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Monitoring Vegetation Systems in the Great Plains with ERTS

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... Bit 7 and Bit 6 are the aerosol level (01 represents low level and 10 represents medium level); Bit 5 is water; Bit 4 is snow or ice; Bit 3 is cloud shadow; Bit 2 is adjacent to cloud shadow; Bit 1 is cloud; and Bit 0 is cirrus. The Landsat 8 OLI, HLSS30, and HLSL30 images were converted into the following spectral vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI) [41]; Green Normalized Difference Vegetation Index (GNDVI) [42]; Normalized Water Difference Index (NDWI) [43]; and Soil-Adjusted Vegetation Index (SAVI) [44] ( Table 3). The NDVI is the most traditional vegetation index and presents a high correlation with photosyntheticactivity-related parameters such as the leaf area index and leaf chlorophyll [41]. ...
... The Landsat 8 OLI, HLSS30, and HLSL30 images were converted into the following spectral vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI) [41]; Green Normalized Difference Vegetation Index (GNDVI) [42]; Normalized Water Difference Index (NDWI) [43]; and Soil-Adjusted Vegetation Index (SAVI) [44] ( Table 3). The NDVI is the most traditional vegetation index and presents a high correlation with photosyntheticactivity-related parameters such as the leaf area index and leaf chlorophyll [41]. However, some plant phenological phases related to changes in leaf pigments, water content, and crop residues may not be entirely analyzed using only NDVI, especially in regions with complex crop cultivation patterns [45]. ...
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The Brazilian Savanna presents a complex agricultural dynamic and cloud cover issues; therefore, there is a need for new strategies for more detailed agricultural monitoring. Using a hierarchical classification system, we explored the Harmonized Landsat Sentinel-2 (HLS) dataset to detect soybean in western Bahia, Brazil. Multispectral bands (MS) and vegetation indices (VIs) from October 2021 to March 2022 were used as variables to feed Random Forest models, and the performances of the complete HLS time-series, HLSS30 (harmonized Sentinel), HLSL30 (harmonized Landsat), and Landsat 8 OLI (L8) were compared. At Level 1 (agricultural areas × native vegetation), HLS, HLSS30, and L8 produced identical models using MS + VIs, with 0.959 overall accuracies (OA) and Kappa of 0.917. At Level 2 (annual crops × perennial crops × pasturelands), HLS and L8 achieved an OA of 0.935 and Kappa > 0.89 using only VIs. At Level 3 (soybean × other annual crops), the HLS MS + VIs model achieved the best performance, with OA of 0.913 and Kappa of 0.808. Our results demonstrated the potential of the new HLS dataset for medium-resolution mapping initiatives at the crop level, which can impact decision-making processes involving large-scale soybean production and agricultural sustainability.
... Then, all tiles of S2 and L8 merged for every month separately, and subset to study area. Reflectance values were then used to calculate NDVI [29], EVI [30], NDWI1 [31] from SWIR 1, and NDWI2 from SWIR 2. In the end, we have obtained monthly temporal profiles of NDVI, EVI, NDWI1, and NDWI2 as input data for ML classifiers. ...
... Four spectral vegetation indices, NDVI [29], EVI [30], NDWI1 [31] from SWIR 1, and NDWI2 from SWIR 2 were calculated using the surface reflectance values. These indices were formulated by using the following equations: ...
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Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.
... The Near-infrared wavelengths are highly reflected by the spongy mesophyll structure. Remote sensing image processing exploits these features from spectral indices including Normalized Difference Vegetation Index (NDVI) (Rouse Jr et al. 1974), Normalized Difference Red Edge Index (NDRE) (Gitelson, Kaufman, and Merzlyak 1996), Green Normalized Difference Vegetation Index (GNDVI) (Gitelson, Kaufman, and Merzlyak 1996), Ratio Vegetation Index (RVI) (Tucker 1979), and Green-red Vegetation Index (GRVI) (Tucker 1979). ...
... In this work, several VIs were used to measure their correlations with the estimation of wheat biomass. The chosen VIs calculated from UAV-based images have been widely used to evaluate crop yields, including NDVI (Rouse Jr et al. 1974), NDRE (Gitelson and Merzlyak 1997), GNDVI (Gitelson, Kaufman, and Merzlyak 1996), RVI (Tucker 1979) and GRVI (Tucker 1979) (Table 2). There are hundreds of multispectral and hyperspectral optical indices available for remote sensing of vegetation applications (Henrich, Götze, and Krauss 2012). ...
