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Monitoring Vegetation Systems in the Great Plains with ERTS

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... Over the years, a myriad of indices have been proposed for a wide variety of applications. For example, the normalized difference vegetation index (NDVI) (Rouse et al. 1974) is a measure of plant health, while the analytical burned area index (ABAI) makes it easy to distinguish between burned and healthy forest areas. In most cases, these indices are either directly derived from spectral reflectance properties or determined through extensive field studies, experiments and numerical optimization techniques. ...
... In view of the heavy use of spectral indices in multispectral remote sensing applications Mouafik et al. 2024;Tran, Reef, and Zhu 2022;Zeng et al. 2020), this work introduces a novel index, the linear ratio index (LRI), a generalization of the popular normalized difference, ratio and difference indices (Jordan 1969;Rouse et al. 1974;Tucker 1979). Specifically, the generalization is achieved by extending the normalized difference index to a weighted linear sum of all available spectral bands in both the numerator and denominator. ...
... Most spectral indices are based on the early normalized difference, ratio and difference indices (Jordan 1969;Rouse et al. 1974;Tucker 1979), which are calculated as shown in Equation 1-Equation 3. c a 2 R m�l and c b 2 R m�l are the spectral input bands where m denotes the height and l denotes the width of the bands. I nd , I r and I d correspond to the normalized difference, ratio and difference index, respectively. ...
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Unmanned aerial vehicles, equipped with multispectral imaging systems, as well as publicly available datasets, have fueled various research in remote sensing applications, including precision agriculture, land cover mapping or even camouflage detection. Many of these applications make use of spectral indices, such as normalized difference or ratio indices, created by merging multiple raw bands. These indices typically provide a direct indication of certain physical surface properties, like plant health, nitrogen content or leaf area index, and are determined by studying spectral reflectance properties or using optimization techniques. Given the heavy use and utility of such indices, this work introduces a novel generalization of the normalized difference, ratio and difference indices, the linear ratio index (LRI), a ratio of two linear functions of all available bands. In addition, an optimization approach for the LRI is presented, which incorporates complexity reduction strategies that enable optimization using only a subset of all available bands and reduced parameter precision, thereby ensuring parameter readability. The LRI and its optimization are thoroughly investigated in the context of camouflage detection in tactical reconnaissance scenarios by optimizing a six-band and a two-band LRI using the eXtended Multispectral Dataset for Camouflage Detection (MUDCAD-X). For comparison with traditional spectral index optimization approaches, the resulting linear ratio indices (LRIs) are evaluated against all raw bands and an optimized normalized difference index and an optimized ratio index. The evaluation shows that the optimized LRIs provide the best overall results in terms of visibility and detectability of camouflaged targets. This could indicate a general superiority of the LRI over established indices optimized by testing band permutations, making the LRI a promising candidate for further investigation in other remote sensing applications where it could also outperform traditional index optimization approaches. Therefore, the software code for optimizing the LRI has been made publicly available for further exploitation, requiring only an adapted optimization criterion to support any other use case. Furthermore, as with most spectral indices, the LRIs obtained in this study have negligible computational overhead and, under the right conditions, can be directly integrated into any existing camouflage detection system.
... Finally, we assessed spatial agreement (Guerschman et al. 2009, Nagler et al. 2003) Surrogate Cellulose Absorption Index (SWIR2/SWIR1) EVI2 (Z. Jiang et al. 2008) Enhanced Vegetation Index 2 2.5 * ((NIR -Red)/(NIR +2.4 * Red + 1)) MNDWI (Xu 2006) Modified Normalized Difference Water Index (Green -SWIR1)/(Green + SWIR1) NBR (Key and Benson 2006) Normalized Burn Ratio (NIR -SWIR2)/(NIR + SWIR2) NDMI (McDonald, Gemmell, and Lewis 1998) Normalized Difference Moisture Index (NIR -SWIR2)/(NIR + SWIR2) NDVI (Rouse et al. 1974) Normalized Difference Vegetation Index (NIR -Red)/(NIR + Red) NDWI (Gao 1996) Normalized Difference Water Index (NIR -Green)/(NIR + Green) SAVI (Huete 1988) Soil Adjusted Vegetation Index ((1 + L) * (NIR -Red))/(NIR + Red + L) between our map and three existing products (CGLS-LC100, MCD12Q1, and MapBiomas) by resampling them to match our resolution and reclassifying their legends to our Level-1 scheme (Supplementary Materials, Tables S1-S3). ...
