NDVI Maps Research Articles

Assessment of Waterlogging and Mitigation Strategies for Alanda Micro Watershed Using Remote Sensing in Akola District of Vidarbha Region, India

The socio-economic progress of Vidarbha, where 89% of the land is used for rainfed farming, is significantly influenced by water resources. Unfortunately, waterlogging, driven by insufficient drainage and erratic weather conditions, poses serious challenges to agricultural productivity and soil health. The Alanda micro watershed in Barshitakli tehsil, Akola district, Vidarbha region of Maharashtra, features hard basaltic terrain and clay soils primarily used for agriculture. Poorly drained soils have caused waterlogging and salinization, highlighting the need for assessment of mitigation measures for the waterlogging to improve agricultural productivity and address climate change impacts. Waterlogging can be effectively identified through remote sensing techniques like NDWI, NDMI and NDVI, using high-resolution Landsat 8 imagery processed in ArcGIS 10.8. These methods facilitate the analysis and management of waterlogged areas, which is critical for improving agricultural output. The NDVI, NDWI and NDMI maps of the Alanda micro watershed highlight vegetation health, moisture levels and waterlogging potential, with the northern regions showing higher moisture and increased waterlogging risk. Mitigating waterlogging in the area requires the implementation of efficient drainage systems, soil improvement through organic matter and deep tillage and precise water management practices, including levelling fields to prevent water accumulation and using drought-resistant crops.

Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies

The use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019–2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering (mclust) was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.

Assessment of vegetation change using NDVI, LST, and carbon analyses in Çankırı Karatekin University, Turkey.

Due to a rising population and urbanization, the green areas have been decreasing in cities, with a negative impact on air pollution, human health, and ecosystem. As part of the urban environment, university campuses contribute to urban ecosystem with their vegetation. This study aims to (1) assess the change of vegetative land cover of the Çankırı Karatekin University in Turkey and (2) evaluate its benefits to the ecosystem in terms of carbon sequestration, storage, and improvement of air quality by means of a simulation. In the study, the density and vegetation change were assessed with NDVI and LST analyses in ArcGIS; carbon emissions and air pollution benefits were estimated in i-Tree Canopy tool. The study showed that the healthy vegetation consisting of trees/shrubs and grass/herbaceous, which was 32.2% (28ha) in 2000, increased to 85% (74ha) in 2020 NDVI maps, and the surface temperature also increased between 2000 and 2020 in LST maps.The rise in vegetation as grass/herbaceous areas instead of trees/shrubs and the use of impervious buildings/roads on the land surface increased the land surface temperature. As a result of the analyses in the i-Tree-Canopy tool, it was estimated that the trees/shrubs and grass/herbaceous vegetation canopy covering 31.42% of the study area removed a total of 512,845.65g of pollutant gas and particles from the air, 20.79 tonnes of carbon sequestered annually, and 522.01 tonnes of carbon stored by vegetative land cover. In the simulation, where 32.62% soil/bare ground areas were converted to trees/shrubs in order to improve vegetation cover in the area, it was determined that it contributed 5 times more to the ecosystem service value for removing pollutants from the air, carbon storage, and improving the ecosystem. It was revealed that the vegetative land cover formed by tree/shrub species should be increased in the campus in the future. The study method model serves as a tool for planning and designing eco-friendly urban environment.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning.

Forest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017-2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence.

Estimativa da Evapotranspiração da Soja Pelo Modelo SSEBop Utilizando Imagens de Alta Resolução Obtidas Por Aeronave Remotamente Pilotada

Abstract Evapotranspiration (ET) is one of the most important processes in the hydrologic cycle, constituting the main responsible for water losses at the surface. Several evapotranspiration models use information from surface temperature and vegetation indices captured by remote sensors such as MODIS and LANDSAT to estimate the ETc value. The objective of this study is to apply SSEBop model to estimate ETc of soybean in a field experiment under four water regimes, using high-resolution multispectral and thermal images collected from remotely piloted aircraft (RPA). Surface temperature and NDVI maps were generated as sources for evapotranspiration estimation. From a Python script, spatial variability maps of ETc were generated at different phenological stages of the crop. The quality of the model for ETc estimates was performed by comparing the modeling results with leaf transpiration data measured in the field using an infrared gas analyzer, whose results showed a good correlation (R2 = 0.76). These results demonstrated the possibility of transferring a model originally developed for processing low to medium-resolution satellite images to high-resolution spatial-temporal images acquired by RPA with small adaptations in the original algorithm, generating great potential for new studies on an experimental and field scale.

Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam

Abstract Normalized difference vegetation index (NDVI) is a conditioning factor that significantly affects slope stabilization, as the low vegetation coverage can create conducive conditions for landslide occurrence. In previous studies, NDVI was often calculated from long-term average NDVI maps or specific yearly NDVI maps. However, this approach is unsuitable due to the time-varying nature of these data, influenced by numerous factors, including human activities. To solve this problem, this study uses NDVI as a time-varying factor. NDVI maps are generated from Sentinel 2 and Landsat_8 imagery at the onset of each rainy season between 2015 and 2020 in the mountainous region of Quang Ngai Province. Moreover, the landslide events that occurred within this 5-year period (2016–2020), along with a set of conditioning factors, are utilized to develop landslide susceptibility models based on three algorithms: logistic regression, support vector machine, and extreme gradient boosting (XGBoost). The obtained results demonstrate that using time-varying NDVI shows superior performance compared to using only NDVI from 2015. The outcomes also indicate that XGBoost is the most effective model. Selecting suitable NDVI maps can improve the predictive accuracy of landslide susceptibility mapping.

Uncertainty assessment of Sentinel-2-retrieved vegetation spectral indices over Europe

ABSTRACT Vegetation spectral indices (VIs) from multispectral remotely sensed imagery provide useful information in several sectors, especially if longing for change detection analyses or land monitoring. In this context, estimating uncertainty of VI values is crucial to recognize significant differences in both space and time domains. Unexpectedly, most applications reported in literature and involving VI do not take care about this issue, thus making unreliable a significant part of deductions. In this work, authors present an approach aimed at mapping in time and space the theoretical uncertainty of some widely used VIs basing their approach on the so-called variance propagation law (VPL). VPL can be consequently used to get an estimate of the theoretical VI uncertainty, starting from one of the bands involved in VI computation. VI uncertainty all along the year 2020 was then mapped at pixel level by Google Earth Engine over the whole Europe to test seasonal trends. Uncertainty of VI differences, as possibly resulting from a change detection approach, was tested by comparing monthly composites of VI and computing the expected uncertainty of differences along the year. An example was reported involving two NDVI maps (June–September) proving that about 30% of ΔVI were not significant.

Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel‐2 NDVI time-series images in an organic farming system

The study investigates the suitability of time series Sentinel-2 NDVI-derived maps for the subfield detection of a sunflower crop cultivated in an organic farming system. The aim was to understand the spatio-temporal behaviour of subfield areas identified by the K-means algorithm from NDVI maps obtained from satellite images and the ground yield data variability to increase the efficiency of delimiting management zones in an organic farming system.Experiments were conducted on a surface of 29 ha. NDVI time series derived from Sentinel-2 images and k-means algorithm for rapidly delineating the sunflower subfield areas were used. The crop achene yields in the whole field ranged from 1.3 to 3.77 t ha−1 with a significant within-field spatial variability. The cluster analysis of hand-sampled data showed three subfields with achene yield mean values of 3.54 t ha−1 (cluster 1), 2.98 t ha−1 (cluster 2), and 2.07 t ha−1 (Cluster 3). In the cluster analysis of NDVI data, the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability. The best period for identifying subfield areas starts from the inflorescences development stage to the development of the fruit stage. Analyzing the NDVI subfield areas and yield data, it was found that cluster 1 covers an area of 42.4% of the total surface and 50% of the total achene yield; cluster 2 covers 35% of both surface and yield. Instead, the surface of cluster 3 covers 22.2% of the total surface with 15% of achene yield. K-means algorithm derived from Sentinel-2 NDVI images delineates the sunflower subfield areas.Sentinel-2 images and k-means algorithms can improve an efficient assessment of subfield areas in sunflower crops. Identifying subfield areas can lead to site-specific long-term agronomic actions for improving the sustainable intensification of agriculture in the organic farming system.