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Remote biomass estimation can benefit agricultural practices in several ways, especially larger areas since it does not require local measurements. The advances of the last few decades in machine learning techniques have created new possibilities for estimating aboveground biomass. A pipeline was established from image acquisition to modelling shoot biomass of two wheat cultivars used in Southern Brazil (TBIO Toruk and BRS Parrudo). A UAV was used to acquire multispectral images with high spatial resolution to calculate vegetation indices (VIs). These VIs along with machine learning approaches are used to model the measured biomass of crops in different growth phases. To correlate the wheat images with measured shoot dry biomass, the following regression models were investigated: random forest, support vector regression, and artificial neural networks. An experiment was designed and conducted at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul (EEA/UFRGS) to assess wheat growth. Variability in crop growth was created for all test areas by varying nitrogen availability. To determine shoot biomass, plants were sampled at three different crop growth stages: V6 (stage of six fully developed leaves), three nodes, and flowering. Our results indicate the importance of the radiometric calibration used. Also, the features extracted from images, such as the VIs combined with machine learning models can be used in precision agriculture for predicting the spatial variability of shoot biomass. The best model for Brazilian wheat cultivars was an artificial neural network with R2 of 0.90, RMSE of 0.83t/ha, and nRMSE of 8.95%. We also found a strong correlation between ground NDVI with image-based NDVI, with an R2 of 0.84.
... The NDVI and NDMI were determined using Equations (1) and (2). The NDVI is a vegetation index used to estimate mangrove greenness associated with green and healthy vegetation because the NDVI is sensitive to chlorophyll absorbing red light and reflects the near-infrared (NIR) wavelengths due to scattering by the internal leaf structure [36,37]. NDVI values range from −1 to 1; when the value is greater than 0.3, it indicates healthy vegetation. ...
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Mangroves, which are vulnerable to natural threats and human activities on small islands in the tropics, play an essential role as carbon sinks, helping to mitigate climate change. In this study, we discussed the effect of natural factors on mangrove sustainability by analyzing the impact of rainfall, land surface temperature (LST), and tidal inundation on the greenness of mangroves in Karimunjawa National Park (KNP), Indonesia. We used Sentinel-2 image data to obtain the normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) during the dry season to determine the effect of inundation on mangrove greenness and soil moisture. The tidal inundation area was calculated using topographic data from the KNP and tidal observations from the area adjacent to it. Unmanned autonomous vehicles and topographic data were used to estimate mangrove canopy height. We also calculated mangrove greenness phenology and compared it to rainfall from satellite data from 2019–2021. Results show that the intertidal area is dominated by taller mangroves and has higher NDVI and NDMI values than non-intertidal areas. We also observed that mangroves in intertidal areas are mostly evergreen, and optimum greenness in KNP occurs from February to October, with maximum greenness in July. Cross-correlation analysis suggests that high rainfall affects NDVI, with peak greenness occurring three months after high rainfall. The LST and NDVI cross-correlation showed no time lag. This suggests that LST was not the main factor controlling mangrove greenness, suggesting tides and rainfall influence mangrove greenness. The mangroves are also vulnerable to climate variability and change, which limits rainfall. However, sea-level rise due to climate change might positively impact mangrove greenness.
... Phenological events, such as the start and end of growing season (SOS and EOS, respectively), can be extracted from the time series of remotely sensed vegetation indices (VIs) (D'Odorico et al., 2015;Piao et al., 2006a;White et al., 2009;Wu et al., 2017). The Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974), Enhanced Vegetation Index (EVI) (Huete et al., 2002), near-infrared reflectance of vegetation (NIRv) (Badgley et al., 2017) and Plant Phenology Index (PPI) (Jin and Eklundh, 2014) are extensively used structural VIs, which are closely linked to leaf area, green biomass and absorbed photosynthetically active radiation (APAR) Huete et al., 2002;Sims et al., 2006;Yin et al., 2020). Previous studies have confirmed that these VIs are efficient for tracking the seasonal changes of photosynthesis, especially for species with visible growth processes (Esteban et al., 2015;Hmimina et al., 2013;Richardson et al., 2006). ...
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Vegetation phenology is a sensitive indicator of ecosystem responses to climate change, and thus the accurate estimation of vegetation phenology is critical to evaluate the impact of climate change on terrestrial ecosystems. Common structural vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-infrared Reflectance of Vegetation (NIRv) and Plant Phenology Index (PPI), are the most widely used indicators of phenology, but they have limited potential in tracking autumn phenology, especially for evergreen species with low seasonal variability of canopy greenness. Given the important role of carotenoid pigments in regulating photosynthetic activity and plant phenology, we hypothesize that satellite-based indicators of leaf pigments derived from MODIS ocean bands could be useful for phenology modeling. Using 624 site-years of flux data at 84 FLUXNET sites and 9979 ground observations at 138 PEP725 sites, we first explored the potential of different forms of scaled photochemical reflectance index (sPRIref) in monitoring photosynthetic activity, and found that band 10 and band 13 were more suitable for tracking gross primary productivity (GPP) than other reference bands. By comparing with canopy photosynthetic phenology, sPRI10 and sPRI13 showed improved representation of phenological transitions (the start and end of growing season, SOS and EOS, respectively) than structural VIs. In spring, all VIs exhibited comparable performances for estimating SOS at deciduous broadleaf forests (DBF) and grasslands (GRA) sites; however, sPRI10 and sPRI13 were better predictors of SOS than structural VIs at evergreen needleleaf forests (ENF) and mixed forests (MF) sites. In autumn, sPRI10 and sPRI13 showed improved predictive strength of EOS than structural VIs for ENF, MF and GRA sites. Further investigations using the ground observed phenological records also confirmed the improved performances of sPRI10 and sPRI13 for both SOS and EOS estimation. We also investigated the spatial patterns of sPRI10-derived SOS and EOS over the Northern Hemisphere with respect to different plant functional types. We showed that sPRI10 reliably tracked plant phenology with 83.0% and 78.8% success in detecting SOS and EOS, respectively. Spatial patterns of SOS exhibited obvious latitudinal gradients, while EOS showed a strong regional heterogeneity. In addition, sPRI10 predicted an overall earlier SOS (61.8%) and later EOS (51.2%) than the MODIS phenology product (VNP22Q2 v001) estimated from structural VI, suggesting the latter underestimated the greening potential of the Northern Hemisphere. Our results suggest that MODIS PRI could be useful to monitor vegetation phenology, and further reveal the importance of underappreciated carotenoid pigments in tracking plant seasonal changes, particularly in autumn months.
