Normalized Difference Vegetation Index Maps Research Articles

Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Gelephu, Bhutan

Gelephu, located in the Himalayan region, has undergone significant development activities due to its suitable topography and geographic location. This has led to rapid urbanization in recent years. Assessing land use land cover (LULC) dynamics and Normalized Difference Vegetation Index (NDVI) can provide important information about urbanization trends and changes in vegetation health, respectively. The use of Geographic Information Systems (GIS) and Remote Sensing (RS) techniques based on various satellite products offers a unique opportunity to analyze these changes at a local scale. Exploring Bhutan’s mandate to maintain 60% forest cover and analyzing LULC transitions and vegetation changes using Sentinel-2 satellite imagery at 10 m resolution can provide important insights into potential future impacts. To examine these, we first performed LULC mapping for Gelephu for 2016 and 2023 using a Random Forest (RF) classifier and identified LULC changes. Second, the study assessed the dynamics of vegetation change within the study area by analysing the NDVI for the same period. Furthermore, the study also characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentinel-2 satellite data, which was then used for pre-and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. The results serve as a basis for strategy adaption required to examine the environmental protection and sustainable development management, and the policy interventions to minimize and balance the ecosystem, taking into account urban landscape.

AGRO-ECOLOGICAL CLASSIFICATION OF AGRICULTURAL LAND USING MACHINE LEARNING, GIS AND REMOTE SENSING DATA

The paper is devoted to agro-ecological classification of agricultural land using modern methods of geo-information data analysis and machine learning.
 Background. There are few works in the literature, which cover the issues of accuracy of machine learning (MLL) models for agro-ecological grouping of agricultural land. A large number of raster information layers are used to improve the accuracy of land classification from satellite images. This considerably increases the time of training and testing MMOs, producing thematic maps of agricultural land classification. This approach requires considerably high computing resources and a considerable amount of computer RAM. Raster GIS data models occupy a much larger volume than vector models.  In this regard, research on automated agro-ecological (grouping, classification) of agricultural land using vector GIS models is of practical importance.
 Purpose.  The aim of the study is to apply GIS methods, remote sensing (ERS) data and machine learning methods for agricultural land grouping.
 Materials and methods.  The materials used were synthesized multispectral high spatial resolution Sentinel-2A images, maps of vegetation indices NDVI (Normalized Difference Vegetation Index), OSAVI (Optimized Soil Adjusted Vegetation Index), EVI2 (Enhanced Vegetation Index2), NDWI (Normalized Difference Water Index), SAVI, PVI, GDVI, MCARI, NDRE, TSAVI; topographic map, ALOS DSM (30 m/pixel) and ALOS PALSAR (12.5 m/pixel) satellite images, soil map and field survey results. Field measurements were carried out using the Triump-2 satellite geodetic receiver and included determination of coordinates of characteristic points of land plot boundaries, relief elements, and soil survey.
 Digital spatial model of land use was created using GIS ArcGIS and QGIS, Python engineering libraries were used in the machine learning process.
 Results. Agro-ecological grouping of lands was realized by the example of the farm "Zerno Sibiri" of Novosibirsk region using the following machine learning methods: Random Forest (RF) method, Decision Tree (DT), k-nearest neighbours method (KNN). The best accuracy is the RF machine learning model. The accuracy of the model averaged 97.9% (with training 99.9%, testing 98.8%, and cross validation 95.0%). The Root Mean Square Error (RMSE) is 0.006: for training sample 0.001; test sample 0.076; validation sample 0.123 respectively). The mean kappa coefficient was 0.97 (1.00 for the training sample; 0.982 for the test sample, and 0.927 for the validation sample). 
 Conclusion. The offered method of agro-ecological grouping of agricultural lands by means of GIS, RS data and machine learning methods enabled to distinguish informative quantitative indicators of the relief. The main essence of the proposed method is to create a machine learning model (MLM) based on a spatial dataset.  The spatial dataset is formed using geoinformation analysis methods and includes: geomorphometric maps, maps of agrometeorological parameters, soil map, on-farm land management map and operational-territorial units of land classification. The application of vector data model allowed for agro-ecological grouping of agricultural lands in automated mode, to accelerate labor-intensive process of raster data recognition, to increase objectivity of the work. The suggested method of agro-ecological agro-ecological grouping of lands allows taking into account the totality of relief and soil-ecological conditions indicators with the help of geoinformation analysis methods, remote sensing and machine learning.

