NDVI Vegetation Research Articles

Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method

The radar-backscattering coefficient (σ0) depends on surface characteristics and instrumental parameters (wavelength, polarization, and incidence angle). For Sentinel-1 (S1), with incidence angles ranging from 25° to 45°, σ0 for similar targets typically differs by a few dB depending on their localization in the S1 swath. Overcoming this angular dependence is crucial for the operational applications of radar data. In theory, σ0 follows a cosine function with an exponent “N” that represents the degree of dependence between σ0 and the incidence angle. In order to reduce the effect of the incidence angle on σ0, dynamic N normalizations based on vegetation descriptors, NDVI and SAR Ratio (VV/VH), were applied and then compared to the results obtained with temporally fixed N normalizations. N was estimated at each S1 date during the period of the study for three main summer crops: corn, soybean, and sunflower. Analysis shows that the angular dependence of the S1 σ0 is similar for all three crops. N varies from 3.0 for low NDVI values to 2.0 for high NDVI values (stage of maximal vegetation development) in the VV polarization and from 2.5 to 1.5 for the VH polarization. Furthermore, N fluctuates strongly during the periods before plant emergence and after harvesting, due to variations in the soil roughness. Finally, the results demonstrated that the dynamic normalization of σ0 significantly reduces its angular dependence compared to fixed N (N = 1 and N = 2), with SAR ratio-based normalization performing similarly to NDVI-based normalization.

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

Analysis of Spatial and Temporal Trends of Vegetation Cover Evolution and Its Driving Forces from 2000 to 2020—A Case Study of the WuShen Counties in the Maowusu Sandland

The WuShen counties in the hinterland of the Maowusu Sandland are located in the “ecological stress zone” of the forest–steppe desert, with low vegetation cover, a strong ecosystem sensitivity, and poor stability under the influence of human activities. Therefore, it is important to study and analyze the changes in vegetation growth in this region for the purpose of objectively evaluating the effectiveness of desertification control in China’s agricultural and pastoral intertwined zones, and formulating corresponding measures in a timely manner. In this paper, the spatial and temporal variations in the vegetation NDVI in the WuShen counties of the Maowusu Sandland and their response relationships with driving factors were investigated by using a trend test, center of gravity transfer model, partial correlation calculation, and residual analysis, and by using the MOD13A3 vegetation NDVI time series data from 2000 to 2020, as well as the precipitation, temperature, and potential evapotranspiration data from the same period. The results showed the following: ① The regional vegetation NDVI did not fluctuate significantly with latitude and longitude, and the NDVI varied between 0.227 and 0.375 over the 21-year period, with a mean increase of 0.13 for the region as a whole and an increase of 0.61 for the region of greatest change. Of the area, 86.83% experienced a highly significant increase, and the trend in increase around rivers and towns was higher than that in the northwestern inland flow area, with the overall performance of “low in the west and high in the east”. ② Only 2.07% of the vegetation NDVI center of gravity did not shift, and the response with climate factors was mainly characterized by having consistent or opposite center of gravity changes with precipitation and potential evapotranspiration. ③ Human activities have been the dominant factor in the vegetation NDVI change, with 75.89 percent of the area positively impacted by human activities, and human activities in the southwest inhibiting the improvement of vegetation in the area. The impact of human activities on the unchanged land type area is increasing, most obviously in the farmland area, and the impact of human activities on the changed land type area is gradually decreasing in the area where the farmland becomes impervious. The vegetation in the area above 1300 m above sea level is degraded by the environment and human activities. The research results can provide scientific support for the implementation of ecological fine management and the formulation of corresponding ecological restoration and desertification control measures in the Maowusu Sandland. At the same time, it is expected to serve as a baseline for other studies on the evolution of vegetation in agro-pastoral zones.

Assessing Precipitation Impact on Vegetation Using Satellite Data and Remote Sensing Techniques: A Case Study of Yakawalang-Afghanistan

Water is essential for all living creatures, including humans. Precipitation directly impacts plant growth; adequate water supply promotes sufficient growth, while drought conditions lead to inadequate growth and plant desiccation. This research investigates the effects of precipitation on vegetation in the Yakawalang District. The study utilizes NDVI vegetation data from the MODIS Terra satellite and precipitation data from the Global Precipitation Measurement (GPM) system. This study employed the non-parametric Mann-Kendall test to identify changes in the obtained data. The research covers the period from 2010 to 2020. Our findings indicate an increasing trend in precipitation and vegetation in the Yakawalang District during the study period. Interestingly, 2010 recorded the lowest precipitation (277 mm) but the highest vegetation index (0.100). Conversely, 2015 saw high precipitation (510 mm) but the lowest vegetation index (0.079). Further analysis of temperature data for 2015 suggests that low temperatures may have inhibited vegetation growth despite increased precipitation. This research concludes that precipitation alone does not guarantee increased vegetation. Other factors, such as appropriate temperature, are crucial in vegetation growth. These findings underscore the complex interplay of environmental factors in plant development and highlight the need for comprehensive approaches to understanding and managing vegetation dynamics.