... Normalized difference vegetation index (NDVI) is one of the most used vegetation indices. It was established by Rouse et al. (1973) to measure vegetative cover. Because there is a chlorophyll absorption peak in the red (600-700 nm) region and a reflectance plateau in the near-infrared (NIR, 750-900 nm) region, NDVI is based on these regions. ...
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Applying fertilizer nitrogen (N) only when a crop response is predicted may enhance use efficiency and profitability while protecting the environment. The crop response index at harvest (RI-harvest, the ratio of the maximum grain yield and that of the plot in question) indicates the actual crop response to applied fertilizer N, although it is calculated after harvest. The objective of this study was to predict RI-harvest of wheat using normalized difference vegetation index (NDVI) response index (RI-NDVI, defined as the ratio of the NDVI in an N-sufficient plot and that in the field in question) captured at Feekes 6 stage. Field experiments were carried out across seven site-years (2017/18 to 2020/21) on wheat. In the first three seasons, the relationships between RI-harvest and RI-NDVI were established by applying a range of fertilizer N levels (0–320 kg N ha − 1 ), whereas the fourth season was used for validation. The results indicated that RI-NDVI could explain 79% of the variation in RI-harvest using the linear relationship: RI-harvest = 7.077 × RI-NDVI – 6.4885. This model was satisfactorly validated in the fourth season using an independent data set in which a range of fertilizer N doses was applied before the Feekes 6 growth stage. Validation was also carried out by applying a fertilizer N dose corresponding to the predicted RI-harvest. In comparison to the general recommendation, the application of appropriate prescriptive fertilizer N dose along with a fertilizer N dose based on the predicted RI-harvest resulted in an 11% increase in fertilizer N recovery efficiency. It suggests that estimation of in-season RI-NDVI is a viable method for identifying fields that are likely to respond to additional fertilizer N.
... Normalized difference vegetation index (NDVI)-a vegetation parameter that is widely used in the analysis of natural hazards. NDVI was obtained by processing Sentinel-2 satellite images from July 30, 2021, and is calculated by the formula [82][83][84]: ...
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Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed, where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can will help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches, and methodology and criteria used in this study can be tested in other high mountainous regions.
... The feature extraction process, shown in Fig. 3, is based on landslide detection literature, as presented by Gerente et al. 48,49 The NDVI, developed by Rouse et al., 50 was used because it presents a drastic reduction in its values when the landslide occurs. In addition to that, once it is a composition of bands, it allows the use of the red and near-infrared (NIR) bands in one single feature. ...
... We computed the normalized difference vegetation index (NDVI) [72] which uses the red and the NIR band for the enhancement of vegetation canopies using averaged images of Landsat-5 and Landsat-8 from October to December in 1986-2021, and multiplied the NIR band to highlight the vegetation red-edge effects [73]. Then, we used the Otsu method to automatically extract thresholds to obtain MF areas. ...
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Although satellite remote sensing technology is intensively used for the monitoring of water quality, the inversion of coastal water bodies and non-optically active parameters is still a challenging issue. Few ongoing studies use remote sensing technology to analyze the driving forces of changes in water quality from multiple aspects based on inversion results. By the use of Landsat 5/8 imagery and measured in situ data of the total nitrogen (TN) and total phosphorus (TP) in the Shenzhen-Hong Kong Bay area from 1986 to 2020, this study evaluated the modeling effects of four machine learning methods named Tree Embedding (TE), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Back-propagation Neural Network (BPNN). The results show that the BPNN creates the most reliable and robust results. The values of the obtained correlation coefficients (r) are 0.83, 0.92, 0.84, and 0.90, and that of the coefficients of determination (R2) are 0.70, 0.84, 0.70, and 0.81. The calculated mean absolute errors (MAEs) are 0.41, 0.16, 0.06, and 0.02, while the root mean square errors (RMSEs) are 0.78, 0.29, 0.12, and 0.03. The concentrations of TN and TP (CTN, CTP) in the Shenzhen Bay, the Starling Inlet, and the Tolo Harbor were relatively high, fluctuated from 1986 to 2010, and decreased significantly after 2010. The CTN and CTP in the Mirs Bay kept continuously at a low level. We found that urbanization and polluted river discharges were the main drivers of spatial and inter-annual differences of CTN and CTP. Temperature, precipitation, and wind are further factors that influenced the intra-annual changes of CTN and CTP in the Shenzhen Bay, whilethe expansion of oyster rafts and mangroves had little effect. Our research confirms that machine learning algorithms are well suited for the inversion of non-optical activity parameters of coastal water bodies, and also shows the potential of remote sensing for large-scale, long-term monitoring of water quality and the subsequent comprehensive analysis of the driving forces.