Assessment of Spatio-Temporal changes of Forest Cover using Remote Sensing techniques in Pavagadh Region, Gujarat State

For effective forest management, it is essential to consider forest patterns and periodic changes in forest cover. Several spectral vegetation indices derived from multi-temporal Remote Sensing data are useful to track the changes over time. The major objective of this study was to monitor the changes in forest cover during the past three decades in the Pavagadh area of Panchmahal district, Gujarat State, India. Various indices like Normalized Difference Vegetation Index (NDVI), Land Surface Temperature, and Urban Thermal Field Variance Index (UTFVI) with the Ecological Evaluation Index were analyzed for assessment of Spatio-temporal changes in the forest cover. Multi-temporal Landsat-TM and OLI sensor data for the years 1991, 2001, 2011, and 2021 were utilized covering the study area. The results indicated that the total forest cover area has gradually increased from 1991 to 2021 and the total forest area has doubled during this period of 30 years. The comparative study of NDVI and Land Surface Temperature map brings out a significant fact that in the areas where moderate and dense forest cover is present, the Land Surface Temperature was lower as compared to areas with poor vegetation cover. This indicates that there is an inverse relationship between forest cover distribution and Land Surface Temperature. However, the Ecological evaluation index shows that the forest vegetation quality is gradually improving to normal conditions with the excellent category and UTFVI value (< 0) in reference to the year 1991.

Integrating UAV Multispectral Indices (NDVI) with Yield Data for Optimizing Flax (Linum usitatissimum L.) Cultivation

Flax (Linum usitatissimum L.), is a multipurpose crop, grown for fiber, seeds, and oil. This study aims to assess flax growth dynamics and agronomic traits using Normalized Difference Vegetation Index (NDVI) maps generated from multispectral imagery captured by an Unmanned Aerial Vehicle (UAV). Conducted at the Agricultural University of Athens, the field experiment followed a split-plot design with four replicates, encompassing various sowing distances (30 and 40 cm, between rows) and fertilization treatments (urea fertilization, urea with inhibitors, organic fertilizer). NDVI index maps were produced at key growth stages (60, 90, 120, 150, and 179 Days After Sowing) to facilitate comprehensive growth analysis. The findings indicate that while fertilization treatments and sowing densities influenced early growth stages, no significant differences were observed post 90 DAS. NDVI values correlated with agronomic traits, particularly during the early vegetative phase, highlighting the potential of UAV-based NDVI mapping for precise flax cultivation monitoring.

Evaluation of Urban Sprawl and Land Surface Temperature along with Vegetation and Built-up Index for Nagpur City, Maharashtra

Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. Introduction: Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST. background: The LULC classification is performed based on four classes Bare land, Built-up, Vegetation and Waterbodies. Method: The study aims to provide a detailed analysis of land and temperature change with variation in Normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI) for the study area using a geospatial technique. The LULC classification is performed based on four classes which are Bare land, Built-up, Vegetation, and Waterbodies from the year 2000 to 2020. The classified data is further used to extract the Land Surface Temperature (LST) data from the thermal band to generate LST maps. The NDVI and NDBI maps are also generated using the land sat imageries. From the above-mentionedanalysis, it is found that Nagpur city temperature has risen by 3.67 °C in two decades. Whereas, LULC results show that bare land and vegetation decreased by 11.88% and 14.93% respectively, while an increase is seen for built-up and water bodies by 25.62% and 0.19% respectively. objective: The classified data is further used to extract the LST data from thermal band to generate LST maps. Result: Regression analysis between temperature and NDVI, NDBI shows that temperature and NDVI have a negation relation and NDBI has a positive relation with temperature (Pearson’s r: between -0.89 to -0.81and between 0.90 to 0.81respectively) for both the years. The increased temperature is a result of urbanization in the study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. method: The NDVI and NDBI maps are also generated using the land sat imageries. From the analysis of above mentioned it is found that Nagpur city temperature is raised by 3.67 °C in two decades. Conclusion: This study recommends that policymakers develop policies that should minimize the transition of different classes and check the outcome of industries and the temperature of the surroundings. conclusion: The increase temperature is a result of urbanization for study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift. other: .

