Vegetation Index NDVI Research Articles

Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study in Moscow Region, Russia

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication.

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks. Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks. Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks. This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.

Effect of Transient Waterlogging Stress on Growth, Physiology and Yield of Cowpea (Vigna unguiculata L.) in Semi-Arid Region

Cowpea cultivation during rainy season is highly affected by the waterlogging stress due to unpredicted high-intensity rains. The studies on assessment of waterlogging effect on different growth stages of cowpea are necessary for planning mitigation strategies. Hence study was conducted during kharif (June to September) 2022 under factorial randomized block design (FRCBD) set up. The first factor consisted of seven waterlogging durations (3 to 15 days), and second factor was three growth stages of cowpea (15 DAE; Days after emergence, 25 DAE and at 50% flowering). The results revealed that regardless of growth stages, growth and yield attributes were drastically decreased with increased duration of waterlogging. The highest plant height (25.07 cm), number of branches plant–1 (5.33) and leaf area (205.27 cm2 plant–1), and number of pods plant–1 (4.27), pod length (15.24 cm), number of seeds pod–1 (14.27), grain (6.27 g plant–1) and haulm yield (15.62 g plant–1) were recorded with 3 days of waterlogging, whereas lowest values were reported with 15 days of waterlogging. Regarding growth stages, highest growth, and yield attributes were recorded with waterlogging during 50% flowering, followed by 25 DAE and 15 DAE. Moreover, the correlation study indicated that physiological parameters such as leaf protein content (r = 0.95) and Normalized Difference Vegetation Index NDVI (r = 0.97) were positively related to grain yield. It was found that, cowpea is sensitive to high-intensity waterlogging (beyond 3–5 days) especially during the early growth stage (15 DAE).

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.

Использование индексов вегетации в почвенно-ландшафтом картографировании и агроэкологической оценке

In this work, the possibilities of using remote sensing data to clarify the contours of soil differences were evaluated. It was found that visualization of mean annual vegetation indices NDVI allows to specify actual information on the contours of productivity zones. The use of this approach allows clarifying the boundaries of soil differences and identifying the actual boundaries of lithogenic lands with different productivity. It was found that the average annual NDVI in the linear model is related to the content of physical clay, with R2=0.26, the highest NDVI values are observed on soils of medium-loamy and heavy loamy composition in conditions of excessive moisture in the foothills of the Krasnodar Territory with average annual precipitation of more than 850 mm. Keywords: VEGETATION INDICES, REMOTE SENSING DATA, LAND PRODUCTIVITY, AGRO-ECOLOGICAL ASSESSMENT

Identification Leaves of Ulmus pumila L., Tilia cordata Mill. and Acer campestre L. Using Vegetation Indices

The aim of the research was to identify a group of vegetation indices (VIs) for species identification. The objects of research were leaves of Ulmus pumila L., Tilia cordata Mill. and Acer campestre L. Hyperspectral imaging (HSI) was carried out under artificial lighting in laboratory conditions using a Cubert UHD-185 hyperspectral camera. A technique was developed for the automated selection of pure spectral profiles from hyperspectral images by setting a double barrier specified by intervals of PSSR and NDVI VIs. A total of 80 VIs was calculated. A statistical analysis of the data was carried out for their representativeness. To determine VIs, the value of which is most dependent on the species characteristics of trees, analysis of variance (ANOVA) and principal component analysis (PCA) methods were used. Research has shown that the PCA method is effective and sufficient to identify the group of VIs characterized by the greatest dispersion in relation to tree species. The PCA carried out for pairs of tree species made it possible to identify a group of vegetation indices, the value of which to the greatest extent depends on species characteristics. These are Carter2, CI2, CRI4, GMI2, mSR2, NDVI2, OSAVI2, SR1, Carter4, Datt2, SR6, Datt, DD, Maccioni, MTC.

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.