... NDVI proposed by Rouse et al. [58], expressed as the following: ...
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The cobalt mining sector is well positioned to be a key contributor in determining the success of the Democratic Republic of the Congo (DRC) in meeting the Sustainable Development Goals (SDGs) by 2030. Despite the important contribution to the DRC’s economy, the rapid expansion of mining operations has resulted in major social, health, and environmental impacts. The objective of this study was to quantitatively assess the cumulative impact of mining activities on the landscape of a prominent cobalt mining area in the DRC. To achieve this, an object-based method, employing a support vector machine (SVM) classifier, was used to map land cover across the city of Kolwezi and the surrounding mining areas, where long-term mining activity has dramatically altered the landscape. The research used very high resolution (VHR) satellite imagery (2009, 2014, 2019, 2021) to map the spatial distribution of land cover and land cover change, as well as analyse the spatial relationship between land cover classes and visually identified mine features, from 2009 to 2021. Results from the object-based SVM land cover classification produced an overall accuracy of 85.2–90.4% across the time series. Between 2009 and 2021, land cover change accounted to: rooftops increasing by 147.2% (+7.7 km2); impervious surface increasing by 104.7% (+3.35 km2); bare land increasing by 85.4% (+33.81 km2); exposed rock increasing by 56.2% (+27.46 km2); trees decreasing by 4.5% (−0.34 km2); shrub decreasing by 38.4% (−26.04 km2); grass and cultivated land decreasing by 27.1% (−45.65 km2); and water decreasing by 34.6% (−3.28 km2). The co-location of key land cover classes and visually identified mine features exposed areas of potential environmental pollution, with 91.6% of identified water situated within a 1 km radius of a mine feature, and vulnerable populations, with 71.6% of built-up areas (rooftop and impervious surface class combined) situated within a 1 km radius of a mine feature. Assessing land cover patterns over time and the interplay between mine features and the landscape structure allowed the study to amplify the findings of localised on-the-ground research, presenting an alternative viewpoint to quantify the true scale and impact of cobalt mining in the DRC. Filling geospatial data gaps and examining the present and past trends in cobalt mining is critical for informing and managing the sustainable growth and development of the DRC’s mining sector.
... The Spectralon ® panel was used as a white reference for calibration purposes; each sample scan represented an average of 20 reflectance spectra. Starting from reflectance data, 10 common spectral vegetation indices (VIs), based on two or more wavelength combinations, were calculated (listed in Table S1) [49][50][51][52][53][54][55][56][57][58][59]. We selected literature indices related to both pigment and phenolics contents [41,60], such as the normalized difference vegetation index (NDVI), the red-edge NDVI (mNDVI), the modified chlorophyll absorption ratio index (MCARI), the plant senescence reflectance index (PSRI), the modified red-edge ratio (mSR), the pigment specific simple ratio (PSSR), the carotenoid reflectance index-1 (CRI 550 ), the carotenoid reflectance index-2 (CRI 700 ), the anthocyanin reflectance index (ARI), and the modified anthocyanin reflectance index (mARI). ...
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This work was aimed at investigating the effects of rate and timing of nitrogen fertilization applied to a maternal wheat crop on phytochemical content and antioxidant activity of edible sprouts and wheatgrass obtained from offspring grains. We hypothesized that imbalance in N nutrition experienced by the mother plants translates into transgenerational responses on seedlings obtained from the offspring seeds. To this purpose, we sprouted grains of two bread wheat cultivars (Bologna and Bora) grown in the field under four N fertilization schedules: constantly well N fed with a total of 300 kg N ha −1 ; N fed only very early, i.e., one month after sowing, with 60 kg N ha −1 ; N fed only late, i.e., at initial shoot elongation, with 120 kg N ha −1 ; and unfertilized control. We measured percent germination, seedling growth, vegetation indices (by reflectance spectroscopy), the phytochemical content (total phenols, phenolic acids, carotenoids, chlorophylls), and the antioxidant activity (by gold nanoparticles photometric assay) of extracts in sprout and wheatgrass obtained from the harvested seeds. Our main finding is that grains obtained from crops subjected to late N deficiency produced wheatgrass with much higher phenolic content (as compared to the other N treatments), and this was observed in both cultivars. Thus, we conclude that late N deficiency is a stressing condition which elicits the production of phenols. This may help counterbalance the loss of income related to lower grain yield in crops subjected to such an imbalance in N nutrition.
