impacts of climate change. Meanwhile, frequent droughts and pest outbreaks are reported as the main reason for its dieback, resulting in severe forest cover loss. Such was the case with Norway spruce forests within the Kopaonik National Park (NP) in Serbia. This study aims to quantify, spatially and temporally, forest cover loss and to evaluate the sensitivity of various vegetation indices (VIs) in detecting drought-induced response and predicting the dieback of Norway spruce due to long-lasting drought effects in the Kopaonik NP. For this purpose, we downloaded and processed a large number of Landsat 7 (ETM+), Landsat 8 (OLI), and Sentinel 2 (MSI) satellite imagery acquired from 2009 to 2022. Our results revealed that forest cover loss was mainly driven by severe drought in 2011 and 2012, which was later significantly influenced by bark beetle outbreaks. Furthermore, various VIs proved to be very useful in monitoring and predicting forest health status. In summary, the drought-induced response detected using various VIs provides valuable insights into the dynamics of forest cover change, with implications for monitoring and conservation efforts of Norway spruce forests in the Kopaonik NP.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Effects of different ecological restoration methods on the soil physicochemical properties and vegetation community characteristics of the Baotou light rare earth tailings pond in Inner Mongolia, China.

This study investigated the soil physicochemical properties and vegetation community characteristics of the Baotou light rare earth tailings pond after 10years of aggregate spray seeding ecological restoration (S1) and ordinary soil spray seeding ecological restoration (S2), and the naturally restored dam slope area without human intervention (S3). The results showed that the vegetation community of S1 was dominated by Caragana korshinskii Kom, and its importance and abundance values were 0.40 and 38.4, respectively, while the vegetation communities of S2 and S3 mainly comprised herbaceous plants. Additionally, the vegetation biomass of S1 was significantly higher than that of S2 and S3 by 215.20% and 1345.76%, respectively, and the vegetation diversity index of S1 was the highest among the three treatment groups. The soil porosity (SP), water content (W), electrical conductivity (EC), and available K were significantly improved in S1, while soil bulk density (BD) was significantly reduced compared with that of S2 and S3. In addition, redundancy analysis revealed that SP, EC, W, and K positively correlate with the biomass, Shannon, Pielou, Simpson, and Marglef indices. Principal component analysis further showed that the comprehensive score of S1 (0.983) was higher than that of S2 (- 0.261) and S3 (- 0.648). Collectively, these findings indicate that appropriate ecological restoration can improve soil structure and vegetation community characteristics, thereby accelerating vegetation restoration, ultimately increasing the stability of the ecosystem.

Construction and assessment of an indicator system of stream conditions in a typical mountain landscape in Northeast China.

Stream ecosystem health assessments are crucial for the effective management of aquatic ecosystem service functions and the assessment of anthropogenic and climatic impacts on aquatic ecosystems. However, there is still a lack of comprehensive assessment methods based on complete ecosystem structures. This study improved an ecological health assessment system, which consists of 13 indicators based on ecosystem structure. The assessment system was applied to 30 streams on Changbai Mountain. The results showed that the comprehensive index of stream health (ISH) in Changbai Mountain was relatively high, and the ISH increased with the elevation gradient. The dominant factors controlling the ecosystem health level were water pH, water flow, vegetation coverage, total phosphorus (TP), and vegetation diversity index. All these results indicated that the damage caused by excessive deforestation and farmland occupation in recent years has not been fully reversed and that human disturbance from tourism may be the main reason for the low stream health at lower elevations. Our results suggest that the key to future work is to increase quantitative research on disturbance sensitivity and to develop economically viable restoration measures.