Monitoring the effects of climate change and topography on vegetation health in Tharparkar, Pakistan

ABSTRACT Pakistan's geographic position and socioeconomic profile make it one of the nations that are particularly susceptible to the negative effects of climate change. The Tharparkar district in Pakistan is of particular importance in this regard as it is an arid region with serious environmental issues like drought, desertification, and soil degradation. Therefore, the purpose of this study is to examine how topographic and climatic factors affect vegetation indicators in the Tharparkar. The study utilizes spatiotemporal data spanning over 20 years (2001–2020) collected from the satellites MOD11A2 and MOD13A3. The collected data are processed using a range of tools in ArcGIS 10.4.1, and the impact of topographic and climatic conditions is analyzed based on different vegetation indices, including EVI, NDVI, STVI, OSAVI, and SAVI. The findings reveal that temperature and precipitation, both of which are controlled by topographic features, such as elevation and slope, are the key elements affecting vegetation in Tharparkar. At high elevations, rainfall (>440 mm) and LST (>39 °C) are also high and where the slope is low the density of vegetation indices is high.

Мониторинг границ биомов суши в глобальном масштабе по данным спутниковых наблюдений

Global climate change leads to the transformation and imbalance of all natural systems, to a change in the boundaries of biomes. In this regard, it became necessary to monitor the boundaries of land biomes and to search for methods for determining the boundaries of biomes, ecotones and their control. The aim of this work is to monitor the boundaries of biomes, and to study the seasonal dynamics of NDVI dispersion. The methodological basis of the study is satellite observations and the NDVI moving dispersion method. The dynamics of the moving dispersion of NDVI biomes and ecotones of the land from 2000 to 2020 is estimated in the work. As a result of research, a raster map of the spatial distribution of NDVI dispersions for various biomes (plant communities) was constructed. The raster map of the dispersion distribution of NDVI vegetation is based on MODIS satellite data (product MCD12C1). It is shown that dispersions of NDVI biomes (zones with a certain type of vegetation) have low values. In the transition zones between biomes in ecotones, the dispersion of NDVI increases significantly. The raster map was compared with a biome vector map. The coincidence of biomes on vector biological maps and raster maps showing dispersions of NDVI was revealed. The main factors determining the boundaries of biomes are the spatial and temporal distribution of temperatures, the greatest changes of which occur in the south of the tundra zone and in the north-boreal forests. Studies of the dynamics of biotemperature in the tundra for the period 2001–2020 years showed a positive trend. The results of the spatial distribution of NDVI dispersions over the seasons of the year (winter, spring, summer, and autumn) give an idea of the dynamics of phenological processes on a global scale.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Spatial-Temporal Evolution Characteristics and Driving Force Analysis of NDVI in Hubei Province, China, from 2000 to 2022

Exploring the characteristics of vegetation dynamics and quantitatively analyzing the potential drivers and the strength of their interactions are of great significance to regional ecological environmental protection and sustainable development. Therefore, based on the 2000–2022 MODIS NDVI dataset, supplemented by climatic, topographic, surface cover, and anthropogenic data for the same period, the Sen+Mann–Kendall trend analysis, coefficient of variation, and Hurst exponent were employed to examine the spatial and temporal characteristics and trends of NDVI in Hubei Province, and a partial correlation analysis and geographical detector were used to explore the strength of the influence of driving factors on the spatial differentiation of NDVI in vegetation and the underlying mechanisms of interaction. The results showed that (1) the mean NDVI value of vegetation in Hubei Province was 0.762 over 23 years, with an overall increasing trend and fluctuating upward at a rate of 0.01/10a (p < 0.005); geospatially, there is a pattern of “low east and high west”; the spatial change in NDVI shows a trend of “large-scale improvement in the surrounding hills and mountains and small-scale degradation in the middle plains”; it also presents the spatial fluctuation characteristics of “uniform distribution in general, an obvious difference between urban and rural areas, and a high fluctuation of rivers and reservoirs”, (2) the future trend of NDVI in 70.76% of the region in Hubei Province is likely to maintain the same trend as that of the 2000–2022 period, with 70.78% of the future development being benign and dominated by sustained improvement, and (3) a combination of partial correlation analysis and geographical detector analysis of the drivers of vegetation NDVI change shows that land cover type and soil type are the main drivers; the interactions affecting the distribution and change characteristics of NDVI vegetation all showed two-factor enhancement or nonlinear enhancement relationships. This study contributes to a better understanding of the change mechanisms in vegetation NDVI in Hubei Province, providing support for differentiated ecological protection and project implementation.