... The camera features high data transfer rates (up to 120 fps). From the acquired multispectral imagery the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974) and the Green Normalized Difference Vegetation Index (GNDVI) (Ecarnot et al., 2013) were computed. Also, as shown in Table S1 (see supplementary material) the calculated reflectance values of the two spectral bands related with the Chlorophyll (Band 560 nm) and water stress (Band 840 nm) (González- Fernández et al., 2015;Peñuelas et al., 1997) were also separately recorded. ...
Article
Efficient irrigation in viticulture requires objective and representative monitoring of the vineyard water status variability. In this work the combination of multispectral, environmental and thermal data (using an infrared radiometer) acquired simultaneously on-the-go (at midday), from a ground moving vehicle (moving at 3 km/h) was tested to assess the vineyard stem water potential (Ψs) and its spatial variability (three different irrigation treatments were imposed) over two seasons in north Spain. Partial least squares (PLS) cross-validation regression models involving the canopy temperature (Tc), environmental and spectral variables yielded determination coefficients (R²cv) of ~ 0.63 and root mean square error of cross-validation (RMSECV) between 0.124 MPa and 0.206 MPa in the two seasons. Linear discriminant analysis (LDA) involving only the variables used to build the regression models was run to distinguish among low, medium and high water stressed vines, yielding an average percentage of correct classification samples of 74%. The satisfactory performance of the multivariate models involving thermal, environmental and spectral data to either estimate or classify the plant water status within a vineyard supports the approach towards the combination of different data source to improve the capabilities of thermography itself. The inclusion of vegetative spectral indices in the regression and classification models of grapevine water status may provide real-time feedback on grapevine water use as influenced by actual vegetative growth, abiotic and/or biotic stress patterns. This combined approach can be seen as an advancement from existing solutions to assess plant water status variability given the simplicity and potential to automation of the thermal sensor employed and the integration of environmental and canopy vigour data into the model.
... The NDVI (Normalized Difference Vegetation Index) was used (Rouse et al., 1973). We used data from the MODIS-Terra (Moderate Resolution Imaging Spectroradiometer) sensor system Product MOD13A1, with a spatial resolution of 500 m evaluated every 16 days, which were downloaded from the NASA AρρEEARS platform (https://lpdaacsvc.cr.usgs.gov/appeears/). ...
... Aggregation from two meters to 15 meters was done by averaging values for continuous covariates or by selecting the prevailing category for categorical covariates. Covariates are as follows, where we use upper-case notation throughout to refer to the names of these covariates: Elevation (or Digital Elevation Model, abbreviated DEM); Aspect, that is, the angle in [0, 2π) describing the exposition of the area with respect to the north (Zevenbergen and Thorne (1987)); Slope Steepness (Zevenbergen and Thorne (1987)); Planar Curvature (Heerdegen and Beran (1982)), which is measured perpendicular to the steepest slope angle and characterizes the convergence and divergence of flow across the surface; Profile Curvature (Heerdegen and Beran (1982)), which indicates the direction of maximum slope; Topographic Wetness Index (TWI) (Beven and Kirkby (1979)), which quantifies topographic properties related to hydrological processes using slope and upstream contributing area as input; Stream Power Index (SPI) (Moore, Grayson and Ladson (1991)), which takes similar input as TWI and measures more specifically the erosive power of flowing water; Landform (with 10 categories, see Wilson and Gallant (2000)); the distance of each pixel to the closest tectonic fault line (Dist2Fault, in m); Normalized Difference Vegetation Index (NDVI) (Rouse Jr. et al. (1974)), which measures the "greenness" of a landscape and serves as a proxy for vegetation; Lithology, that is, soil type with 22 categories, where rare soil types with less than 500 occurrence pixels have been summarized in a single class "other;" Land Use (with 13 categories). The choice of a 15 m × 15 m grid yields a representation of the study area through 449,038 pixels. ...
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Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution. However, the spatial distribution of the triggering event (e.g., precipitation or earthquakes) is often not directly observed. In this paper we develop Bayesian spatial hierarchical models for point patterns of landslide occurrences using different types of log-Gaussian Cox processes. Starting from a competitive baseline model that captures the unobserved precipitation trigger through a spatial random effect at slope unit resolution, we explore novel complex model structures that take clusters of events arising at small spatial scales into account as well as nonlinear or spatially-varying covariate effects. For a 2009 event of around 5000 precipitation-triggered landslides in Sicily, Italy, we show how to fit our proposed models efficiently, using the integrated nested Laplace approximation (INLA), and rigorously compare the performance of our models both from a statistical and applied perspective. In this context we argue that model comparison should not be based on a single criterion and that different models of various complexity may provide insights into complementary aspects of the same applied problem. In our application our models are found to have mostly the same spatial predictive performance, implying that key to successful prediction is the inclusion of a slope-unit resolved random effect capturing the precipitation trigger. Interestingly, a parsimonious formulation of space-varying slope effects reflects a physical interpretation of the precipitation trigger: in subareas with weak trigger, the slope steepness is shown to be mostly irrelevant.