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Northeast Brazil (NEB), about three times the area of Spain, hosts >90% of Brazil’s drylands along with tropical rain- and dry forests. Climate variability, 45–60% cloud cover, and scarce reference data limit land use and land cover (LULC) mapping accuracy to ~80% across much of the region. Here, we introduce NEB’s first context-specific LULC framework, using phenologically timed annual MODIS mosaics (2000–2020) with <0.5% pixel gaps and minimal seasonal bias. Ecoregions were individually classified using customized random forest (RF) models with 56 features – including linear spectral unmixing fractions – prior to NEB-wide integration. Binary masks excluding non-mangrove pixels during RF training significantly improved mangrove-rainforest separation. Our two-tier classification scheme uniquely distinguishes NEB shrublands nationally, aligns with global datasets (7 Level-1 classes), and preserves ecoregion detail (16 Level-2 classes). Validated against >10,000 independent points, the 2018 NEB-wide map achieved 90.5% overall accuracy at Level-1. At Level-2, ecoregional accuracies were 95.9% (Amazon), 94.3% (Atlantic Forest), 89.4% (Cerrado), and 87.9% (Caatinga). Per-pixel spatial agreement with national and global datasets ranged from 29–70%. Between the 2000 and 2020 endpoints, ~540,000 km² of NEB underwent LULC changes, based on pixel counts. Forest declined 22%, grasslands 68%, and agriculture expanded 140% – roughly 10 million soccer fields – mainly in the Cerrado MaToPiBa (Maranhão, Tocantins, Piauí, Bahia) frontier. Meanwhile, encroachment around protected areas intensified, particularly in the Amazon. This open-access product (http://www.dsr.inpe.br/DSR/laboratorios/LAF) sets a benchmark for LULC mapping in global dryland-forest mosaics, positioning NEB as a model for data-driven land management.
... In the context of warming and humidification, climate factors such as precipitation and temperature have different impacts on vegetation growth (Horion et al., 2013). The Normalized Difference Vegetation Index (NDVI) first proposed by Rouse (1973) in 1973 effectively represents changes in vegetation and ecosystem parameters and sensitively reflects changes in climate factors (Gao & Dennis, 2001;Horion et al., 2013). Among these climate factors, Liu et al. (2016) found that in other arid and semi-arid regions, the dominant factors influencing vegetation changes are temperature and precipitation. ...
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The study of seasonal-scale climate-vegetation coupling mechanisms is important for coordinating desertification control and climate adaptation. Taking the Mu Us Sandy Land (MUSL) as a case study, we gathered meteorological data from 1959 to 2019 (including maximum temperature, minimum temperature, and precipitation) for seasonal analysis. We conducted M–K significance and mutation analysis, Morlet wavelet periodicity analysis and correlation analysis and investigated the effects of various seasonal and annual climate factors on NDVI using NDVI values collected from 1999 to 2019. The results indicate the following: (1) Both the maximum and minimum temperatures in the MUSL exhibit an upward trend across all four seasons. Precipitation in autumn shows a decreasing trend, while in spring, summer, and winter, it increases, leading to an overall rise in precipitation. (2) The maximum and minimum temperatures in MUSL experienced a mutation in the 1980 and 2000, respectively, while precipitation underwent a mutation in the 1980 and 2019. After these mutations, both temperature and precipitation exhibited an overall upward trend. (3) The first primary cycle for both the maximum and minimum temperatures is 18 years, while the first primary cycle for precipitation is 8 years. (4) The impact of the climate in MUSL on vegetation is as follows: precipitation > temperature.
... Crop parameter extraction was performed using time series of the Normalized Difference Vegetation Index (NDVI) [38], derived from Sentinel-2 imagery with a spatial resolution of 10 m and a revisit time of 5 days [39]. These satellite images were integrated with land use maps to confirm the crop types irrigated by each system. ...