A high-resolution map of diffuse groundwater recharge rates for Australia

Abstract. Estimating groundwater recharge rates is important to understand and manage groundwater. Numerous studies have used collated recharge datasets to understand and project regional- or global-scale groundwater recharge rates. However, recharge estimation methods all have distinct assumptions, quantify different recharge components and operate over different temporal scales. We use over 200 000 groundwater chloride measurements to estimate groundwater recharge rates using an improved chloride mass balance (CMB) method across Australia. Groundwater recharge rates were produced stochastically using gridded chloride deposition, runoff and precipitation datasets. After filtering out groundwater recharge rates where the assumptions of the method may have been compromised, 98 568 estimates of recharge were produced. The resulting groundwater recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of groundwater recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality and potential evapotranspiration, exert the most significant influence on groundwater recharge rates, with vegetation (the normalised difference vegetation index or NDVI) also contributing significantly. Importantly, the mean values of both the recharge point dataset (43.5 mm yr−1) and the spatial recharge model (22.7 mm yr−1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method, the potential disparities arising from distinct recharge estimation methodologies and limited averaging across climate zones. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation and the spatial recharge models collectively provide valuable insights for water resource management across the Australian continent, and similar approaches can be applied globally.

Changes in Vegetation NDVI and Its Response to Climate Change and Human Activities in the Ferghana Basin from 1982 to 2015

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development.

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses.

Analysis of NDVI and Plant Vegetation Diversity in the Traditional Zone, Mount Halimun Salak National Park, Bogor

Mount Halimun Salak National Park (MHSNP) traditional zone is a zone which is highly utilized by the local community to fulfill their daily needs by utilizing non-timber forest resources and implementing an agroforestry system with the main commodity is poh-pohan (Pilea melastomoides). Forest monitoring in the MHSNP traditional zone is very important to do as part of sustainable forest management and the realization of supporting Indonesia's Forestry And Other Land Use (FOLU) Net Sink 2030 program. This study aims to analyze the density of vegetation based on NDVI values; besides, analyze the level of plant species diversity and stand structure in the traditional zone of MHSNP. Furthermore, the data collection of plant species diversity at the area based on NDVI value of vegetation density. NDVI values ​​are obtained into three classes that have different land conditions. The class 1 value range from 0.147 to 0.273 has a low vegetation density. Class 2 from 0.273 to 0.319 has medium vegetation density. Meanwhile, class 3 has high vegetation density with a value of 0.319 to 0.433. Diversity of plant species has 60 different species from a total of three classes. The density of seedlings is lower than saplings and at the level of poles, trees is decreasing that is indicates a balanced stand structure. The low seedling level is caused by inhibition of seedling growth due to cleaning by the local community in preparation for planting understory since the community prefers to plant understory which harvest faster woody plant seedling.

Analysis of Vegetation NDVI Changes and Driving Factors in the Karst Concentration Distribution Area of Asia