... LiDAR is suitable for analyzing the underlying microtopography of wetlands because it can produce a digital terrain model (DTM) with a high spatial resolution (Boon et al., 2016;Pricope et al., 2020). If a multispectral sensor that can also capture near-infrared wavelengths is used, it is possible to calculate the normalized difference vegetation index (NDVI), which is related to the amount of vegetation present (Rouse et al., 1973), and the normalized difference water index (NDWI), which is related to the dryness and wetness of the soil (Kon et al., 2020). LiDAR and multispectral sensors are mainly used in wetlands for habitat classification (Dronova et al., 2021;Rapinel et al., 2015). ...
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Recently, technological advances in UAV-mounted sensors, such as light detection and ranging (LiDAR) and multispectral sensors, have expanded the applications of unmanned aerial vehicles (UAVs) in ecosystem monitoring. LiDAR is suitable for analyzing the underlying microtopography of wetlands because it can produce a digital terrain model (DTM) with high spatial resolution. If a multispectral sensor that can also capture near-infrared wavelengths is used, it is possible to calculate the normalized difference vegetation index (NDVI), which is related to the amount of vegetation present, and the normalized difference water index (NDWI), which is related to the dryness and wetness of the soil. The purpose of this study was to understand the distribution of a disturbance-dependent species in wetlands using high spatial resolution images acquired with a consideration of phenology, and to evaluate the habitat of this disturbance-dependent species using data acquired by LiDAR and multispectral sensors. The wetland around the Omimaiko Inland Lake in Minamikomatsu, Otsu City, Shiga Prefecture, Japan, was chosen as the site for this study. I chose to examine the distribution of Euphorbia adenochlora as a disturbance-dependent species growing in the wetlands of the study area. Using high spatial resolution images acquired with a consideration of phenology, we were able to determine the distribution of the disturbance-dependent species E. adenochlora. Using the data obtained using LiDAR and multispectral sensors, we were able to evaluate its habitat and deduce its viability at six growth sites. This study aims to introduce a new way of applying UAVs in monitoring disturbance-dependent species in wetlands.
... Soil Adjusted Vegetation Index (SAVI) [34] Atmospherically Resistant Vegetation Index (ARVI) [45] Transformed Soil Adjusted Vegetation Index (TSAVI) [35,36] Normalized Difference Index (NDI 45) [46] Modified Soil Adjusted Vegetation Index (MSAVI) [37] Meris Terrestrial Chlorophyll Index (MTCI) [47] Second Modified Soil Adjusted Vegetation Index (MSAVI 2) [37] Modified Chlorophyll Absorption Ratio Index (MCARI) [48] Difference Vegetation Index (DVI) [38] Sentinel-2 Red-Edge Position Index (S2REP) [49] Ratio Vegetation Index (RVI) [39] Inverted Red-Edge Chlorophyll Index (IECI) [50] Perpendicular Vegetation Index (PVI) [40] Pigment Specific Simple Ratio (PSSR) [51] Infrared Percentage Vegetation Index (IPVI) [41] Normalized Difference Vegetation Index (NDVI) [52] Weighted Difference Vegetation Index (WDVI) [42] Modified Red Edge Normalized Difference Vegetation Index (NDVI 705 ) [53] Transformed Normalized Difference Vegetation Index (TNDVI) [38] Enhanced Vegetation Index (EVI) [54] Green Normalized Difference Vegetation Index (GNDVI) [43] 2-Band Enhanced Vegetation Index (EVI2) [55] Global Environmental Monitoring Index (GEMI) [44] https://doi.org/10.1371/journal.pone.0272300.t004 ...
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Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods.
... The most commonly used vegetation index is the NDVI (normalized difference vegetation index). It is defined as the difference between the surface reflectance in the near-infrared range and the red on the sum of the plants [36,37]. The similarity between the evolution of the vegetation index (NDVI) during the crop development cycle and that of the crop coefficient has encouraged scientists to study the relationship between these two parameters in order to estimate crop water requirements on a regional scale [17,32]. ...
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In arid and semi-arid regions, agriculture is an important element of the national economy , but this sector is a large consumer of water. In a context of high pressure on water resources, appropriate management is required. In semi-arid, intensive agricultural systems, such as the Tadla irrigated perimeter in central Morocco, a large amount of water is lost by evapotranspiration (ET), and farmers need an effective decision support system for good irrigation management. The main objective of this study was to combine a high spatial resolution Sentinel-2 satellite and meteorological data for estimating crop water requirements in the irrigated perimeter of Tadla and qualifying its irrigation strategy. The dual approach of the FAO-56 (Food and Agriculture Organization) model, based on the modulation of evaporative demand, was used for the estimation of crop water requirements. Sentinel-2A temporal images were used for crop type mapping and deriving the ba-sal crop coefficient (Kcb) based on NDVI data. Meteorological data were also used in crop water requirement simulation, using SAMIR (satellite monitoring of irrigation) software. The results allowed for the spatialization of crop water requirements on a large area of irrigated crops during the 2016-2017 agricultural season. In general, the crops' requirement for water is at its maximum during the months of March and April, and the critical period starts from February for most crops. Maps of water requirements were developed. They showed the variability over time of crop development and their estimated water requirements. The results obtained constitute an important indicator of how water should be distributed over the area in order to improve the efficiency of the irrigation scheduling strategy .
