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In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture. [ABSTRACT FROM AUTHOR]

Windthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of the ecosystem chain, it is essential to rehabilitate the windthrow area as soon as possible. Therefore, it is mandotory to determine the success of the rehabilitation processes. In this study, the rehabilitation process of windthrow that occurred in the Düzce Tatlıdere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite images between 2017 and 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance Index (PSRI), and Normalized Difference Vegetation Index (NDVI) were calculated from Sentinel-2A satellite images, and the most successful index for detecting reforested areas was identified. UAS images were used to create training data, and this data was used to classify Sentinel-2A images with the Random Forest (RF) algorithm. The classification’s accuracy was assessed using the Kappa Coefficient and Overall Accuracy (%). Results showed that NDVI had the lowest accuracy in both years, whereas NDRE succesfully detected windthrow area borders. PSRI was most successful in monitoring rehabilitation progress and detecting reforested areas between 2017 and 2022. This study, he effectiveness and limitations of the NDRE, PSRI and NDVI indices in the rehabilitation process of the windthrow area have been detected, and the most important Sentinel-2A bands were determined based on the results of the RF classification. This study is pioneering in the use of NDRE and PSRI to detect reforested areas post-windthrow. [ABSTRACT FROM AUTHOR]

Utilizing Unmanned Aerial Vehicle (UAV) multispectral technology offers a non-destructive and efficient approach to monitoring plant health and stress responses by analyzing reflectance data across various wavelengths. This study integrates UAV-based multispectral imagery with ground-measured sample data to evaluate the impact of atrazine (ATR) on chlorophyll a, chlorophyll b, carotenoids, and anthocyanins in Red Cos lettuce. The results indicate a significant increase (p < 0.05) in ATR concentration in lettuce with soil application, leading to notable reductions in pigment concentrations. Heatmap analysis reveals that EVI shows the strongest negative correlations with pigment classes (coefficients ranging between −0.75 to −0.85), while NDVI, GNDVI, and BNDVI exhibit the strongest positive correlations with pigments (coefficients > 0.75). These findings highlight the potential of this innovative technique in predicting pigment concentrations and emphasize its importance in monitoring pesticide effects for sustainable agriculture. [ABSTRACT FROM AUTHOR]

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m : n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion. [ABSTRACT FROM AUTHOR]

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002–2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for analyzing the effects of water regime on grapevine canopy status using robotics as an aid for monitoring and mapping. Data from an unmanned aerial vehicle (UAV) and a ground mobile robot are used to obtain multispectral images and multiple vegetation indexes, and the 3D reconstruction of the canopy, respectively. Unlike previous works, sixty vegetation indexes are computed precisely by using the projected area of the vineyard point cloud as a mask. Extensive experimental tests on repeated plots of Pinot gris vines show that the GDVI, PVI, and TGI vegetation indexes are positively correlated with the water potential: GDVI (R2=0.90 and 0.57 for the stem and pre-dawn water potential, respectively), PVI (R2=0.90 and 0.57), TGI (R2=0.87 and 0.77). Furthermore, the canopy volume and the canopy area projected on the ground are impacted by the water status, as well as stem and pre-dawn water potential measurements. The results obtained in this work demonstrate the feasibility of the proposed approach and the potential of robotic technologies, supporting precision viticulture.

This study investigates the efficacy of Synthetic Aperture Radar (SAR)-based vegetation indexes for filling gaps in the optical-driven Leaf Area Index (LAI) time series. The statistical properties of coherent (e.g., interferometric coherence) and incoherent (e.g., backscattered signal) SAR vegetation indexes are systematically studied, including their correlation with LAI measurements and significance for LAI reconstruction. First, the Multi-Output Gaussian Process (MOGP) algorithm is selected, analyzed, and subsequently improved to handle the non-Gaussian distribution of the exploited SAR indexes. Hence, a refined MOGP method incorporating a quantile-transform (QT) operation is proposed. Experiments focus on the Arborea zone in Sardinia, Italy, exploiting one year of optical and radar images from the European Copernicus Sentinel-1/2 sensors. The results prove the usefulness of the refined MOGP model in obtaining LAI time series with reduced uncertainties (R2 = 0.9/0.7 training/validation) and filling gaps in optical-based LAI observations, reconstructing feasible crop dynamic along the season. The study also provides insights into phenological state evolution and implications for future applications of the presented method.