Detection of Soil Erosion Hotspots in the Croplands of a Typical Black Soil Region in Northeast China: Insights from Sentinel-2 Multispectral Remote Sensing

Global efforts to restore the world’s degraded croplands require knowledge on the degree and extent of accelerated soil organic carbon (SOC) loss induced by soil erosion. However, the methods for assessing where and to what extent erosion takes place are still inadequate for precise detection of erosion hotspots at high spatial resolution. Drawing on recent advances in multitemporal Sentinel-2 remote sensing to create a bare soil composite that reflects erosion-induced variations in soil spectral signatures, this study attempted to develop a spectra-based soil erosion mapping approach to pinpoint eroded hotspots in a typical catchment located in the black soil region of northeast China as characterized by undulating landscapes. We built a ground-truth dataset consisting of three classes of soils representing Severe, Moderate and Low erosion intensity because of their inter-class contrasts in estimated erosion rates from 137Cs tracing. The spectral separability of different erosion classes was first tested by a combined principal component and linear discriminant analysis (PCA-LDA) against laboratory hyperspectral data and then validated against Sentinel-2-derived broadband spectra. The results show that PCA-LDA produced excellent classification accuracy (Kappa coefficient > 0.9) for both data sources and even more so for Sentinel-2 spectra, highlighting the effectiveness of the multitemporal approach to extract bare soil pixels. Further investigations into the spectral curves enabled identification of distinctive spectral features representative of shifting soil albedo and biochemical composition due to erosion-induced SOC mobilization. A classification scheme comprising the spectral features was applied to the Sentinel-2 bare soil composite for pixel-wise soil erosion mapping, in which 15.9% of the cropland area was detected as erosion hotspots, while the Moderate class occupied 65.4%. Comparing the erosion map to a NDVI map demonstrated the negative impact of soil erosion on crop growth from a spatial perspective, highlighting the potential of the proposed approach to aid targeted cropland management for food security and climate.

INCREASING THE SPATIAL ACCURACY OF THE LAND USE MAP USING FUSION OF OPTICAL AND RADAR IMAGES OF SENTINEL AND GOOGLE EARTH ENGINE

Abstract. Nowadays, exact and real-time land use and land cover (LULC) maps are imperative to supply precise data for energetic observing, arranging and arrive administration. With the appearance of cloud computing frameworks, time arrangement includes extraction methods, and machine learning classifiers, unused openings emerge in more precise and larger-scale LULC mapping. In this research, the aim is to obtain a land use map with a spatial resolution of 10 meters by combining Sentinel-1 radar images and Sentinel-2 optical images. The processing was done in Google Earth Engine cloud computing system. Also, in this research, NDVI map and NDBI map were used to increase classification accuracy. Classification was done using the decision tree method and in eleven classes. To evaluate the classification accuracy and the final map, randomly collected points were used in the study area. The results of the comparison of the ground collection points and the final classified map showed that the classification accuracy in this study was around 94%. The results obtained from such as research can be optimally used in urban planning and crisis management.

Analytic Hierarchy Process (AHP) Based Soil Erosion Susceptibility Mapping in Northwestern Himalayas: A Case Study of Central Kashmir Province

The Kashmir Valley is immensely susceptible to soil erosion due to its diverse topography and unstable geological formations in the Himalayan region. The present study helps in assessing the spatial distribution and prioritizing soil erosion zones in the Central Kashmir region covering the Sindh and Dachigam catchments. The study implemented the GIS-based analytic hierarchy process (AHP) and weighted sum method (WSM) using datasets of precipitation, geological map, soil map, and satellite imagery and derived eleven factors (topographical derivatives, LULC, soil, drainage, rainfall, lithology, wetness index and greenness of an area). The ratings and weightage were proven to be unbiased and reliable based on the observed value of the consistency ratio (CR) (i.e., 0.07). The study depicts 41% of the total area to be extremely vulnerable to soil erosion. The slope varies from 0–62° with mean of 22.12°, indicating 467.99 km2 (26%) and 281.12 km2 (15%) of the area under high and very high susceptible zones, respectively. The NDVI and NDWI maps indicate soil erosion severity covering an area of 40% and 38%, respectively, in highly susceptible zones. High drainage density and curvature zones were observed in 18.33% and 22.64% of the study area, respectively. The study will assist in the planning and implementation of conservation measures.