... The NDVI index is based on the relationship between energy absorption in the red range by chlorophyll and increased reflectance in the near infrared range for healthy vegetation. This index is calculated from Equation 3 (Rouse et al., 1974). ...
... The NDVI was calculated using the bands 5 (near-infrared) and 4 (red) of the Landsat 8 satellite. The NDVI indicates the presence of photosynthetic activity, what allows infer about productivity, aggregation, tree stratum, and the different types of vegetation present in the soil (Rouse et al. 1974;Tucker 1979). ...
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... The NDVI is a vegetation index reflecting the properties of photosynthetic vegetation [57,58]. Indeed, generally, the vegetation absorbs solar radiation in the blue and red wavelengths due to the presence of photosynthetic pigments in the leaves (chlorophyll, xanthophyll, carotene) and it reflects it in the near-infrared (NIR) wavelength due to the structure of the mesophyll of the leaf [59]. ...
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Regardless of their biogeographic origins or degree of artificialization, the world’s forests are a source of a wide range of ecosystem services (ES). However, the quality and quantity of these services depend on the type of forest studied and its phytogeographic context. Our objective is to transpose the concept of ES, in particular, the assessment of forest ES, to the specific Mediterranean context of the North African mountains, where this issue is still in its infancy and where access to the data needed for assessment remains difficult. Our work presents an introductory approach, allowing us to set up methodological and scientific milestones based on open-access remote sensing data and already tested geospatial processing associated with phytoecological surveys to assess the ES provided by forests in an Algerian study area. Specifically, several indicators used to assess (both qualitatively and quantitatively) the potential ES of the Ouled Hannèche forest, a forest located in the Hodna Mountains, are derived from LANDSAT 8 OLI images from 2017 and an ALOS AW3D30 DSM. The qualitative ES typology is jointly based on an SVM classification of topographically corrected LANDSAT images and a geomorphic-type classification using the geomorphon method. NDVI is a quantitative estimator of many plant ecosystem functions related to ES. It highlights the variations in the provision of ES according to the types of vegetation formations present. It serves as a support for estimating spectral heterogeneity through Rao’s quadratic entropy, which is considered a relative indicator of biodiversity at the landscape scale. The two previous variables (the multitemporal NDVI and Rao’s Q), completed by the Shannon entropy method applied to the geomorphon classes as a proxy for topo-morphological heterogeneity, constitute the input variables of a quantitative map of the potential supply of ES in the forest determined by Spatial Multicriteria Analysis (SMCA). Ultimately, our results serve as a useful basis for land-use planning and biodiversity conservation.
... Nevertheless, vegetation indices are among the most widely used indicators for the remote sensing of vegetation properties. Probably, the most popular index is the Normalized Difference Vegetation Index (NDVI), originally proposed by Rouse et al. [24], which has been used in several studies about the development of vegetation, Chl, green biomass, N content, and LAI. Apart from NDVI, a wide array of alternative indices was proposed that aimed to optimize sensitivity toward LAI and CCC. ...
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The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. According to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year−1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with different doses of granulated urea and granulated urea was carried out, where biophysical characteristics were evaluated in the field and in Sentinel-2 satellite observations. Field data correlated well with satellite data, with an R2 of 0.91, 0.96, and 0.85 for LAI, fCOVER, and CCC, respectively. The application of nitrogen in all its doses produced significantly higher yields than the control. The LAI and CCC variables had a positive correlation with yield in the months of November and December. A significant difference was observed between U250 (62 Mg ha−1) and the other treatments. The U500 dose led to a yield increase of 27% compared to U250, while the difference between U750 and U500 was 6%.
... , including common indices such as the Normalized Difference Vegetation Index (NDVI;Rouse et al. 1974), Enhanced Vegetation Index (EVI;Liu and Huete 1995), and Land Surface Water Index (LSWI;Xiao et al. 2004), as well as lesser-known indices (i.e., Green Chlorophyll Index (GCI;Gitelson et al. 2003), Green-red Vegetation Index (GRVI; Tucker 1979) that have shown prior success in evergreen tree monitoring(Huete 2012, Muraoka et al. 2013, Xue and Su 2017. We produced Maximum Value Composite (MVC) images for each index year -1 of the study. ...