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This study presents a robust methodology for the indirect estimation of groundwater abstraction for irrigation at the scale of individual wells, addressing a key gap in data-scarce agricultural settings. The approach combines NDVI time series, crop water requirement modelling, and spatial analysis of irrigation systems within a GIS environment. A soil water balance model was applied to Homogeneous Units of Analysis, and irrigation requirements were estimated using an ensemble approach accounting for key sources of uncertainty related to phenology detection, soil moisture at sowing (%SAW), and irrigation system efficiency. A spatial linkage algorithm was developed to associate individual wells with the irrigated areas they supply. Sensitivity analysis demonstrated that 10% increases in %SAW resulted in abstraction reductions of up to 1.98%, while 10% increases in irrigation efficiency reduced abstractions by an average of 6.48%. These findings support the inclusion of both parameters in the ensemble, generating eight abstraction estimates per well. Values ranged from 33,000 to 115,000 m 3 for the 2023 season. Validation against flowmeter data confirmed the method's reliability, with an R 2 of 0.918 and an RMSE equivalent to 9.3% of the mean observations. This approach offers an accurate, spatially explicit estimation of groundwater abstractions without requiring direct metering and offers a transferable, cost-effective tool to improve groundwater accounting and governance in regions with limited monitoring infrastructure.
... The geology of the Chianti area was simplified into fourteen major groups based on lithology type and age. The Normalized Difference Vegetation Index (NDVI; Rouse et al., 1973) and the Normalized Difference Water Index (NDWI; McFeeters, 1996) were derived from remote-sensing data. The NDVI is useful to understand the distribution of vegetation and provides information about the relation between vegetation cover and bare soil. ...
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Ensuring an efficient Extra Virgin Olive Oil (EVOO) traceability is fundamental to support a sustainable produced food. To date assessing geographical origin of EVOO is a challenging issue, especially if the authenticity is evaluated based on objective parameters beyond subjectivity or paper certifications. Consequently, technical approaches are becoming increasingly complex, such as the use of radiogenic 87Sr/86Sr to link the soil of the origin to the EVOO. We present a novel database of geological and organic 87Sr/86Sr values (n = 133) from Tuscany, using newly analyzed samples and literature data, to establish the first comprehensive bioavailable Sr isotope baseline for this region. We show that the 87Sr/86Sr of EVOO is a powerful fingerprint if we consider the olive mill processes. To this end a two-component mixing approach based on olive fruits and tap water was used to assess the contribution of different sources of 87Sr/86Sr to EVOO. The isoscape of bioavailable 87Sr/86Sr was generated using a novel geoinformatics framework for a representative Tuscan territory with an high quality standard EVOO production. Results show that the 87Sr/86Sr values of Tuscany EVOOs range from 0.70854 to 0.70974. The median value, equal to 0.70911, is higher than Tunisian (0.70861) and northern Spanish (0.70837) EVOOs. The proof of concept shown in this research is highly relevant for geographic EVOO traceability as it allows a complete evaluation of the critical path in using the Sr isotope signature for provenance study.
... Values closer to 0.5 represent coffee plants with low vigor, which may be a physiological response to some kind of constraint. We calculated the NDVI through the difference in reflectance between the NIR (near infrared) and Red bands divided by the sum of the NIR and Red bands according to equation 1, proposed by (Rouse et al., 1974). ...
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Soils of the Cerrados (Brazilian Savanna) are deep, well-structured, and well-drained, with flat to gently undulating terrain that favors mechanization for coffee cultivation. However, these soils are susceptible to compaction. This study aimed to assess the effect of mechanization on the physical characteristics of an Oxisol under irrigated coffee cultivation in the Alto Paranaíba-Minas Gerais State. We selected eight areas with different cultivars and years of Arabica coffee plantation, sampling five positions: right soil under the tree crown (RSC), right tractor lines (RTL), interrows (IR), left tractor lines (LTL), and left soil under the tree crown (LSC) at layers of 0.00-0.10, 0.10-0.20, 0.20-0.30, and 0.30-0.40 m. We conducted principal component analysis (PCA) and analysis of variance, comparing means through Tukey’s test (p<0.05). The PCA selected three principal components (PC1, PC2, and PC3) composed of 12 physico-chemical properties from a total of 27 evaluated. Total porosity (TP), mean penetration resistance (PRmean), volumetric moisture (θ) at 100 kPa (θ 100 kPa) and 300 kPa (θ 300 kPa) tensions, particle density (PD), and granulometric fractions (clay, fine sand, and coarse sand) were among the most influential attributes. Total porosity and PRmean demonstrated the existence of compaction in the tractor wheel tracks, particularly in the 0.00-0.20 m layer. The 3.5-year-old plantation did not show significant variations in these properties. The θ 100 kPa and θ 300 kPa were higher in the compacted areas, indicating increased water retention but potentially limiting aeration. Clay content increased with depth, while sand fractions decreased, influencing the soil susceptibility to compaction. The vigor of coffee plants, as identified by satellite images (NDVI), could not be fully associated with the physical constraints of the subsurface, as even areas with low vigor did not consistently correlate with poor physical properties in laboratory analyses. These findings highlight the complex interplay between soil physical properties and coffee plant performance, emphasizing the need for comprehensive management strategies in mechanized coffee cultivation.