A Parrot Sequoia four-band multispectral camera mounted on a Parrot Disco-Pro Ag drone allowed us to study six vegetation indexes in four lakes within the Tilacancha Private Conservation Area (PCA) in 2021. These lakes are a source of water for consumption for more than 32,000 people in the province of Chachapoyas in the Amazon region of Peru. To obtain the six vegetation indexes (Green Normalized Difference Vegetation Index—GNDVI; Leaf Chlorophyll Index—LCI; Modified Chlorophyll Absorption in Reflective Index—MCARI; Normalized Difference Red Edge—NDRE; Normalized Difference Vegetation Index—NDVI; and Structure Intensive Pigment Index 2S—SIPI2), Pix4DFields 1.8.1 software was used. The sensitivity and distribution of pixel values were compared in histograms and Q–Q plots for each index. Statistical differences were established for each index, and the SIPI2 obtained the highest level of sensitivity concerning the degree of pixel distribution in the ranges shown in the histogram according to the standard deviation; however, the values of all the indexes were not disregarded, because they showed statistical differences between lakes despite their closeness. The family error rate and Tukey-Kramer HSD statistics allowed for establishing statistical differences between pairs of lakes. The six vegetation indexes can be used to detect and analyze the dynamics of biological beings with photosynthetic activity in aquatic ecosystems of the Peruvian Jalca. [ABSTRACT FROM AUTHOR]

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A majority of mangroves are located in the coastal intertidal zone and are subject to tidal periodic inundation. However, the previous vegetation indices used for extracting the spatial distribution of mangroves were not able to effectively extract submerged mangroves, and the applicability of the vegetation indices used on different spatial resolution images obtained from different sensors was not verified. In this study, a new vegetation index, namely the intertidal mangrove identification indices (IMIIs), was proposed, based on GF-2 images of high and low tide levels. Meanwhile, other commonly used vegetation indices were also extracted. All the vegetation indices were used to extract the spatial distribution of mangroves under tidal inundation, and applicability tests of the vegetation indices were conducted on Sentinel-2 images in three different regions. It was found that the IMIIs proposed based on GF-2 images of high and low tide levels can extract submerged mangroves relatively well, and the spatial distribution extraction results of mangroves are better than those of other vegetation indices, with IMII2 outperforming IMII1. At the same time, IMIIs have good applicability in medium resolution Sentinel-2 images, and there are relatively large differences in the extraction results of mangrove spatial distribution among different vegetation indices in areas with significant impact of tidal inundation. Among all vegetation indices, the extraction results of IMIIs are relatively superior. In most cases, multi variables collaborative application can improve the accuracy of mangrove spatial distribution extraction results. Based on the results of this study, it was concluded that the IMIIs proposed in this study can accurately extract the spatial distribution of mangroves inundated by tides from both medium- and high-resolution images, providing accurate basic data for effective management and scientific protection of mangrove resources. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates the spatial analysis of morphological and chemical changes and how reflectance hyperspectroscopy and fluorescence kinetics spectroscopy can enhance our understanding of biophysical, biochemical, and photochemical changes in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analysis included pigment profiling, hyperspectral curves, chlorophyll a fluorescence induction kinetics, and multivariate analyses associated with 23 JIP test parameters and 34 vegetation indexes. The results show that the analysis of chemical composition combined with vegetation indexes, such as PRI, PSSRc, ARI1, RARS, and SIPI, are highly correlated with pigment concentration and photochemical components of photosystems in leaves. Furthermore, decreased damage to energy transfer in the electron transport chain is associated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds linked with specific wavelengths. Our results reveal the potential of optical spectroscopy techniques and multivariate data analyses to enhance the management and monitoring of the leaf color status of plants. This is the first study and report on the monitoring of nonuniform leaves, particularly in the alteration of photosystem changes in variegated leaves together with high-throughput analyses. The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques. [ABSTRACT FROM AUTHOR]

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p

Horizontal structure of natural plant communities attracted the attention of researchers for a long time, while the problem of horizontal structure of urban park plantations was not studied sufficiently. Species richness of different tiers of park plantation in the large industrial city of Dnipro (Ukraine) was revealed in this study. Also features of variation in the structure of plant communities at different spatial levels were revealed, the influence of park plantation canopy on the understory and herbaceous layer of the park. There were 30 plant species in the tree layer of the park plantation. The most common species were Robinia pseudoacacia L., Acer platanoides L., A. negundo L., Gleditsia triacanthos L., Aesculus hippocastanum L., Populus carolinensis Moench. The variance-to-mean ratio revealed that 13 tree species were randomly distributed throughout the park, and 14 species were aggregated. The number of occurrences of a given tree species per site and variance-to-mean ratio were positively correlated. The numerous tree species showed a tendency of aggregated distribution within the park. Sixteen plant species were found in the understory. Among them, the most abundant species were Acer platanoides L., A. negundo L., A. pseudoplatanus L., Sambucus nigra L., Robinia pseudoacacia L. Eight species were found to be randomly distributed over the park area, and eight species showed an aggregate distribution. The number of species encountered in the understory and variance-to-mean ratio were positively correlated. In the herbaceous stand, 99 plant species were found, of which Chelidonium majus L., Viola odorata L., Impatiens parviflora DC., Parthenocissus quinquefolia (L.) Planch., Geum urbanum L. predominated. The variance-to-mean ratio of all species was significantly less than unity, indicating regular spatial distribution. The values of alpha- and gamma-diversity of the plant community in separate layers are very different. The highest gamma diversity was found for the herbaceous stand, while the diversity of the tree stand and understory was significantly lower. Alpha biodiversity of the tree stand and the understory did not practically differ. Beta diversity values between the layers are very close, and beta diversity is practically equal for tree stand and herbaceous layer. Thus, we can assume that the mechanisms of species turnover for the plant communities of different layers are determined by the common causes. The spatial broad-scale component was able to explain 8.2% of community variation, the medium-scale component was able to explain 4.2% of community variation, and the fine-scale component was able to explain 0.7% of community variation. The understory is the most sensitive to the environmental factors, the herbaceous stand is somewhat less sensitive, and the tree stand is the least sensitive to the environmental factors. The environmental factors in this study are represented by a set of variables. The spatial variation of the stand is predominantly influenced by the factors of trophicity and moisture of the edaphotope. These same factors also act on the herbaceous stand and understory, but along with them are included the environmental variables, which are determined by the architectonics of the crown space and thus the light regime, which is regulated by the tree stand. It is important to note that the variation of the communities of the different layers of the park plantation is subject to spatial patterns. The herbaceous and understory variation is more spatially structured than the tree stand variation. The spatial patterns can arise as a result of the influence of spatially structured environmental factors and as a result of factors of a neutral nature. The latter aspect of variation is best described by the pure spatial component of community variation.