Remote Sensing Techniques for Identifying Flood Prone Areas Using the Vegetation Index, Water Index, and Simple Additive Weighting Methods for Mekarsari Areas, Cianjur Regency, West Java

Flood is a natural disaster with an intensity that often occurs in cities on a scale with excess water discharge in some areas. This research was conducted to determine the risk of flooding in Mekarsari Village and its surroundings, Cianjur Regency, West Java. (According to InaRisk, 2022) Risk and danger of flooding Moderate – High. This study uses data for the entire year 2022. With several parameters used in this study including land cover, rainfall, soil type, slope, NDVI, NDWI and SAVI. Simple Additive Weighting or weighting for each parameter with different results, according to the classification of each of these parameters. The results of this weighting will become a Flood Hazard Risk Map which was previously the result of overlay and intersection of land cover map, rainfall map, soil type map, slope map, Normalized Difference Vegetation Index map, Normalized Difference Water Index map and Soil Adjusted Vegetation Index map. On the Flood Disaster Risk Map there are five categories of vulnerability levels Not Hazardous, Less Hazardous, Moderate, Hazardous and Very Hazardous. The cause of the risk of flooding in the study area is that most of the land use is diverted to rice fields, plantations, dry agricultural land and settlements so that the lack of vacant land causes low absorption of rainwater and also steep slopes and high elevations which also cause rainwater to be stored in the lowlands. settlements) with moderate to high rainfall intensity every month. There is also a lack of education for the community to mitigate the risk of flood disasters so that waste management and waterways in settlements are inadequate and well managed.

Анализ восстановления растительного покрова в пределах Шуруповских карьеров на основе данных дистанционного зондирования

The article presents an analysis of restoration processes within two quarries located in the northwest of the Shurupovsky Khutor in the Frolovsky district of the Volgograd region. The described methods of revegetation analysis within the quarry-dump complexes included images from the USGS Earth Explorer portal (US Geological Survey). The Landsat-5 TM satellite images were used in July 2007, Landsat 8 OLI_TIRS was used in July 2015, and Landsat 9 OLI_TIRS described the status in July 2022. Using the ArcGIS geoinformation system 10.3.1, we considered multispectral images of different times and calculated the Normalized Difference Vegetation Index (NDVI) for 2007, 2015, and 2022. As a second index, we calculated the NDVI trend for 2007–2015 and 2015–2022. Based on the results of interpretation, the assessment of changes in the vegetation cover of quarry-dump complexes was carried out. As a result, it was revealed that the vegetation cover of quarries demonstrated different restoration pathways despite their close territorial location. By 2022, the NDVI of the eastern quarry did not exceed 0.2, and the NDVI of the western quarry increased to 0.5. However, in general, there is a positive NDVI trend in the territories of both quarries. Resulting from the data obtained, NDVI maps and NDVI trend maps for the studied quarries were prepared. Tables for calculating changes in the areas of flooding and vegetation cover of the Shurupov quarries for 2007, 2015, and 2022 were created in MS Excel.

How similar is the zoning of different vegetation indices: Defining the optimal framework for monitoring grapevines’ growth within vigorous vineyards

The spatial patterns of grapevines’ vigor across vigorous vineyards were assessed through distinct vegetation indices (VI) for comparison purposes. This survey also aids the selection of the most sensitive VI to monitor the vineyards during a given optimal stage of the last period of the growing season. The canopy reflectance (ρ) of grapevines cv. Cabernet Franc and Cabernet Sauvignon (Vitis vinifera L.) was measured throughout their berry development and ripening stages. Georeferenced data of ρ at the red (680 nm), red edge (730 nm), and near-infrared (780 nm) wavebands were taken using a hand-held active sensor to compute the normalized difference red edge index (NDRE), and the normalized difference vegetation index (NDVI). Spatial predictions of both VI were estimated by geostatistical interpolation technique (kriging) and then categorized into homogeneous zones (HZ) through the Jenks natural breaks optimization method to delimit low and high vigor regions. Statistics of the inter-rater reliability between each VI zoning were calculated afterward. The relative dispersion of data around their mean was assumed as a criterion to assess the sensibility of each VI in detecting vigor variability under a given condition. The most appropriate stage to monitor the vineyards was determined based on the peak of vegetative growth inferred from the values of the VI we presumed as most sensitive. Differences between the HZ defined from the best combination of VI and stage in terms of yield, cane weight, and crop load were also examined. We demonstrated that NDRE was more sensitive than NDVI in detecting the vigor variability of high-density vegetation due to the relative dispersion of its datasets and the saturation effect of NDVI at the late stages of the grapevine growing season. The lowest and higher agreement between the VI zoning was usually detected at the berry touch and berry softening growth stages, respectively. Although there were distinct levels of concordance between NDRE and NDVI maps, their overall agreement was higher than 70% in most surveys. The disagreement in quantity or spatial allocation of vigor categories provided by each VI relied on the vineyard, growth stage, and growing season. Nevertheless, the early definition of HZ from NDRE monitoring carried out at veraison could be suitable to identify differences in cane weight at pruning, with a moderate effect size.