... The resulting chlorophyll distribution map reveals significantly elevated concentrations, reaching a maximum of 39 mg m − 3 , in coral reefs and adjacent waters due to the presence of symbiotic and coralline algae (Varunan and Shanmugam 2021) surrounded by coral reefs, while the rest of the region displays notably very low concentrations. In Figure 12(c), the Normalized Difference Vegetation Index (NDVI) is computed using the red (684 nm) and NIR (704 nm) wavelength (Rouse et al. 1974). The 704 nm band lies within the red-edge transition region, making it particularly sensitive to changes in chlorophyll contents. ...
Article
Unmanned Aerial Systems (UAS) equipped with push broom hyperspectral imaging (HSI) sensors offer unique advantages for high-resolution monitoring of inland and coastal aquatic environments. However, accurate retrieval of water-leaving radiance from UAS-based HSI data is challenged by atmospheric path effects, such as molecular and aerosol scattering, which significantly impact observed radiance and necessitate a robust atmospheric correction method. This study introduces Hycor (HYperspectral atmospheric CORrection), an atmospheric correction algorithm designed specifically for UAS-based hyperspectral data. Hycor leverages in-situ atmospheric measurements to accurately retrieve water-leaving radiance. It incorporates a novel pressure correction scheme for Rayleigh Optical Thickness (ROT) and employs a pixel-wise, image-based aerosol estimation method tailored to the lower altitudes typical of UAS deployments. Validation against in-situ data in turbid coastal waters demonstrated Hycor’s effectiveness in reducing radiance deviations and improving water-quality metrics, such as chlorophyll and turbidity. Statistical comparisons across wavelengths showed significant accuracy improvements. At shorter wavelengths (413–560 nm), initial radiance deviations from in-situ data ranged from 25.8% to 70.3%, reduced to 7.8%–20.2% with Hycor. At longer wavelengths (670–865 nm), where initial deviations were higher due to complex water reflectance and minimal atmospheric boundary signals, Hycor reduced these deviations from 83.0%–1166.7% to 26.1%–99.5%. Overall, Hycor’s corrections enable more accurate retrieval of spectral water-leaving radiances, particularly at shorter wavelengths, enhancing UAS-based HSI’s capacity to assess water quality in aquatic environments. This advancement lays the groundwork for real-time, high-precision UAS-based HSI applications in water colour remote sensing.
... Excess green index (ExG) Rouse et al., 1974 Normalized difference red edge index (NDRE) Barnes et al. (2000) R, G, B, RE, and NIR represent the red, green, blue, red edge, and near infrared bands, respectively in the above formulas. environment for the 2nd ratoon (CVs3), untested cultivar in tested crop/ environment for the 3rd ratoon (CVs4), and untested cultivar in untested crop/environment (CVs5) (Fig. 2). ...
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Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R 2 = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R 2 = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R 2 = 0.69, RMSE = 10,975 stalks ha − 1). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally , SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly , the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.
... (c) Spatial analysis, land cover classification and landscape heterogeneity assessment First, we extracted the normalized difference vegetation index (NDVI) for each point where a recorder was located. This metric is used in remote sensing to assess vegetation density and greenness based on the reflection of light [29]. To classify land cover, we applied the Corine LandCover classification scheme (the most used land cover system in the country for land cover classification) to categorize satellite imagery obtained from the PlanetScope satellite constellation (Planet Labs) for the year 2020, with a spatial resolution of 3 m (1:10 000). ...