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

AbstractThe effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor‐intensive manual methods, which are difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α‐diversity index on a sub‐meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semiautomate large‐scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry‐standard supervised classification algorithms (support vector machine [SVM], maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible‐band difference vegetation index, soil‐adjusted vegetation index, modified soil‐adjusted vegetation index, and excess green–excess red) were used to assess vegetation diversity obtained from on‐ground survey plot data (the Margalef, Pielou, Simpson, and Shannon–Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine‐scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

Assessing unmanned aerial vehicle‐based imagery for breeding applications in St. Augustinegrass under drought and non‐drought conditions

AbstractThe use of imagery collected from small unmanned aerial vehicles (UAVs) in turfgrass breeding has rapidly increased, as has the demand to develop drought‐resistant cultivars. However, prior to adopting UAVs to help guide turfgrass selection under drought stress conditions, a clear understanding of the value and predictive ability of imagery‐based turfgrass characterization is required. In St. Augustinegrass, a major warm‐season turfgrass species grown in the Southeastern United States, limited research has been published about characterizing drought stress using aerial imagery. Specifically, no efforts have compared the various vegetation indices (VIs) commonly used to evaluate vegetative health in other species and sought to identify the most useful index for phenotyping drought stress traits in St. Augustinegrass. In this study, traditional ground‐based approaches for measuring percent green cover (PGC) and normalized difference vegetation index (NDVI) were compared against their UAV‐derived counterparts as well as 13 VIs under drought and non‐drought conditions, and broad‐sense heritability (H2) was calculated. A population of 115 genotypes from a ‘‘Raleigh’’ × ‘‘Seville’’ cross were analyzed at two environmentally distinct field sites in North Carolina. At both sites, a significant relationship between ground‐based and UAV‐derived measurements for PGC and NDVI was observed before and during drought (r = 0.82 to 0.95) and suggests a clear advantage to using UAVs for phenotyping drought traits given the reduced time and labor costs compared to on‐ground efforts. Among all VIs compared, UAV‐derived NDVI (NDVI‐U) showed strong correlation with the PGC taken on the ground (r > 0.85), a similar trend over time, and a higher H2 estimate under drought conditions, suggesting that NDVI‐U has the potential to assist in the selection of St. Augustinegrass genotypes with the best phenotypic response to drought. Implementing UAV imagery‐based high‐throughput methods will allow breeders to evaluate germplasm with unbiased quantitative consistency, quickly and thoroughly, and with increased frequency—all without sacrificing the response to selection potential.

ANALISIS EKOLOGI PADA SKEMA IZIN PEMANFAATAN PERHUTANAN SOSIAL DI KAWASAN HUTAN LINDUNG GUNUNG RAKUTAK JAWA BARAT

Social forestry is a program that aims to realize sustainable forest management. There are three important components in Social Forestry, namely ecology, economy, and social. Ecological characteristics are one important aspect that indicates the success of the program. The purpose of this study was to identify the ecological characteristics of the Social Forestry Permits (SFP) scheme in Mount Rakutak Protected Forest, KPH Bandung Selatan. The data collected are land characteristics and vegetation characteristics. The method used is field observation, codding and literature study. The land characteristics in Mount Rakutak Protected Forest area are loam-sandy soil, soil temperature ranges from 20.5°C-29.8°C, soil moisture ranges from 13-90%, soil pH ranges from 2-6.9, and slope ranges from 60 -85%. The total land cover area decreased by 0.04 m2 in 2019, and, the average NDVI in 2021 (after SFP) has decreased compared to 2015 (before SFP). The results show that the SFP Scheme has not had a significant impact on forest coverage in Mount Rakutak Protected Forest area. Vegetation characterization based on vegetation composition was classified as moderate with an average of 3 species found in each village. The vegetation diversity index is low to medium with values ranging from 0-1.95, and there are four dominant species, namely Eucalyptus spp., Pinus merkusii, Persea americana, and Coffea robusta. Based on ecological analysis, it indicates that the Social Forestry Program with the SFP scheme has not shown a sustainable condition.