Due to the special nature of karst landforms, quantification of their vegetation dynamics and their underlying driving factors remains a formidable challenge. Based on the NDVI dataset, this study uses principal component analysis to extract comprehensive factors and utilizes an optimized parameter-based geographical detector and geographically weighted regression models to assess the explanatory capacity of comprehensive factors concerning the spatial differentiation of vegetation change. The results of this study revealed the following: (1) In terms of temporal and spatial vegetation changes, the Asian karst concentrated distribution area (AKC) displayed overall stability and an increasing trend between 2000 and 2020. Notably, the northern (Southwest China) karst region experienced the most substantial vegetation increase, with increased areas exceeding 70%, primarily concentrated in the provinces of Guizhou and Guangxi. In contrast, the southern (Indochina Peninsula) karst region, particularly in Cambodia, Laos, and Vietnam (CLV), exhibited a significant decreasing trend, with decreased areas exceeding 30%. (2) By analyzing the driving factors affecting vegetation change, vegetation changes exhibited distinct spatial differentiations, along with positive and negative effects. Human factors, including human activity intensity, urban economic development, and agricultural economic development (explanatory power and local R2 were both greater than 0.2), exerted a more significant impact on vegetation change in the AKC than natural factors such as thermal conditions, water conditions, and soil conditions. This impact was positive in Southwest China but inhibited in the Indochina Peninsula, particularly within the CLV karst area. Notably, the interaction between natural and human factors greatly enhanced their impacts on vegetation changes. These results provide valuable insights into vegetation changes and their driving mechanisms, which are crucial for preserving the stability of delicate karst ecosystems and facilitating vegetation recovery.

USING MACHINE LEARNING ALGORITHMS FOR NATURAL HABITATS ASSESSMENT

The potential of AI to process and interpret large volumes of data can provide researchers with a powerful tool to understand and monitor biodiversity on a global scale. In this paper we aimed to identify dominant individual plant species in natural protected habitats. Mapping the dominant species from the targeted natural habitats was followed by testing machine learning algorithm for differentiating similar species using satellite images. In the end we validated the data generated by machine learning algorithms through extensive field observations. Using the Sentinel-2 mission 10m resolution data and comprehensive field mapping we managed to see different phenology variations between diverse types of plant communities. Using the NDVI and NDII vegetation indexes and Random Forest algorithm during the dominant species phenology stages for each consecutive 10-day periods between May 1st and September 10th, revealed distinct responses to climate fluctuations and environmental factors. The natural habitats different signatures are strongly influenced by their ecological and conservation status and are not yet suitable for identification, but could help improve AI’s automatic detection for multiannual analysis if a favorable conservation trend is reached. The main achievement of the proposed methodology is the ability to differentiate between different species of deciduous trees, with machine learning training accuracy generally exceeding 95% and classification accuracy surpassing 90%.

Intercropped Soybean Plant Population in a Coffee Plantation and Its Effects on Agronomic Parameters and Geospatial Information

Coffee farming has high land use value, which can result in economic losses without proper land use planning. Intercropping has improved coffee production by providing an alternative income source to producers, especially in the crop formation phase. The objective of this study was to evaluate productivity, growth, and geospatial data in different soybean plant populations intercropped with coffee. The experiment was conducted at the Federal University of Uberlândia, in Monte Carmelo, Minas Gerais, Brazil. It had an experimental randomized block design with five treatments: the control (no soybeans); 80 thousand plants ha−1; 160 thousand plants ha−1; 240 thousand plants ha−1; and 320 thousand plants ha−1. Productive and vegetative characteristics of coffee and soybeans were evaluated, as well as the NDVI and GNDI vegetation indices. The mass of 1000 grains of soybean reached its peak (178.96 g) with a population of 222 thousand plants of soybean ha−1. The maximum soybean productivity of 102.78 bags ha−1 was obtained in the population of 185 thousand plants soybean ha−1. An increasing population of 240–320 thousand plants ha−1 soybean between coffee rows reduced soybean yield due to reduced light, causing plant lodging. The biometric parameters of the coffee trees did not change, which was evidenced by high normalized and green normalized difference vegetation indices (NDVI and GNDVI, respectively). Therefore, it is concluded that the use of soybeans between the coffee trees does not affect the vegetative and productive parameters of the coffee tree, making the use of the intercrop viable.

ESTIMASI KANDUNGAN BAHAN ORGANIK TANAH DI LAHAN TANAMAN JERUK, KECAMATAN DAU, KABUPATEN MALANG MENGGUNAKAN INDEKS VEGETASI DAN SISTEM INFORMASI GEOGRAFIS

Citrus plants (Citrus sp.) are most plants that grow in Petungsewu Village, Dau District. The decreased productivity of citrus plants causes the availability of citrus plants also to decrease. This needs to be increased to meet citrus plants' needs. The influence of soil organic matter is the main factor causing a decrease in the productivity of citrus plants in Petungsewu Village, Dau District. Therefore, it is necessary to estimate soil organic matter using the NDVI vegetation density index to obtain adequate and efficient results. Reliability testing was also carried out to know the accuracy level of the estimates made. The research was conducted at the Citrus Plantation and Subtropical Fruit Research Institute in Petungsewu Village, Dau District, Malang Regency. Estimation of soil organic matter using the NDVI vegetation density index showed an accuracy rate of 81.1%. The results of the accuracy analysis were strengthened by the presence of a paired t-test with a value of t = 0.01 and a value of p = 0.991.