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As agriculture continues to expand in tropical regions, effectively preserving biodiversity will rely not only on protected areas but also on sustainable practices within agricultural landscapes. Studying biodiversity changes associated with landscape management is essential for refining practices that support ecosystem resilience and species conservation, but collecting the data required to draw strong conclusions remains challenging because of the high spatial and temporal resolution needed. In this context, passive acoustic monitoring (PAM) offers a valuable solution by enabling researchers to gather continuous, high-resolution biotic signals over time. Using PAM, our objective was to examine how changes in landscape characteristics, specifically heterogeneity and composition, correlate with soundscape patterns including acoustic activity and beta diversity. We collected data across 52 000 ha in the Magdalena River Valley, Colombia, a biodiversity hotspot significantly transformed by palm oil monocultures. We contrasted soundscape data, including acoustic activity and soundscape turnover, with landscape metrics derived from Geographical Information Systems analysed satellite imagery, focusing on landscape composition and configuration. Our analysis showed that compositional and heterogeneity-related landscape variables, such as the proportion of natural cover (NC) and patch shape, were associated with differences in acoustic activity and soundscape homogenization. Specifically, patches with uniform shapes and a lower NC correspond to more homogeneous soundscapes with higher acoustic activity. In contrast, patches with irregular shapes and a higher NC were linked to more heterogeneous soundscapes and lower acoustic activity. By linking soundscape patterns with landscape metrics, we highlight the importance of retaining natural habitat features within productive areas to support acoustic diversity, and by extension, ecological resilience. This article is part of the theme issue ‘Acoustic monitoring for tropical ecology and conservation’.
... Aplicações derivadas do sensoriamento remoto em estudos da paisagem utilizando índices de estrutura da vegetação, por meio de imagens, são novas ferramentas que auxiliem em estudos com finalidade de acompanhar e compreender processos em níveis regionais à globais, ou seja, avaliar mudanças nas paisagens (SOARES-FILHO, 1998;LUZ, 2002). O NDVI, desenvolvido por Rouse et al. (1973), tem como importância a capacidade de monitorar as mudanças sazonais e interanuais. O NDVI é preciso ao analisar as alterações sofridas pela cobertura vegetal ao longo do tempo (LIMA et al., 2015), sendo o mais utilizado em estudos com a cobertura vegetal, por detectar as condições da vegetação e sua dinâmica têmporoespacial, facilitando o monitoramento sazonal e variações de longo prazo (WANG et al., 2003). ...
Article
The regional landscape encompassed by Rio Grande basin, on Western Bahia, has on its Chapadas, in the erosive retreat fronts, the Evergreen Valley forest between the mosaics composed of forest and savanna phytophysiognomies. Therefore, this study intends to evaluate the spatial distribution of the Evergreen Valley forest occurrence, what are the conditions that influence the local landscape, and investigate the possible changes in this scenario at a spatial-temporal intermission. The methods was based on the use of products from the mission SRTM to the development of the geomorphological maps of the areas where the Evergreen forest is found on the landscape, around the river basin, towards the use of NDVI, to compare the variations on vegetation cover during 40 years, since 1975. Moreover, a stratigraphic profile of the site was done in order to obtain information about the local geological structure, and a land profile under the values of NDVI to assess the vegetative strength at the Evergreen forest area throughout the length of the stain. The stratigraphic profile indicated the presence of low porosity rocks along the forest occurrence altimetry, indicating a favorable geomorphological condition to its development.
... Numerous research articles focus on urban ecology assessment, including the monitoring of built-up and vegetation [26,27], urban heat islands (UHIs) [28,29], water resource management [30], air quality regulation [31], and biodiversity monitoring [32]. These studies underscore the effectiveness of remote sensing methods in urban ecology monitoring [33,34], employing various indices such as NDVI [35], LST [36], BI [37], EVI [38], NDBI [39], SAVI [40], and MNDWI [41]. However, Xu [42] introduced the remote sensing based ecological index (RSEI) to observe and evaluate regional ecological conditions. ...