In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors. [ABSTRACT FROM AUTHOR]

Parks perform a wide range of ecosystem services in urban environments. The functional importance of parks depends on the composition and structure of the tree stand and the specific influence on soil and microclimatic conditions. The article reveals the dependence of soil and microclimatic properties on the structure of the crown space of a park stand. Spectral indices were also shown to be applicable for predicting the spatial variability of soil and climatic properties and indicators of crown space. Soil properties (temperature, moisture, and electrical conductivity in the 5–7 cm layer) and microclimatic parameters (light exposure, air temperature, and atmospheric humidity) were measured in the park plantation using a quasi-regular grid. The canopy structure and gap light transmission indices were extracted from the true-colour fisheye photographs. Thirty species of trees and shrubs were detected in the stand and understory. Robinia pseudoacacia L. was found most frequently (24.5% of all tree records). Acer negundo L. and A. platanoides L. were also frequent (12.4% and 15.5%, respectively). The first four principal components, whose eigenvalues exceeded unity, were extracted by the principal components analysis of the variability of ecological properties and vegetation indices. The principal component 1 explained 50.5% of the variation of the traits and positively correlated with the spectral vegetation indices. The principal component 1 reflected the variability of tree cover densities due to the edaphic trophicity. The principal component 2 described 13% of the variation in the feature space. This component correlated positively with the spectral indices. The principal component 2 was interpreted as a trend of vegetation cover variability induced by moisture variation. The principal component 3 described 8.6% of trait variation. It was most strongly correlated with the atmospheric humidity. An increase in atmospheric humidity was associated with an increase in the soil moisture and electrical conductivity and a decrease in the soil and atmospheric temperature. The principal component 4 described 7.5 % of the variation of traits. An increase in the values of principal component 4 was associated with an increase in the soil moisture and electrical conductivity and atmospheric moisture and was associated with a decrease in the soil and atmospheric temperature. The combinations of the trophotope and hygrotope create the optimal conditions for specific tree species, which is a condition for achieving the maximization of ecosystem services. The mineral nutrition conditions of plants and soil moisture exhibit spatial patterns that allow them to be considered in the design and management of park plantations. The ecological indices measured in the field were shown to be predicted using the vegetation indices. Multiple regression models were able to explain 11–61% of indicator variation. The regression relationships between markers of soil and microclimatic conditions and vegetation predictors are important for monitoring the condition of park plantations and evaluating the performance of park plantation management tools.

An urban heat island phenomenon has increased in the last decades due to rapid urbanization, resulting in a significant impact on local climate. In this study, remote sensing data was used to analyze Spatiotemporal patterns of Urban Heat Island Intensity (UHII) over a 20 years period in a semi-arid climate area. The relationship between the Land Surface Temperature (LST), vegetation and Land Cover Types (LCTs) were examined. The relation between the UHII and its driving factors (different LCTs and meteorological conditions) was analyzed. Analysis of 8-day daytime and nighttime LST data acquired from MODerate-resolution Imaging Spectroradiometer (MODIS) shows that Amman has a significant UHII in both daytime and night time. The results show a negative correlation between the LST and vegetation indicators and between the UHII and the wind speed average, indicating a positive correlation between the UHII and temperature. Vegetation has been proven to significantly reduce LST, mainly in the daytime, due to its cooling effect that results from the transpiration process and shadow effect.