A spatio-temporal fusion method based on Landsat and MODIS normalized vegetation index data

Abstract. Normalized difference vegetation index (NDVI) can simply and effectively reflect the growth status of plants, and has a linear relationship with vegetation coverage, thus it is an important indicator to identify vegetation growth status and coverage. However, the commonly used MODIS NDVI or Landsat NDVI cannot simultaneously achieve the high temporal and spatial resolution. To this end, we proposed a generic and automatic method to fuse MODIS and Landsat satellite vegetation index (NDVI) into daily high-resolution products which takes time series NDVI data from MODIS/Landsat as input, filters noise pixels, and generates high-resolution and high-frequency products through a fusion process of temporal-linearly interpolation to increase the temporal resolution and spatial filtering to alleviate the blocky artifacts from MODIS pixel border. The fused NDVI maps are visually compared to evaluate the performance of this method, and more quantitative assessment will be continued in the next step. This study can provide important support for large-scale long-term surface vegetation monitoring.

Effects of climates and physical variables of parks on the radius and intensity of cooling of the surrounding settlements

The effects of different climates and physical variables of parks on the radius and intensity of the cooling of the surrounding environment of Khorasan-Razavi settlements in Iran were investigated by determining the climates of selected cities (Mashhad, Sabzevar, Neyshabur, and Quchan) based on the Litinsky model. Earth surface temperature was calculated using the normalized difference vegetation index (NDVI), proportional vegetation (Pv), and emissivity (ԑ). According to the NDVI maps, vegetation was classified into three classes. Short vegetation (grass and bush) does not significantly affect cooling the surrounding environment compared to tree cover. Cities with dense tree vegetation (Mashhad and Neyshabur) are 3.7 and 1.53 °C cooler than the surrounding environment, respectively. Tree heights, the sun's angle to the surface, and the shading of tree cover are important factors that control the surface temperature. The climatic diversity of the studied cities in connection with the spatial fluctuations of the temperature has led to the creation of three different climates in the cities. The radius and intensity of cooling of the green infrastructure on the neighborhood scale (parks) revealed that park area, tree cover, location, and climate are very effective on reducing the thermal load of the microclimates of the studied cities.

ARD‐VO: Agricultural robot data set of vineyards and olive groves

AbstractThe availability of real‐world data in agricultural applications is of paramount importance to develop robust and effective robotic‐based solutions for farming operations. In this application context, however, very few data sets are available to the community and for some important crops, such as grapes and olives, they are almost absent. Therefore, the aim of this paper is to introduce and release ARD‐VO, a data set for agricultural robotics applications focused on vineyards and olive cultivations. Its main purpose is to provide the researchers with a real‐world extensive set of data to support the development of solutions and algorithms for precision farming technologies in the aforementioned crops. ARD‐VO has been collected with an unmanned ground vehicle (UGV) equipped with different heterogeneous sensors that capture information essential for robot localization and plant monitoring tasks. It is composed of sequences gathered in 11 experimental sessions between August and October 2021, navigating the UGV for several kilometers in four cultivation fields in Umbria, a central region of Italy. In addition, to highlight the utility of ARD‐VO, two application case studies are presented. In the first one, the data set is used to compare the performance of simultaneous localization and mapping and odometry estimation methods using vision systems, light detection and ranging, and inertial measurement unit sensors. The second one shows how the multispectral images included in ARD‐VO can be used to compute Normalized Difference Vegetation Index maps, which are crucial to monitor the crops and build prescription maps.