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Assessing the ecological environmental quality (EEQ) is crucial for protecting the environment. Dhaka's rapid, unplanned urbanization, driven by economic and social growth, poses significant eco-environmental challenges. Spatiotemporal ecological and environmental quality changes were assessed using remote sensing based ecological index (RSEI) maps derived from Landsat images (1993, 2003, 2013, and 2023). RSEI was based on four indicators-greenness (NDVI), heat index (LST), dryness (NDBSI), and wetness (LSM). Landsat 5 TM and 8 OLI/TIRS images were processed on Google Earth Engine (GEE), with principal component analysis (PCA) applied to determine RSEI. The findings showed a decline in the overall RSEI (1993-2023), with low-and very low-quality areas increasing by about 39% and high-and very high-quality areas decreasing by 24% of the total area. NDBSI and LST were negatively correlated with RSEI, except in 1993, while NDVI and LSM were generally positive but negative in 1993. The global Moran's I (0.88-0.93) indicated strong spatial correlation in the distribution of EEQ across Dhaka. LISA cluster maps showed high-high clusters in the northeast and east, while low-low clusters were concentrated in the northwest. This research examines the degradation of ecological conditions over time in Dhaka and provides valuable insights for policymakers to address environmental issues and improve future ecological management.
... The combination of the normalised difference formulation with the regions exhibiting the highest chlorophyll absorption and reflectance renders it a suitable tool for a wide range of conditions. However, saturation may occur in areas of dense vegetation when LAI (leaf area index) values are high (Rouse et al., 1973). ...
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The existence of an extensive Gräberstraße-type necropolis in the Roman city of Segobriga is confirmed by the funerary-type structures located 2,400 m from the city and by the excavation of five funerary monuments located along its main entrance/exit road. The inscriptions, sculptures and architectural remains of funerary character exhumed prove, in addition, its use by members of the higher social classes, including wealthy freedmen. Until now we did not know the spatial structuring of the monumenta and their relationship with each other and with the road. This information is vital to know the internal topographic organisation and the constructive density of the necropolis. Geophysical surveys with ground penetrating radar (GPR) and multispectral images captured with unmanned aerial vehicles (UAV) have recently been carried out in order to improve our knowledge in this field. They have been developed within the framework of an ongoing research project to study the northern suburb. The objective of these surveys was to identify new funerary monuments not visible on the surface along the route of the road. This paper analyses the methodology and processing of the two techniques used. It also evaluates their comparative applicability to detect buried remains in calcareous soils. The data obtained indicate the presence of mausoleums on both sides of the roadway according to the Italic model of funerary viae. Those located in the first line form two continuous rows, while isolated monuments are located at the rear. This model prevailed in the cemetery areas of the Western Roman Empire from the end of the 1st century BC onwards.
... NDVI [47] ρ nir (·)−ρ r (·) ρ nir (·)+ρ r (·) ...
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This document presents a low-cost, open-source, robust, and user-friendly Unmanned Aerial System for precision agriculture tasks. The proposed methodology for data collection and image processing is flexible and adaptable to most basic systems. Besides, a new vegetation index is defined for temperature estimation on vegetation without thermal imagery as usually done by other related systems. The system also features a control algorithm that is robust against external disturbances. The output information is used then to estimate the vegetation's temperature. The experiments presented in this work are enclosed to corn fields.
... [−1, + 1] ( Rouse et al., 1974 ) Green Normalized Difference Vegetation Index Gitelson et al. (1996) Generalized Difference Vegetation Index ...
... 10.1029/2024JG008421 Measures green vegetation health and density (Rouse et al., 1974). ...
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Wetland ecosystems are critical to global carbon and nitrogen cycles. This study leverages unmanned aerial system (UAS)‐based hyperspectral imaging to quantify soil organic matter (SOM), total carbon (C), and total nitrogen (N) in moderately to densely vegetated salt marshes at the Virginia Coast Reserve Long‐Term Ecological Research (VCR‐LTER) site. We utilized elastic net (ENet) regression and gradient‐boosted regression trees (GBRT) within a hybrid modeling framework to predict these soil properties using features from the visible to near‐infrared (VNIR) and shortwave infrared (SWIR) spectral ranges. Validated through a 1,000‐iteration bootstrap analysis, the hybrid model demonstrated robust predictive capabilities. The model achieved mean normalized root mean square error of 0.118 for SOM, 0.127 for C, and 0.138 for N, with corresponding mean R2 R2{R}^{2} values of 0.874, 0.865, and 0.822, respectively. These outcomes highlight the efficacy of integrating advanced statistical methods with high‐resolution remote sensing data to enhance soil property estimation in ecologically sensitive areas.
... Gitelson et al. (2003),Rouse et al. (1974) e Miranda et al. (2020. ...