The paper deals with the application of the remote sensing for drought monitoring. A way of their application in practice are shown through example for monitoring of drought that is done for three different area in Bulgaria with different pattern, for the same time (July) and for eleven randomly selected years from 1994 to 2009. For the purpose, NDVI, LAI and FPAR vegetation indexes are used. According to Intergovernmental Panel on Climate Change (IPCC) drought and floods are the most severity events threatening the country, so attention is paid on drought monitoring. [ABSTRACT FROM AUTHOR]

An urban heat island phenomenon has increased in the last decades due to rapid urbanization, resulting in a significant impact on local climate. In this study, remote sensing data was used to analyze Spatiotemporal patterns of Urban Heat Island Intensity (UHII) over a 20 years period in a semi-arid climate area. The relationship between the Land Surface Temperature (LST), vegetation and Land Cover Types (LCTs) were examined. The relation between the UHII and its driving factors (different LCTs and meteorological conditions) was analyzed. Analysis of 8-day daytime and nighttime LST data acquired from MODerate-resolution Imaging Spectroradiometer (MODIS) shows that Amman has a significant UHII in both daytime and night time. The results show a negative correlation between the LST and vegetation indicators and between the UHII and the wind speed average, indicating a positive correlation between the UHII and temperature. Vegetation has been proven to significantly reduce LST, mainly in the daytime, due to its cooling effect that results from the transpiration process and shadow effect. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes. [ABSTRACT FROM AUTHOR]

Aims: Shelter forest communities in the Yellow River Delta (YRD) have been disturbed, withering, or even dying, showing ecosystem degradation. It is challenging to systematically analyze the relationship between forest dynamics and factors since multiple simultaneous drivers of degradation may interact and have confounding effects in different phenophases. Methods: The vegetation indexes (VI), groundwater, soil water content (SWC), and electrical conductivity (EC) dynamics of the 0–260 cm soil layers were investigated in 3 health levels of shelter forests from May to October 2015, as well as the root distribution and topography. The Geodetector model was used to detect the factors affecting the forests in different phenophases. Results: The kernel normalized difference vegetation index (kNDVI), normalized difference infrared index (NDII), and leaf area index (LAI) can monitor the growing season of shelter forests. SWC in the 60–220 cm soil layers had better explanatory power for NDII ( Q ¯ =0.74) and kNDVI ( Q ¯ =0.76) in the leaf expansion, full leaf and discoloration periods. LAI was sensitive to SWC in the 60–180 cm soil layers ( Q ¯ =0.85) during the fruit development and ripening period. The interaction of topography, soil texture, and groundwater influenced the dynamics and distribution of SWC ( Q ¯ =0.40–0.98) and EC ( Q ¯ =0.31–0.90). Conclusions: SWC was the leading cause of degradation of shelter forests in the YRD during the growing season, and EC synergistically enhanced forest degradation with SWC in leaf expansion and discoloration periods. Differences in topography, soil texture and groundwater contribute to the different distribution and dynamics of SWC and EC. [ABSTRACT FROM AUTHOR]

The use of remote sensing in natural resource management is an easily accessible input for obtaining detailed information on the ground and landscape. There is a wide range of procedures to analyze the forest canopy through satellite images. The purpose of this work is to obtain a map of forest opening with remote sensing by relating several vegetation indices, Kauth-Thomas transformation and texture filters, to a Landsat 8OLI image. A factor analysis was made to evaluate the contribution of these variable to identify the opening of the forest cover, yielding a σ2 = 76%. The results show that the Modified Soil Adjusted Vegetation Index (MSAVI), Soil Adjusted Vegetation Index (SAVI), and brightness factor have the best correlation (0.225–0.216 component coefficient). The resulting model was reclassified into five categories of forest opening and associated with land use data from the National Institute of Statistics and Geography (INEGI-México). Thus, 95% of human settlements have a canopy opening between medium and very high, the crops areas 72%, and the low deciduous forest with secondary shrub vegetation 100% of the opening. Coniferous and mixed forests have a low to very low canopy opening 46% and 55%, respectively of their surface. The forests with secondary vegetation, both shrub and arboreal, present greater openness than the same forests in the primary state. Verification of the spatial representation data of canopy opening was made by comparing 94 hemispheric photographs with 94 sites located in open areas obtaining an r = 0.57. This work offers a simple and straightforward methodology, easily replicable in different types of vegetation using free satellite imagery. Hence, it is a helpful tool for decision-makers when considering the general status of conservation of forest systems and their spatial distribution.

It is imperative to rapidly and precisely acquire crop lodging area and severity for disaster prevention and yield prediction. However, estimation of crop lodging area at a large scale remains challenging due to the relatively low sensitivity of remote sensing signal to the lodging variation, limited availability of remote sensing images, and lodging statistical data. This study proposes a new method for lodging area estimation based on the optimal grid cell of Sentinel-2 and crop lodging percentage, overcoming the limitation of traditional pixel-based mapping approaches that fail to obtain quantitative lodging information. Basing the spatial aggregation method, we analyzed the optimal grid size of Sentinel-2 data for lodging percentage estimation. Then we investigated the spectral response for different lodging percentage levels and analyzed the potential of lodging percentage estimation for Sentinel-2 metrics (including selected spectral bands and their derived vegetation indexes (VIs)). A quantitative model was established between the training set and the Sentinel-2 metrics using the random forest (RF) algorithm. Finally, around 1462.62 ha fields from six counties or districts in Heilongjiang province in China were estimated for lodging percentage. Results indicate that the proposed method can estimate the crop lodging percentage on the testing set with an R2 and RMSE of 0.64 and 25.24, respectively, which can explain around 95 % spatial variation of lodging crop. Moreover, the overall magnitude of reflectance increased with the increase in lodging percentage. Among all Sentinel-2 optimal metrics, the Green, SWIR1, and Red edge 1 bands are the most crucial indicators for lodging percentage estimation. Our results on lodging percentage estimation in the study area indicate that there is more lodging maize in the Meilisidawoerzu district than in other areas. Although typhoons passed over Fuyu and Lindian counties, the lodging percentage in these areas is relatively low. The lodging percentage map has great value in agriculture management and insurance claim.