Community-Based Approach for Agricultural Water Management of Summer Crops

Globalization, population change, and rural-to-urban movement are the main causes of the enormous issues faced by rural communities. Every growing nation, including the Kurdistan Region of Iraq (KRI), has a sizable proportion of rural farmers among its populace. This study was done in the Sulaimani Governorate, which was divided into six main districts namely; Garmian, Penjwin, Halabja, Chamchamal, Sharazur, and Raniya, and aimed to compare the groundwater level with summer crop water demand. The weighted Sum Method (WSM) and a Normalized Difference Vegetation Index map (NDVI) were used to analyze and illustrate the current water demand status for summer crops. The study employed a participatory research design, utilizing a purposive sampling method to select 60 representative farmers, 25 key decision-makers, representatives from Non-Governmental Organizations (NGOs), and experts in the field of water management. Data was collected through the use of focus group discussions (FGDs) and Key Informant Interviews (KIIs) to gather information from the selected participants. This study is innovative in its attempt to establish a correlation between the yield of summer crops and the availability of groundwater. As a conclusion for this study, the results suggest that the level of groundwater plays a significant role in determining the production of summer crops. Additionally, the sustainable development of various regions within the Sulaimani Governorate is influenced by a complex interplay of environmental, economic, and social factors. Future work will be focus on doing a detail research regarding time series for ground water level (GWL) vs. crop production vs. NDVI.

Efficiency-first spraying mission arrangement optimization with multiple UAVs in heterogeneous farmland with varying pesticide requirements

Combining multiple crop protection Unmanned Aerial Vehicles (UAVs) as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency. However, given some issues such as different configurations, irregular borders, and especially varying pesticide requirements, it is more important and more complex than other multi-Agent Systems (MASs) in common use. In this work, we focus on the mission arrangement of UAVs, which is the foundation of other high-level cooperations, systematically propose Efficiency-first Spraying Mission Arrangement Problem (ESMAP), and try to construct a united problem framework for the mission arrangement of crop protection UAVs. Besides, to characterise the differences in sub-areas, the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index (NDVI). Firstly, the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined. Furthermore, an acquisition method of a farmland’s NDVI map is proposed, and the calculation method of pesticide volume based on NDVI is discussed. Secondly, an improved Genetic Algorithm (GA) is proposed to solve ESMAP, and a comparable combination algorithm is introduced. Numerical simulations for algorithm analysis are carried out within MATLAB, and it is determined that the proposed GA is more efficient and accurate than the latter. Finally, a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation. Test results illustrated that it performed well, which took only 90.6 % of the operation time taken by the combination algorithm.

A cascaded data fusion approach for extracting the rooftops of buildings in heterogeneous urban fabric using high spatial resolution satellite imagery and elevation data

In this study, a cascaded data fusion approach was implemented to extract the rooftops of buildings from remote sensing data in a heterogeneous urban fabric in Irbid city, Jordan. The rooftops of buildings were extracted from Pleiades-B1 very high spatial resolution imagery (i.e., 2 m) along with LiDAR-derived normalized digital surface model (nDSM) data and normalized difference vegetation index (NDVI). The imagery from Pleiades-B1 satellite was classified using support vector machine (SVM), then a normalized digital surface model (nDSM) was calculated to generate the heights map. Then, the trees and vegetation cover were filtered using NDVI in which different threshold values were tested to achieve the best identification of the rooftops of buildings. The results of the SVM, nDSM, and NDVI maps were combined to obtain one layer representing the rooftops of buildings in the study area. Finally, the results were evaluated against reference data obtained from the municipality of Irbid and ground surveying. The correctness and completeness measures of the detected buildings’ footprints were 0.956, 0.854 and 0.902 for the precision, recall, and F-score respectively. The total area of the extracted rooftops of buildings was approximately 6.2 km2 when compared to the area of the registered buildings in the municipality of Irbid (i.e., 5.9 km2) in 2021. The results proved the possibility of using the implemented method in mapping the rooftops of buildings in such a heterogeneous urban fabric in the study area.