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Technological advancements have reduced geographical barriers, demanding rapid adaptation across all sectors, including the use of remotely piloted aircraft (RPAs), commonly known as drones, which have been widely applied in areas such as urban and agricultural monitoring. In municipalities like Jataí-GO, where agribusiness is predominant, the use of RPAs for measuring vegetative indices (VIs) brings benefits for more precise and sustainable agricultural practices. However, the cost of some RPAs limits their broader adoption, as many devices can exceed R$ 160,000 (Brazilian currency). This research is experimental and aimed to develop techniques for using rotary-wing drones, which are more affordable than other models available on the market, to generate vegetation indices for mapping and monitoring commercial grain crops. The methodology consisted of: 1) field surveys in soybean planting areas; 2) acquisition of images from the Sentinel-2 satellite and the drone; 3) processing of images captured by the Phantom 4 PRO drone, equipped with a MAPIR Survey 3W camera; and 4) analysis of correlations between the VIs obtained by the drone and Sentinel-2 in two areas on the campus of the Federal University of Jataí (UFJ), for validation. Data collection included the use of a high-precision Trimble R4s GNSS for georeferencing. The results show a strong correlation between the VIs calculated using drone and Sentinel-2 images for soybean crops, with coefficients of determination (R²) exceeding 0.72 for VARI and 0.73 for NDVI. Plot 001 stood out for its uniformity, achieving high coefficients of determination, reinforcing the effectiveness of the geotechnologies employed. In more heterogeneous areas, such as plot 002, the VIs revealed differences, particularly with the VARI index, which proved less efficient for mixed cultivation environments. The data confirms that VIs generated by cost-effective drones can indicate planting variations, demonstrating the feasibility of their application in the agricultural sector without significant investments.
... The urban greening map was generated by means of the normalized difference vegetation index (NDVI) applied to CBERS-4A images, previously subjected to an atmospheric correction using the Dark-Object Subtraction (DOS) method [53,54]. This index, proposed by [55], is a metric meant to quantify biomass, vegetation health, and density. Thus, the NDVI is also capable of highlighting the presence or absence of vegetation, calculated from the normalized difference between the red (highly absorbed by healthy vegetation to fuel photosynthesis and create chlorophyll) and near-infrared (which is strongly reflected by healthy plants) spectral bands [56]. ...
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Analyzing the population’s access to ecosystem services offered by urban greening constitutes a measure of environmental justice, as it directly affects the quality of life and health of the population living in cities. This article is committed to proposing a geoenvironmental model in a geographic information system (GIS), envisaged to estimate the share of urban forests and green spaces in territorial planning units (TPUs), corresponding to neighborhoods of a pilot city, using high-spatial-resolution images of the China–Brazil Earth Resources Satellite (CBERS-4A) and the normalized difference vegetation index (NDVI). These data were combined by means of a Boolean analysis with social vulnerability indicators assessed from census data related to income, education, housing, and sanitation. This model ultimately aims to identify priority areas for urban afforestation in the context of environmental justice and is thus targeted to improve the inhabitants’ quality of life. The municipality of Goiânia, the capital of Goiás state, located in the Brazilian Central–West Region, was chosen as the study area for this experiment. Goiânia presents 19.5% of its urban territory (82.36 km2) covered by vegetation. The analyses indicate an inequity in the distribution of urban forest patches and green areas in this town, where 7.8% of the total TPUs have low priority, 28.2% have moderate to low priority, 42.2% have moderate to high priority, and 21.8% have high priority for urban afforestation. This urban greening imbalance is particularly observed in its most urbanized central nuclei, associated with a peripheralization of social vulnerability. These findings are meant to support initiatives towards sound territorial planning processes designed to promote more sustainable and equal development to ensure environmental justice and combat climate change.
... The calculations were performed using the R programme. The NDVI was calculated according to the formula by Rouse et al. (1973). ...
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This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The analysis integrates forest inventory data collected from permanent plots monitored between 2011 and 2019 with Landsat satellite imagery processed through the Google Earth Engine platform. By incorporating surface reflectance and vegetation indices, the approach significantly improved the accuracy of productivity estimates while reducing the costs and efforts associated with traditional field-based methods. The Random Forest model achieved a strong performance (R2 = 0.8867; RMSE = 0.87), and its predictions were further refined using post-processing correction factors. These results demonstrate the potential of data-driven modeling to support forest monitoring and sustainable management practices, especially in ecosystems vulnerable to the impacts of climate change.
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