Проведение мониторинга поверхностного покрова территории города Читы по материалам дистанционного зондирования

Natural areas where settlements are located are a subject to constant anthropogenic impact, which has a negative impact on the environmental situation. In this regard, it becomes relevant to monitor the surface cover of such territories, using remote sensing data. The object of the study is the territory of the urban district “City of Chita”. The purpose is to conduct a spatiotemporal analysis of changes in the va­lues of the NDVI vegetation index from satellite images from 2016 to 2023, to identify natural and artificially created forms of surface cover, describe their quantitative and qualitative composition within the boundaries of the urban district, as well as establish the nature of the dynamics of changes in the obtained values. The research objectives are as follows: preparation and collection of materials for remote sensing of the Earth; description of the territory and structural forms of the surface cover of Chita based on the NDVI index. The following research methodology and me­thods are used: methods of visual and automated decryption of multispectral images; geoinformation methods for analyzing raster and vector data. Archival multispectral Sentinel-2 images for the specified period have been used in the research process. A classification of surface cover forms of the studied territory has been carried out, and an assessment of changes in their areas over an 8-year period has been made for the administrative districts of the city (Tsentralny, Ingodinsky, Chernovsky and Zheleznodorozhny). Monitoring the surface cover of urbanized areas, using vegetation indices obtained from remote sensing data (e. g. Sentinel-2) allows to obtain up-to-date information on the quantitative and qualitative state of vegetation. The above mentioned has helped to make reasoned decisions in the field of territorial planning and urban development, including planning of protected areas, recreation parks, and public gardens in Chita.

Drone agrotechnology’s for cotton (Gossypium hirsutum L.) pest and diseases management in Western of Burkina Faso

Drone technologies, also known as "unmanned aerial vehicles" (UAVs) or "unmanned aerial systems" (UAS), have a wide range of applications, including mapping, land-use planning, crop/infrastructure damage assessment, fertilizer spraying and precision crop protection. However, its use is still very limited in the agricultural sector in Africa in general and in Burkina Faso in particular. This study was initiated to explore the potential of UAVs for optimizing cotton yields through monitoring and phytosanitary efficiency. To this end, phytosanitary treatments and monitoring of vegetation status using multispectral drone imagery were carried out on cotton. The experimental sample consisted of four (04) cotton fields in four (04) locations. Phytosanitary treatments and aerial photography were respectively carried out using a DJI AGRAS T16 spray drone and a DJI Phantom 4 Multispectral drone. Phytosanitary treatment with DJI AGRAS T16 drone resulted in savings of 32.41% in treatment duration, 23.58% in water, 1% in pesticides and 10.5% in labor compared with manual spraying, but did not have a positive impact on boll yields due to jassid populations attack. The RGB and NDVI vegetation indices generated by photogrammetric processing of multispectral images acquired by drone enabled us to perceive phytosanitary state of cotton plants during their development cycle. Information provided by these vegetation indices and by this study in general can be used to improve the efficiency of phytosanitary treatments against cotton pests and diseases.

Comparative analysis of the NDVI vegetation index obtained using Sentinel-2, Sequoia remote sensors and the GreenSeeker terrestrial sensor in corn (Zea mays L.)

In Mexico, the cultivation of corn is part of the daily diet, it is considered the crop with the greatest presence in the country, it constitutes an input for livestock activity, in addition to being the main source for obtaining industrial products. For its part, precision agriculture is based on applying the right number of inputs, at the right time and in the exact place. NDVI is the most popular greenness index, it helps understand vegetation health and land use remotely. The study consisted of carrying out an analysis by comparing the values obtained from this index estimated by the Sentinel-2 and Sequoia indirect remote sensors against the NDVI values estimated by the GreenSeeker terrestrial sensor, which is considered a direct measurement. The results obtained show a very high correlation with an r2 of 0.926 between the Sequoia sensor vs. GreenSeeker while an r2 ratio of 0.802 was obtained between the Sentine-2 sensor vs GreenSeeker. Which can be explained with the difference that exists in terms of spatial resolution between the Sentinel-2 and Sequoia sensors, while one has 10 meters per pixel, the other has 18 centimeters.