Human intervention on vegetation cover has always had a negative impact on the environment, directly affecting the quality of life in urban areas. Therefore, this study aimed to develop a methodology for the construction of an urban environmental quality indicator (UEQI) that could reflect the environmental quality of urban areas considering the vegetation conditions to which the resident population is exposed. For this, the method sought to integrate data on demographic density (Dd), leaf area index (LAI), normalized difference vegetation index (NDVI), and surface temperature (St). The Mamdani fuzzy inference system was used to generate a rule base containing 108 variations and a defuzzed output with five condition classes, ranging from very bad to excellent. The results showed that the studied area is characterized by a very bad to good UEQI, with most neighbourhoods having poor conditions (64.51%) and only two with good conditions. It was found that in general the studied area has unsatisfactory environmental quality, showing the need for initiatives aimed at urban afforestation in order to improve the quality of life for the studied population. It can be concluded that UEQI proved to be an efficient tool to identify priority areas for the planning and management of vegetation cover in urbanized areas, enabling the improvement of people's living conditions.

The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants' phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson's, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem's phenology and the influence on the Caatinga's natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall. [ABSTRACT FROM AUTHOR]

Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000–2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R2 = 0.87) and monthly (polynomial correlation, R2 = 0.976) time scales by comparing with VIs. During July–December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011–2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August–October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring. [ABSTRACT FROM AUTHOR]

Remote monitoring and utilization of digital technologies is essential for the application of the precision farming approach, which contributes significantly to the improved quality of agricultural products. The paper compares the data for six vegetation indices when observing the sunflower vegetation in South Dobrudzha in 2021. Images with RGB and digital NIR camera were obtained via a remotely piloted quadcopter. The flight plan specifies speed 8 m/s, altitude 100 m and shooting overlapping images of 80%. Six vegetation indices: NDVI, EVI2, SAVI, CVI, MGVRI and MPRI were calculated from the images obtained during the flight. The calculation of the indices takes into account the intensity of solar radiation and the parameters of the meteorological situation at the time of shooting. The findings obtained reveal a stable trend of change of the vegetation indices, thus, establishing accurate and reliable results as for the monitoring of agricultural areas with unmanned aerial vehicles.

ABSTRACT Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a 'graph–spectrum' fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the 'graph–spectrum' information formed by eight parameters, including the normalized vegetation index NDVI (R578, R490), the difference vegetation index DVI (R830, R778), MEA490, MEA778, VAR490, VAR578, VAR778, and HOM578 as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring. [ABSTRACT FROM AUTHOR]

One of the most efficient approaches to provide a current and objective assessment of forest cover over large areas is remote sensing data combining with an empirical models obtained from field ground research. For a detailed study of the phytomass of plantations by GIS technologies, calculations of spectral characteristics and biophysical parameters obtained as a result of decoding satellite images of monitoring objects are usually performed. The object of study – pine stands of the ravine Steppe of Ukraine. The subject of research – bioproductivity of aboveground phytomass components of pine stands. The aim of the study to develop the models for pine stands phytomass assessing at the ravine Steppe of Ukraine by remote sensing. The evaluation of aboveground phytomass pine forests was carried out by combining terrestrial and remote sensing methods. In order to estimate the aboveground biomass in the pine forests of the ravine Steppe based on the Sentinel-2 satellite imagery data for each pixel of images containing temporary sample plots (TSPs), the following indicators were calculated: NDVI, LAI, FAPAR, FCOVER, CAB and CW. In accordance with statistical analysis the distribution of spectral indices in most cases corresponds to normal, given the lower values of asymmetry and excess, compared with the critical values. Correlation analysis revealed that the spectral characteristics obtained from satellite images had a stronger correlation with the aboveground phytomass of trunks and total phytomass, compared with the crown phytomass of pine wood stands. With the help of the obtained geoinformation data, models of aboveground phytomass dependence on vegetation and biophysical parameters were designed with the help of which about 70 % of cases were subject to description. The NDVI, LAI, FARAP, TVI, FCOVER indicators were used as input arguments. The equation of the dependence of the aboveground pine phytomass on the NDVI index is the most significant statistically ftom the designed equations. The lowest approximation accuracy is observed when the LAI index is included in the equation. The comparison of the actual and simulated values of aboveground phytomass of pine stands obtained as a result of ground research revealed insignificant their deviation, which testifies to a rather accurate description of the empirical data model. The most intensive accumulation of pine phytomass at the amount from 150 to 250 tˑha-1 is observed at NDVI values in the range from 0.4 to 0.5. Based on the obtained data, the values of Scots pine aboveground phytomass were transformed into a forest area structure of the Dnieper state forestry of ravine Steppe (on the example of Obukhiv forestry) with the formation of a spatial distribution map of aboveground phytomass forestry. Values of aboveground phytomass pine plantations of Obukhiv forestry was estimated in the range from 75 to 310 tˑha-1. At the same time, most of the Scots pine plantations territory (61 % of the area) has a phytomass in the range from 100 to 200 tˑha-1. It is estimated that the total value of aboveground phytomass of pines in Obukhov forestry, calculated on the basis of spatial data processing, is 195782.40 tons, while the obtained biomass, determined by ground estimation 210861.95 tons, which is 7.2% higher, compared with remote sensing data. The obtained results demonstrate the prospects of the proposed method for the aboveground phytomass of pine plantations assessment at the regional level as an effective method of operational monitoring and management of forest resources in the Steppe zone.

Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.

This paper considers remote sensing estimation of forest renewal in Suzemskoye forestry, Bryansk oblast. The objective was to study the possibility of assessing forest renewal on clear fellings through planting forest crops using the time series of vegetation indexes (NDVI and SWVI). The time series consists of cloud-free summer composite images based on Landsat data from 2002–2015. We used 2003 forest management plans and taxational descriptions of Kholmchenskoye forestry division which is part of Suzemskoye forestry as reference data. As a result, a linear relationship was obtained between the values of vegetation indexes, their time dynamics, and forest taxational characteristics (forest type, species composition, and age group). This enabled 80.1%-accurate detection of the forests regeneration if Steklyanskoye, Krasnoslobodskoye and Kokorevskoye forestry divisions, which are part of Suzemskoye forestry of Bryansk oblast. These forests were renewed after clear fellings. The results of this study can be used in practical forest management where the issue of monitoring reforestation and afforestation activities is highly relevant.

The inadequate land cover data have predominantly limited the significance and impacts of land cover. Even though remote sensing or satellite imaging has been deployed in mapping several temporal and spatial scales, but its full effort was not yet recognized. Hence, this paper intends to implement a novel land cover classification model through optimal deep learning architecture. Here, it includes three main phases namely, (i) Segmentation (ii) Feature Extraction and (iii) Classification. Initially, the land cover image is segmented, and subjected for feature extraction process. For feature extraction, Vegetation Indexes (VI), such as Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Kauth–Thomas Tasseled Cap, are extracted. Furthermore, these features are classified using Neural Network (NN) and Convolutional Neural Network (CNN). In both the classifiers, the number of hidden neuron is optimized by modified Water wave Optimization (WWO) called Modified Propagation Update based WWO (MPU-WWO). The optimization of hidden neurons is done in such a way that the classification accuracy should be high, which is taken as the main objective. [ABSTRACT FROM AUTHOR]

ABSTRACT: Correlation between proximal sensing techniques and laboratory results of qualitative variables plus agronomic attributes was evaluated of a 3,0 ha vineyard in the county of Muitos Capões, Northeast of Rio Grande do Sul State, Brazil, in Vitis vinifera L. at 2017/2018 harvest, aiming to evaluate the replacement of conventional laboratory analysis in viticulture by Vegetation Indexes, at situations were laboratory access are unavailable. Based on bibliographic research, looking for vegetative indexes developed or used for canopy reflectance analysis on grapevines and whose working bands were within the spectral range provided by the equipment used, a total of 17 viable candidates were obtained. These chosen vegetation indices were correlated, through Pearson (5%), with agronomic soil attributes (apparent electrical conductivity, clay, pH in H2O, phosphorus, potassium, organic matter, aluminum, calcium, magnesium, effective CTC, CTC at pH 7.0, zinc, copper, sulfur and boron) for depths 0 -20 cm and 20-40 cm, and plant tissue (Nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, zinc, iron, manganese and boron) , in addition to some key oenological and phytotechnical parameters for the quantification of wine production and quality. One hundred and thirty ninesignificant correlations were obtained from this cross, with 36 moderate coefficients between 19 parameter variables versus 12 of the indexes. We concluded that in cases where access or availability of laboratory analyzes is difficult or impracticable, the use of vegetation indices is possible if the correlation coefficients reach, at least, the moderate magnitude, serving as a support to decision making until the lack analytical structure to be remedied.

The Colombian Pacific has extensive areas in mangrove forests (MF), which is a strategic ecosystem of great environmental and socioeconomic for climate change mitigation. This work aimed to perform spectral characterization and monitoring of 66.59 km2 for four MF densities in Bajo Baudó (Colombia), using three Landsat images (1998, 2014 and 2017), combinations of spectral bands and three vegetation indices (VI) (Normalized Difference Vegetation Index-NDVI, Soil Adjusted Vegetation Index-SAVI and the Combined Mangrove Recognition Index-CMRI). The results showed that the best combination of spectral bands for visual identification of MF corresponded to infrared color (NIR, Red, Green) and false-color composite 1 (NIR, SWIR, Red). The spectral sign of MFs had different behaviors in four densities under the conditions of high tide and low tide. During the 19 years analyzed, there was a difference of up to 17.9% in the average reflectance value in MF. Similarly, the values of VI were proportional to the densities of MF, but their value was reduced by tidal effects at the time of capturing the images; the largest increases in VI were recorded over the coastal area of land-water transition, where there is a strong interaction with the tidal condition. This research contributes to the spatial characterization and monitoring of MF with remote sensors and the spectral study of this important ecosystem in Colombia. [ABSTRACT FROM AUTHOR]