INVESTIGATION OF THE PROCESSES OF ECOLOGICAL AND ECOSYSTEM CHANGES IN WATER BODIES USING UAV DATA

In recent years, the water landscapes of inland rivers have been subjected to significant an- thropogenic impacts exceeding the limits of their self-healing ability. Traditional methods of monitoring ge- oecological parameters - sampling of water in the field with subsequent laboratory analysis - require signif- icant financial and time costs. Traditional methods allow assessing the ecological state of the entire water- course not spatially, but pointwise. In this research, sensors on board the UAV measured solar radiation reflected from the surface of the water and used image data from different camera ranges. The combina- tion of images used was based on a common methodology for identifying key features, and a geographic information system (GIS) modelling approach was used for spatially analyze the distribution and interaction of environmental features. In addition, research the ecological condition of the Yertis River and the quality of water discharged after the municipal wastewater treatment facilities of Ust-Kamenogorsk city into the Yertis River, the data from the DJI Phantom 4 RTK/multispectral drone was processed and the normalized vegetation index NDVI, normalised water level difference index NDWI, water chromaticness index CI, tur- bidity index NDTI and chlorophyll concentration index "a" were calculated. The results obtained can serve as a basis for scientific analysis of the environmental conditions of the researching area, including for learning the processes of eutrophication and other changes in the environment.

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%.

Comparison of Citrus Leaf Water Content Estimations Based on the Continuous Wavelet Transform and Fractional Derivative Methods

Citrus tangerines are famous fruits worldwide, and monitoring the water content of citrus leaves is highly important for citrus production. However, there are still challenges in quantitatively estimating the water content of citrus leaves using hyperspectral technology, and the random noise generated during spectral acquisition and the overlapping peaks in the sensitive band of the citrus leaf water content will affect estimation accuracy. To solve these problems and further explore the roles of the continuous wavelet transform (CWT) and fractional-order derivative (FOD) in the estimation of citrus leaf water content, this study intends to use of CWT and FOD to decompose the original spectrum, and then compare the correlation between the original spectrum and leaf water content to explore whether the decomposition treatment has improved the correlation between spectrum and leaf moisture content. Then, the successive projections algorithm (SPA) was used to select feature bands and combine spectral vegetation indices. Partial least squares regression (PLSR) was used to construct water-content inversion models for citrus leaves, and the inversion accuracies of two commonly used spectral preprocessing methods were compared. The results indicate that (1) the CWT can improve the sensitivity of the spectrum to the citrus leaf water content to a certain extent, and the inversion accuracy of the CWT is approximately 5% greater than that of the FOD. (2) On the basis of the CWT and FOD methods, the inversion accuracy of the citrus leaf water content based on SPA screening increased by 9.61% and 9.29%, respectively, compared with the original spectrum. (3) Under CWT decomposition, Scale4 of the Gaus1 wavelet was screened by the SPA, and the inversion model of citrus leaf water content was constructed by combining the spectral vegetation index NDVI with the best results. The R-squared (R2) and root mean square error (RMSE) values were 0.7491 and 0.0284, respectively, which were both 0.0138 greater than those of the best inversion model for the FOD R2. In conclusion, the CWT-SPA combined with the spectral vegetation index can improve the sensitivity of the spectrum to the citrus leaf water content, eliminate a large amount of redundant data, and enhance the prediction ability and stability of the citrus leaf water content.

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.

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning

The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.

REMOTE IDENTIFICATION OF MICROSEDIMENTAL RELIEF FORMS AND SOIL SECTIONS OF AGROLANDSCAPES OF THE FORESTS OF UKRAINE WITH SIGNS OF HYDROMORPHISM

Background. Agricultural lands play a key role in ensuring the food security of the population and the development of the country's economy. However, excessive wetting poses a significant threat to these lands, as a result of which the conditions for the formation of soils with signs of glaciation and low fertility are formed within the lower relief elements, which significantly reduces their potential. In order to highlight the problems of geospatial identification of micro-recessed landforms (MRLF) on agricultural lands, the article uses spectral indices based on the data of RSE. Methods. 6 spectral indices were selected for the research. They were used to obtain data on areas of soil subsidence on arable lands, namely: NDWI, NWI, NDMI, NDVI, OSAVI, WRI. Solving research tasks involved the use of data from the Sentinel-2A satellite system. In order to visualize the spread of MRLF on the research territory, a high-resolution image (0.2 m per 1 pixel) obtained in the "Digitals Professional 5.0" software was used. Processing and geospatial visualization of the RSE data were performed in the Arc Map environment of the Arc GIS 10.8 program using the raster calculator tool. Results. Within the reference fields, the dynamics of the values of water and vegetation indices were constructed and analyzed, and the identification ability for the geospatial separation of soil areas with signs of hydromorphism was evaluated. It is shown that the identification capacity of the indices depends not only on the level of soil moisture, but also on the biomass of vegetation (scales of crop damage), which is indicated by the high information capacity of the traditional vegetation index NDVI. The most informative index ranges were established: for NDVI, the range is from -0.117 to -0.024 with an identification percentage of 98.0 %; for OSAVI – 78.0 % with a range of 0.255–0.313; for NDMI with a range variation of -0.041 to -0.149 and an identification percentage of 56.0. Сonclusions. The results of remote identification of the areas of the MRLF enabled to obtain information about the moisture content of the soils of the arable lands of the research area. The ability of the specified indices during the geospatial identification of microrecessed landforms (MRLF) and soil areas within them with signs of hydromorphism was evaluated. It is demonstrated that the use of orthophotos with a resolution of 0.2 m per 1 pixel serves as important supporting aids of successful completion of the specified tasks. It was found that the identification ability of water indices on test fields without existing vegetation is too low. On the other hand, the shielding of the soil surface by vegetation with areas of damaged crops makes it possible to isolate MRLF. The obtained information can be used during the development of the methodology of soil science surveying and planning of largescale soil survey activities