Spatial estimation and climate projected change of cover-management factor in semi-arid region of India

Soil erosion and its future projection play an important role in the changing climatic scenario. An attempt has been made to establish a relationship between NDVI and cover-management factor (C-factor) which varies with area and crop. IRS LISS III data with 23.5 m resolution is used to derive precisely NDVI and correlated with C-factor, the derived regression equation shows a good relationship between NDVI and C-factor value. An approach was made to use this area specific equation for getting C- factor for the whole Mahabubnagar district, Telangana from 2013 imagery data and the study was conducted in 2017. For future scenarios study, a relationship between rainfall, temperature, and NDVI of the whole district was derived. The NDVI value of the study area varies between 0.63 and 0.046. Based on the regression model C-factor for the future scenarios using available CMIP5 (Coupled Model Intercomparison Project-5) has been mapped for the whole district. Using the NDVI map, the C-factor map of the area was prepared in ArcGIS 10.0 software. The spatial distribution shows that the C-factor values varied between 0.52-0.86. On an average C- factor for 2020s, 2050s and in 2080s were 0.53, 0.76, and 0.77 respectively. The predicted C- factor values were then brought under the GIS environment and C- factor prediction maps were generated.

Detection and counting of meadow cuts by copernicus sentinel-2 imagery in the framework of the common agricultural policy (CAP)

ABSTRACT Greening is a subsidy provided by the Common Agricultural Policy (CAP), related to mowing and designed to protect environment. National or regional paying agencies (PP) monitor and verify compliance of farmers’ declarations with CAP rules. In this work, an operational procedure is proposed aimed at supporting PPs in detecting, mapping and quantifying the number of times mowing occurred in a meadow field. In particular, 72,539 meadows fields within the Piemonte region (NW – Italy) were analysed with a time series of Sentinel-2 (S2) data. The procedure is based on the processing of filtered and regularized time series of NDVI maps. The Fast Fourier Transform (FFT) was applied at field level to decompose the local NDVI temporal profile. The frequency ( ω p e a k ) corresponding to the maximum amplitude ( A p e a k ) was therefore considered. A p e a k value was used to detect not-mowed meadows by thresholding based on the application of the non-parametric Kolmogorov-Smirnov test. Mowing counting were achieved with reference to ω p e a k and the correspondent map (called MCM) generated for the study area. MCM was, finally, tested against the validation set (285 fields). Results showed an Overall Accuracy (OA) > 87%, confirming the effectiveness of the proposed procedure in detecting, mapping and quantifying the number of times mowing occurred.

Responses of NDVI to Climate Change and LUCC along Large-Scale Transportation Projects in Fragile Karst Areas, SW China

The fragile karst habitat is extremely sensitive to human activities such as large-scale engineering construction. To explore the influence of the construction and operation of the GH (Guiyang-Huangguoshu) highway on the vegetation within a certain range and the response of NDVI to climate factors, Landsat data were used to synthesize annual NDVI maps using the maximum value compositing method. Trend, correlation, and coefficient of variation analyses were performed. The results demonstrate that: (1) During the construction and operation periods, NDVI showed an overall upward trend, and the NDVI value and growth rate in the contrast area were greater than those in the core area; (2) the correlation between temperature and vegetation cover along the GH highway was stronger than that between precipitation and vegetation; (3) construction of the GH highway has had a significant impact on the surrounding vegetation, with the impact on vegetation ecology along the road mainly concentrated within the 2 km range. The increase of artificial surfaces along the road has had a great impact on the NDVI, and the vegetation cover change in the core area is more significant than that in the contrast area; and (4) the overall disturbance of the GH highway project to the surrounding ecology was mainly observed in the form of low and medium fluctuations. This study aims to provide a reference for environmental assessment and management in karst areas.

Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.

Potential of functional analysis applied to Sentinel-2 time-series to assess relevant agronomic parameters at the within-field level in viticulture

• Functional analysis methods were able to decompose the yearly trend and spatial structure of the vineyards. • Performance of PCA declines if more than three componets are added. • The results are linked to soil characteristics and other key variables, highlighting several phenomena affecting the vineyard. Sentinel-2 satellite imagery offers a wealth of spectral information combined with a weekly temporal resolution. It is seen as a promising tool to extract spatial information about vineyards and link them to agronomic parameters. Usually, only one or a few images are commonly employed at specific stages like veraison in viticulture. Extracting further information from time-series images may be of interest; however, this remains an issue due to the noisy and complex nature of extracted time-series. The functional analysis proposes a robust continuous representation of these time-series, which can then be used with adapted statistical tools. This paper focuses on extracting relevant information at the within-field level on two vineyards in Spain, which can be jointly interpreted with field observations and measurements. More precisely, it discusses the use of popular linear dimensionality reduction techniques, namely Principal Component Analysis (PCA) and Partial Least Square (PLS), adapted to functional data in order to decompose NDVI time-series into a weighted sum of several functional components. The unsupervised methods, like PCA, decomposed the spatial structure within the vineyards using a few components, resulting in a better and more manageable dataset than the one obtained using simple non-constrained methods. The results show significant correlations with ground-truth data showing the added value of considering the whole NDVI temporal series compared to a single NDVI map at veraison. The proposed approach provided helpful information about each component's yearly trend. Moreover, the results are linked to grapevines' seasonal phenology and management practices, highlighting phenomena affecting the vineyard's development. This method is particularly suited for interactions with field experts, who may derive relevant agronomic information from the decomposition maps.