Impact of precipitation and grazing on recovery of plant vegetation in temperate grasslands

AbstractGrasslands support a rich diversity that plays a pivotal role in ecological balance, carbon sequestration, and climate regulation. Grassland ecosystems are facing a loss of biodiversity and a decline in soil function due to precipitation variations caused by climate change. However, variations in grassland species diversity are not clearly known, especially under the combined effects of precipitation and grazing. Based on a 5‐year split‐plot experiment with two grazing regimes (i.e., moderate grazing and grazing prohibition) along with three precipitation controls (ambient precipitation and ±50% precipitation) in Inner Mongolian grasslands, we researched the response of precipitation variability and grazing prohibition to vegetation diversity indices and investigated the response of vegetation community composition and biomass to these factors. Five years results showed that increasing precipitation resulted in a significant increase in the coverage of perennial forbs, annual and biennial plants, and Gramineae. Grazing prohibition and increased precipitation significantly increased biomass and species richness compared to all other treatment combinations. Interestingly, grazing exclusion significantly reduced the percentage of Amaryllidaceae, especially in the presence of altered precipitation. The vegetation community composition of a typical grassland was correlated with soil volumetric water content (VWC), bulk density (SBD) and pH, soil organic carbon (SOC), and total nitrogen (TN). VWC, SBD, and TN were found to be the most significant factors affecting vegetation diversity under grazing. Partial canonical ordination revealed that soil moisture, soil bulk density, and SOC had the most significant correlation with vegetation diversity under both grazing prohibition and increased precipitation treatment conditions. These results suggested that alterations in precipitation can change the species composition and ecosystem function of plant communities, leading to a close relationship between species diversity and soil properties. Our results underline the importance of species diversity for soil characteristics and environmental factors in a typical Eurasian steppe ecosystem.

Temporal convolutional network based rice crop yield prediction using multispectral satellite data

Early prediction of crop yield has a significant role in ensuring food security. The crop yield depends on several parameters, such as vegetation parameters, climatic parameters, soil condition, etc. Spatial and temporal analysis of cropland is necessary for accurate prediction of yield. Usage of satellite images along with climatic data improves the prediction accuracy. This paper outlines a novel crop yield prediction model for the Paddy from Moderate Resolution Imaging Spectroradiometer (MODIS) data and climatic parameters. Various vegetation indices (VI) are collected from MODIS data for the crop’s entire life cycle. The proposed Temporal Convolutional network (TCN) with a specially designed dilated convolution module predicts the rice crop yield from vegetation indices and climatic parameters. The causal property of TCN and dilated convolution contribute to the multivariate time-based analysis of the crop and results in better performance.

Prediction of Potassium Content in Rice Leaves Based on Spectral Features and Random Forests

The information acquisition about potassium, which affects the quality and yield of crops, is of great significance for crop nutrient management and intelligent decision making in smart agriculture. This article proposes a method for predicting the rice leaf potassium content (LKC) using spectral characteristics and random forests (RF). The method screens spectral characteristic variables based on the linear correlation analysis results of rice LKC and four transformed spectra (original reflectance (R), first derivative reflectance (FDR), continuum-removed reflectance (CRR), and normalized reflectance (NR)) of leaves and the PCA dimensionality reduction results of vegetation indices. Following a second screening of the correlated single band and vegetation index variables of the four transformed spectra, the RF is used to obtain the mixed variable (MV), and regression models are developed to achieve an accurate prediction of rice LKC. Additionally, the effect of potassium spectral sensitivity bands, indices, spectral transformation form, and different modeling methods on rice LKC prediction accuracy is assessed. The results showed that the mixed variable obtained with the second screening using the random forest feature selection method could effectively improve the prediction accuracy of rice LKC. The regression models based on the single band variables (BV) and the vegetation index variables (IV), FDR–RF and IV–RF, with R2 values of 0.62301 and 0.7387 and RMSE values of 0.24174 and 0.15045, respectively, are the best models. In comparison to the previous two models, the MV–RF validation had a higher R2 and a lower RMSE, reaching 0.77817 and 0.14913, respectively. It can be seen that the RF has a better processing ability for the MV that contains vegetation indices and IV than for the BV. Furthermore, the results of different variable screening and regression analyses also revealed that the single band’s range of 1402–1428 nm and 1871–1907 nm, as well as the vegetation indices constituted of reflectance 1799–1881 nm and 2276–2350 nm, are of great significance for predicting rice LKC. This conclusion can provide a reference for establishing a universal vegetation index related to potassium.