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This paper submitted the method of remote sensed estimate the forest artificial regeneration (tree planting). The objective was to designate the moment of transition from clear cuts to forested lands. The studies took place in Suzemskoe forestry in Bryansk region. We used a forest taxation data and annual cloud-free Landsat composite images (2002 – 2015 years). Vegetation indexes (NDVI and SWVI) were calculated by the time series of cloud-free composite images. The database of the ages of transition of clear cuts to artificial regeneration was compiled based on taxation data from forest inventory of the forestries (2003 y.). Cumulative values of vegetation indexes for the whole study period were calculated from the time series for the whole period. Examining the NDVI-SWVI indexes of the artificial regeneration sites allowed finding the linear relationship, the incline of the linear regression and thresholds of clear cuts transitioning to forested lands. The method was tested for Steklyanskoe, Krasnoslobodskoe and Kokorevskoe forestries. The correct classification of the clear cuts transitioned to the regrowth category reached 80.1%.

The normalised-difference vegetative index (NDVI) seasonal dynamics of 15 types of vegetation communities in dry 2010 and in 2016 with a favourable weather conditions was studied in the Pre-Urals Steppe site where the Programme on establishing a semi-free population of the Przewalski's horse Equus ferus przewalskii in Orenburg State Nature Reserve started. The NDVI seasonal dynamics in a year with normal weather conditions includes four main phases including the 1st phase of spring NDVI increasing, the 2nd phase of the productivity maximisation of the dominant vegetation, the 3rd phase of the summer progressive decrease in photosynthetic activity in response to the heat and lack of moisture, the 4th phase of low photosynthetic activity lasting from midsummer to the late autumn. The dependence of NDVI on vegetation productivity during its maximum development in the Pre-Urals Steppe in 2016 was described by the linear regression equation. Vegetation productivity on 20 June 2010 and that on 20 June 2016, calculated by the linear regression equation, were compared. The comparison revealed that in 2010, the decline in productivity during vegetation maximum development varied between 19% and 65% depending on the type of plant community. The following plant communities were the least resilient to drought: communities of fallow lands on the locations of previous true steppes, communities which appeared under the influence of overgrazing in the locations of the previous true steppes and the communities of meadow-steppes. Good moistening resulted in a slight decline in productivity communities located in topographical depressions. Productivity of plant communities with early-spring growth of dominating species declined to a lesser degree as the communities are located on a well-warmed surface. Some biological features of the Caragana frutex + Spiraea crenata vegetation type also result in the less decline in productivity. The NDVI coefficient does not accurately reflect productivity at the end of the growing season. Therefore, patterns of declines in productivity of similar vegetation types during the drought in Trans-Urals region in 1998 were used to project the autumn decline in vegetation productivity during the drought in the Pre-Urals Steppe. Based on these findings, the average vegetation productivity in the Pre-Urals Steppe in autumn in dry 2010 were four to five times lower than that during the maximum plant development in 2016 which had favourable weather conditions. As the height of vegetation considerably reduces during drought, the availability of pasture forage may sharply drop in winter, especially if a deep snow cover accumulates and unevenly distributes according to terrain relief. In the Pre-Urals Steppe, only some steppe communities, covering about 20% of the area, are the most accessible to supply Przewalski's horses with dry pasture forage in winter. Projecting the winter supply of pasture forage after a severe drought should be based on the assumption that the forage availability may reduce by 8 to 10 times as compared with calculated summer productivity in a year with favourable weather conditions. In this regard, there is a need to make stocks of hay for feeding horses in winter after a drought. For potentially repeated droughts the sufficient amount of hay to feed Przewalski's horses in the Pre-Urals Steppe over two winter seasons should be stocked in years with normal precipitation as it will be difficult to make the stock in a dry year. For a final assessment of the accessibility of winter pasture forage stock for Przewalski's horses in the Pre-Urals Steppe, some additional field studies of productivity of the main vegetation types at the end of the growing season using the harvest method are necessary as well as an analysis of the distribution and height of snow cover on the territory in years with high winter precipitation.

Abstract Brazilian fruit culture has a great influence on the social and economic sector in the most diverse regions of the country, generating employment and income in the exercise of its activities. As it is an activity carried out most often in a manual and conventional manner, fruit culture has a great potential for technological growth, especially when adopting the concepts applied by precision agriculture on the crops of grains, fibers and energy, creating a new segment, Precision Fruit Farming. The present work aims to carry out a bibliographic review on the main trends that have emerged in the last five years on Precision Fruit growing, highlighting its future perspectives and the history of technological evolution. 83 articles were analyzed, classified in different perennial cultures and applications, such as machine learning, remote sensing, robotics, using UAV to obtain different vegetation indexes, among others.