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.

Mapping Pinus spp. Forestry and Land Cover Classes Using High-resolution PlanetScope Satellite Data: Experimenting Images from Different Seasons and Machine Learning Methods

The Remote Sensing and machine learning techniques are cost-effective ways of mapping land use and cover, especially forestry areas. This is essential for the management and planning of such resources. The purpose of this study was to identify which classifier (Random Forest or Support Vector Machine) reach the best accuracy in land use and cover classification and determine which is the best season of year for Pinus spp. forest mapping. PlanetScope multispectral image was used with 3.7 m of spatial resolution, collected over the coastal region of Rio Grande do Sul state. The input variables for the classifiers were the four spectral bands: RGB and NIR, and the NDVI vegetation index. In both classifiers, high accuracy values were obtained, as well as for all seasons of the year. The Random Forest classifier obtained better results in the spring and summer seasons, while in the autumn and winter seasons there was no significant difference between the classifiers for the classification of Pinus spp. forests. The results reached an adequate precision to be used for the management and monitoring of the land use and cover in the municipality of São José do Norte, RS.

Contemporary Dynamics in Vegetation of the Polar and Subpolar Regions of Western Siberia Using Remote Sensing Data

Abstract—Trends in the climatogenic dynamics of phytomass on the sites located along the borders of Western Siberia’s natural zones (the border of frozen and unfrozen bogs, the border of taiga and open boreal woodlands, and the northern boundary of the distribution of woody vegetation) were spotted based on the vegetation index NDVI derived using the sensors of the MODIS-Terra satellite for the period 2000–2020. The assessment of the vegetation dynamics and the factors that determine it was conducted with the use of the mean and maximum NDVI values and the relation of the index with basic climatic variables. A positive trend of NDVI caused by an increase in air temperature and precipitation was revealed in all the studied areas. The phytomass of open boreal larch woodlands grew the most strongly, and the greatest interannual variability was noted there. A correlation analysis conducted on the basis of the NDVI mean values displayed that improvements in vegetation growth conditions in tundra are mainly caused by the impact of the average summer temperature. The increase in phytomass in taiga is mostly connected with the presence of a tree stand that better retains snow cover, which contributes to less freezing of the ground during the cold season.

Remote Sensing Indicators of Spongy Moth (Lymantria dispar L.) Damage to Birch Stands in Western Siberia

The goal of this study is to detect indicators of damage to birch stands in western Siberia by spongy moth (Lymantria dispar L.) using remote sensing methods. The need for such indicators is due to the fact that the size of the study area is about 1 million square kilometers, and ground methods are too laborintensive. It is crucial for these indicators to differentiate the effects of insects from other destructive factors like fires and droughts. During the 2021–2022 fieldwork, we identified 18 areas for trial (intensive damage due to caterpillars) and control. For each area, we obtained seasonal time-series data of vegetation index NDVI mean values within its boundaries. We acquired the data from a Sentinel-2 satellite with a spatial resolution of 10 m. Relative reduction indices of NDVI during the season were introduced for pairs of damaged–control plots. We also considered the effect of foliage regeneration on damaged trees. The obtained indicators demonstrate increased analytical significance in identifying areas affected by pests compared to the simple reduction in vegetative indices.