Use of Sentinel-2 Satellite for Spatially Variable Rate Fertiliser Management in a Sicilian Vineyard

Satellites can be used for producing maps of within-field crop and soil parameters and, consequentially, spatially variable rate crop input application maps. The plant vegetative vigour index (i.e., Normalised Difference Vegetation Index—NDVI) and the leaf water content index (i.e., Normalised Difference Water Index—NDWI) maps were used to study—through both time and space—the phenological phases of two plots, with Syrah and Nero d’Avola grapevine varieties, in a Sicilian vineyard farm, located in Naro (Agrigento, Sicily, Italy). The aim of this work is to produce spatially variable rate nitrogen fertiliser maps to be applied in the two vineyard plots under study as well as to understand when they should be fertilised or not according to their target crop yields. The average plant vegetative vigour and leaf water content of both the plots showed a high temporal and spatial variability during all phenological phases and, according to these results, the optimal fertilisation time should have been 12 April 2021. In fact, this crop operation is aimed at supporting the vegetative activity but must be performed when the soil water and, therefore, the plant leaf water content are high. Therefore, spatially variable rate fertilisation should have been performed around 12 April 2021 in both plots, using previous NDVI maps and taking into consideration two management zones. This work demonstrates the usefulness of remote sensing data as Decision Support Systems (DSS) for nitrogen fertilisation in order to reduce the production cost, environmental impact and climate footprints per kg of produced grapes, according to the European Green Deal challenges.

UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable.

DESIGN OF OPTIMIZING THE APPLICATION OF THE MANURE IN HIGH NATURAL VALUE (HNV) GRASSLANDS, IN SUSTAINABLE AGRICULTURE, BASED ON NDVI

In many countries, especially in Eastern Europe, there is much discussion about how grasslands are managed in order to prevent pollution of soils, rivers, lakes and to maintain and conserve wild flora and fauna. These objectives favor the maintenance of high biodiversity in High Natural Value (HNV) grasslands through their extensive use, with a low intake of organic fertilizers in line with EU CAP policies. In Romania, in the High Natural Value (HNV) grasslands, subsidies are allocated for "environmentally friendly practices", through the Agri-environment and climate program, but the amount of organic fertilizer is restricted up to 40 kg nitrogen active element/ha regardless of the type fertilizer) but also their administration. Currently, through remarkable advances in areas such as GIS and remote sensing, applications for agriculture provide complex information with high accuracy, but also the ability to provide predictions for various parameters. In this context, the paper aims to optimize the application of manure in HNV grasslands, in accordance with Agri-environment policies, by improving and adapting in practice the OneSoil application based on NDVI map generation from Sentinel satellite images. The validation of the "adjustments" brought to the application was done by field visits with the Phantome 4 UAV equipment. An experimental site was considered an HNV grassland located in Brădişoru de Jos (ATU Oraviţa) which has an area of 391 ha. In the study area the vegetation is mosaic, the grassland being fragmented by hedges, forest curtains and trees in clumps that will be excluded from fertilization. The usable area for fodder at the level of 2020 was 265.41 ha, but in the absence of cleaning works in the coming years, there is a risk of substantial reductions. The proposed optimization model is finally presented in the form of a “coverage” map that indicates both the required quantity, differentiated according to the vegetation characteristics suggested by NDVI, and the spatial location of these quantities on subzones. Thus, on an area of 60.73 ha, 4 t.ha-1 of manure are required, on 94.66 ha it is necessary to administer 5 t.ha-1 of manure, and on an area of 110.02 ha, where the vegetation of the grassland suffers, it is necessary to administer 8 t.ha-1 of cattle manure. The major advantage of this model is that the user can import, on different devices (phone, tablet or laptop) vector files with plot outline or GPS points for location, can differentiate the dose of fertilizer on the surface of the plot, under specific conditions and has at its disposal the map with the "location" of the different quantities of fertilizers.