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... For leaf and plant characteristics, we estimated leaf pigments using six spectral indices: the chlorophyll vegetation index (CVI) and the green leaf index (GLI) for chlorophyll content; the chlorophyll index green (CIgreen), which quantifies the photosynthetic activity; the carotenoid reflectance index 550 (CRI550) and the structure intensive pigment index 3 (SIPI3), which quantify the carotenoids and photosynthetic pigments; and the green difference vegetation index (GDVI), which estimates the content of nitrogen (Tucker et al., 1979;Peñuelas et al., 1995;Gobron et al., 2000;Gitelson et al., 2005;Vincini et al., 2008). Canopy biomass characteristics were described by computing five spectral indices: the normalized difference vegetation index (NDVI) and simple ratio (SR), which are canonical biomass indices; the enhanced vegetation index (EVI), which improves the biomass estimate compared to two previous indices, reducing the atmospheric influence; the transformed vegetation index (TVI), which is sensitive to the photosynthetically active biomass; and the visible atmospherically resistant index green (VARIgreen), able to estimate the canopy biomass using visible bands (B, G, and R, Huete et al., 2002;Tucker, 1979;Birth and McVey, 1968;Rouse et al., 1973;Gitelson et al., 2002). As for soil features, we calculated the brightness index (BI) and brightness index 2 (BI2) as proxies of the bare surface, while the coloration Frontiers in Environmental Science frontiersin.org ...
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Reasonable cultivation is an important part of the protection work of endangered species. The timely and nondestructive monitoring of chlorophyll can provide a basis for the accurate management and intelligent development of cultivation. The image analysis method has been applied in the nutrient estimation of many economic crops, but information on endangered tree species is seldom reported. Moreover, shade control, as the common seedling management measure, has a significant impact on chlorophyll, but shade levels are rarely discussed in chlorophyll estimation and are used as variables to improve model accuracy. In this study, 2-year-old seedlings of tropical and endangered Hopea hainanensis were taken as the research object, and the SPAD value was used to represent the relative chlorophyll content. Based on the performance comparison of RGB and multispectral (MS) images using different algorithms, a low-cost SPAD estimation method combined with a machine learning algorithm that is adaptable to different shade conditions was proposed. The SPAD values changed significantly at different shade levels (p < 0.01), and 50% shade in the orthographic direction was conducive to chlorophyll accumulation in seedling leaves. The coefficient of determination (R2), root mean square error (RMSE), and average absolute percent error (MAPE) were used as indicators, and the models with dummy variables or random effects of shade greatly improved the goodness of fit, allowing better adaption to monitoring under different shade conditions. Most of the RGB and MS vegetation indices (VIs) were significantly correlated with the SPAD values, but some VIs exhibited multicollinearity (variance inflation factor (VIF) > 10). Among RGB VIs, RGRI had the strongest correlation, but multiple VIs filtered by the Lasso algorithm had a stronger ability to interpret the SPAD data, and there was no multicollinearity (VIF < 10). A comparison of the use of multiple VIs to estimate SPAD indicated that Random forest (RF) had the highest fitting ability, followed by Support vector regression (SVR), linear mixed effect model (LMM), and ordinary least squares regression (OLR). In addition, the performance of MS VIs was superior to that of RGB VIs. The R2 of the optimal model reached 0.9389 for the modeling samples and 0.8013 for the test samples. These findings reinforce the effectiveness of using VIs to estimate the SPAD value of H. hainanensis under different shade conditions based on machine learning and provide a reference for the selection of image data sources.
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Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution.
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Bu çalışmada 2009 yılında meydana gelen ve afet bölgesi olarak ilan edilen Manisa ili, Demirci ilçesi sınırlarında bulunan Tekeleler köyünün heyelan duyarlılık haritası coğrafi bilgi sistemleri (CBS) tabanlı frekans oranı (FO) yöntemi kullanılarak üretilmiştir. Heyelan duyarlılık analizinde yağış, eğim, bakı, yükseklik, akarsuya uzaklık, yola uzaklık, arazi kullanımı, litoloji, eğrisellik, topografik nemlilik indeksi (topographic wetness index, TWI), normalize edilmiş fark bitki örtüsü indeksi (normalized difference vegetation index, NDVI) koşullandırma faktörleri olarak seçilmiştir. Heyelan olan bölgeden Google Earth görüntüleri kullanılarak örnek rastgele noktalar belirlenmiş, belirlenen noktalar %70’i eğitim %30’u test için iki sınıfa bölünmüştür. Üretilen heyelan duyarlılık haritası çok düşük, düşük, orta, yüksek ve çok yüksek olmak üzere beş farklı sınıfa ayrılmıştır. Bu sınıflar içerisinde kalan alanlar sırasıyla tüm alanın %11,36, %39,61, %34,32, %12,89 ve %1,81’ini kapladığı görülmüştür. Heyelan duyarlılık haritasının doğruluğu alıcı işletim karakteristiği (receiver operating characteristic, ROC) eğri altında kalan alan (curve and the area under the curve, AUC) yöntemi kullanılarak hesaplanmıştır. AUC değeri başarı oranı %95,14 ve tahmin oranı %94,11 olarak hesaplanmıştır. Bu çalışma ile FO yöntemi kullanılarak heyelan duyarlılık haritalarının başarılı bir şekilde üretilebileceği gösterilmiştir. Ayrıca bulunan sonuç haritanın olası muhtemel heyelanlar için bir öngörü niteliğinde olduğu, afet yönetim ve planlama çalışmalarına entegre edilebileceği sonucuna varılmıştır.
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