Objective: To evaluate the effect of nitrogen fertilization on the growth and crude protein content of five perennial pastures of Ecuador. Materials and Methods: To evaluate the effect of nitrogen fertilization on the growth and crude protein content of the species Lolium perenne L. var. Alto, Lolium perenne x Lolium multiflorum var. Shogun, Festuca arundinacea Schreb var. Alta Barolex, Dactylis glomerata L. var. Quick Draw and Festuca arundinacea x Lolium multiflorum var. Premium, six progressive doses of nitrogen were applied (0, 70, 140, 210, 280 and 350 kg N ha-1 year-1). A split-plot design was used, where the species was placed in the large plot, and the nitrogen doses, in the small ones, with three repetitions. The variables biomass production, crude protein and normalized differential vegetation index, were evaluated. The data were analyzed through the statistical program INFOSTAT®. Results: The biomass production indicated statistically significant differences (p < 0,05) for the studied factors species and nitrogen doses, but not for the interaction of such factors. D. glomerata and F. arundinacea showed the lowest biomass production values (2 457 and 2 490 kg DM ha-1), compared with L. perenne, which had a moderate yield. F. arundinacea x L. multiflorum and L. perenne x L. multiflorum reached the highest biomass values (3 407 and 3 364 kg DM ha-1). The crude protein content also increased with the nitrogen doses. In turn, the normalized differential vegetation index increased, also according to a quadratic model, with a different response among species. Conclusions: All the species responded positively to the nitrogen application doses. It was determined that there is increase in the biomass production, crude protein content and normalized differential vegetation index. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Agricultura Irrigada - RBAI is the property of Revista Brasileira de Agricultura Irrigada - RBAI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several environmental impacts are resulting from the process of anthropization and climate variability that have caused degradation of biomes and humid environments. Thus, the objective of this study was to evaluate the effect of the anthropic process and the variation of climatic conditions on the dynamics of the marsh vegetation in the Pandeiros River preservation area in the north of Minas Gerais, Brazil. The Enhanced Vegetation Index (EVI) of product MOD13Q1 and the gross primary productivity (GPP) of product MOD17A2 of the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for the period from 2001 to 2017 were used in this process. Rain and air temperature data were obtained from the Conventional Weather Station of Januária-MG. The time series were submitted to the nonparametric statistical test of Mann-Kendall. The process of anthropization of the swamp area in the environmental protection area of the Pandeiros River/MG (EPA) showed a pattern of expansion of vegetation cover associated with the reduction of the water table, a phenomenon resulting from the silting process and reduction of the incidence of rain in this region, which contributes to the reduction of ecosystem services. Thus, understanding the influence of climatic variables on the dynamics of vegetation in humid environments, such as the EPA swamp area on the Pandeiros River, is essential for the preservation and recovery of these ecosystems and for the implementation of public policies for preservation and conservation. [ABSTRACT FROM AUTHOR]

• Sentinel-2 satellite imagery accurately estimates spinach's Above Ground Biomass (AGB). • Canopy parameters are estimated more accurately than leaf-level parameters. • Linear regression with MCARI provides precise AGB estimation with 9.48 % nRMSE. • Field-scale validation demonstrating the utility of satellite imagery for spinach management. The estimation of biophysical and biochemical parameters using Sentinel-2 satellite imagery provides crucial information to support agronomic management and logistics for the processing industry, yet this approach has been poorly explored for vegetable crops, such as spinach (Spinacia oleracea L.). For this reason, Sentinel-2 satellite images were used to estimate biophysical and biochemical parameters of open field spinach crops (Above Ground Biomass (AGB), Canopy Nitrogen Content (CNC), Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), Leaf Dry Matter Content (LDMC), Leaf Nitrogen Content (LNC), Leaf Nitrate Content (LNO 3 − C) and Leaf Chlorophyll Content (LCC)). Spinach samples were collected in northern Italy in two different growing seasons (2020 – 2021) to measure biophysical and biochemical parameters which were linearly regressed via linear regression k-fold to five vegetation indices retrieved from Sentinel-2 images (MCARI, NDRE, NDVI, NDWI and SR). The AGB estimation models were also validated at field scale using historical data, totally independent from those used for the model calibration, collected by the processing industry between 2018 and 2021. Overall, canopy-level parameters (AGB, LAI, CNC, CCC) were estimated more accurately than leaf-level parameters (LDMC, LNC, LNO 3 − C , LCC). The highest accuracy was observed for the estimation of the canopy-scale parameter AGB (nRMSE = 9.48 %, R 2 = 0.87) using MCARI, while the lowest accuracy was observed for the estimation of the leaf-scale parameter LCC (nRMSE = 29.24 %, R 2 = 0.01) using NDWI. At field level, the validation of the AGB estimation models showed the highest performance using SR (nRMSE = 19.69 %, R 2 = 0.21). This work demonstrates that using Sentinel-2 satellite images, it is feasible to estimate biophysical and biochemical parameters useful for monitoring the health of spinach crops for both agronomic management and the industrial supply chain. [ABSTRACT FROM AUTHOR]