Evaluation of Multispectral Data Acquired from UAV Platform in Olive Orchard

Precision agriculture is a management strategy to improve resource efficiency, production, quality, profitability and sustainability of the crops. In recent years, olive tree management is increasingly focused on determining the correct health status of the plants in order to distribute the main resource using different technologies. In the olive grove, the focus is often on the use of multispectral information from UAVs (Unmanned Aerial Vehicle), but it is not known how important spectral and biometric information actually is for the agronomic management of the olive grove. The aim of this study was to investigate the ability of multispectral data acquired from a UAV platform to predict nutritional status, biometric characteristics, vegetative condition and production of olive orchard as tool to DSS. Data were collected on vegetative characteristics closely related to vigour such as trunk cross-sectional area (TCSA), Nitrogen concentration of the leaves, canopy area and canopy volume. The production was collected for each plant to create an accurate yield map. The flight was carried out with a UAV equipped with a multispectral camera, at an altitude of 50 m and with RTK correction. The flight made it possible to determine the biometric condition and the spectral features through the normalized difference vegetation index (NDVI). The NDVI map allowed to determine the canopy area. The Structure for Motion (SfM) algorithm allow to determine the 3D canopy volume. The experiment showed that the NDVI was able to determine with high accuracy the vegetative characteristic as canopy area (r = 0.87 ***), TCSA (r = 0.58 ***) and production (r = 0.63 ***). The vegetative parameters are closely correlated with the production, especially the canopy area (r = 0.75 ***). Data clustering showed that the production of individual plants is closely dependent on leaf nitrogen concentration and vigour status.

EVALUATION OF RGB VEGETATION INDICES DERIVED FROM UAV IMAGES FOR RICE CROP GROWTH MONITORING

Abstract. The unmanned aerial vehicles (UAVs) are widely used for agricultural monitoring due to reduce the cost and time of crop monitoring via the acquisition of images with high spatial-temporal resolution. The normalized difference vegetation index (NDVI) is the most widely studied and used for mapping crop growth. A relatively expensive multispectral sensor is required to produce an NDVI map. The visible vegetation indices (VIs) derived from UAV images showed potential capabilities for predicting crop growth. The purpose of this paper is to evaluate the RGB indices to monitor the growth of the rice crop. The images were obtained from the study area by DJI PM4 multispectral UAV. The multispectral images were used to calculate NDVI as a reference vegetation index and different RGB indices were implemented and compared with the reference index. The results showed that RGB indices can be used as the vegetation index in the case of unavailable multispectral images.

A COBERTURA DA TERRA E AS CARACTERÍSTICAS DAS TEMPERATURAS SUPERFICIAIS E DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA NA RAIA DIVISÓRIA SÃO PAULO/PARANÁ/MATO GROSSO DO SUL

The purpose of this article is to analyze the changes in land cover in the São Paulo-Paraná- Mato Grosso do Sul dividing line following the formation of lakes for the generation of energy from hydropower plants, considering the years 2001 and 2021, in addition to verify how the transformations in the landscape affected the vegetation cover and surface temperatures. To do this, images from the Landsat 7 and 8 satellites were used, as well as maps of land cover, and NDVI (Normalized Difference Vegetation Index) and surface temperatures were generated in order to analyze vegetation interference on the target temperatures. Among the available images, those that were suitable for this type of study (without cloud cover) were chosen on representative days of the dry season, taken on August 3, 2001, and on August 18, 2021, as well as of the rainy season, taken on February 7, 2021. The results showed that, even with minor changes in land cover in the period from 2001 to 2021, the decrease in vegetative vigor, as confirmed by the NDVI, in the dry period resulted in an increase in areas with higher target temperatures. It also showed that the spatial variability of surface temperatures in the São Paulo-Paraná-Mato Grosso do Sul dividing line does not depend solely on the transformations in the landscape but is rather directly related to the types of weather that provide greater or lesser precipitation totals in the period prior to the obtaining of the images. Therefore, it was demonstrated that changes in land cover alone do not respond to the variability and intensity of surface temperatures.

Detection of the Land Surface Temperature Changes in Ma’an Governorate using Remote Sensing Data during the Period (1990-2018)

This paper investigates multi-temporal land surface temperature (LST) for large ungauged areas of Ma’an Governorate, Jordan, based on changes in normalized difference vegetation index (NDVI) using remotely sensed data. Five mosaic images were taken for both the 1990 and 2018 years by Landsat 5 (TM) and Landsat 8 (OLI) (path/row, 174/36- 174/37). These were used as the basic data source, where most of Ma’an Governorate has no meteorological stations. The five-mosaic images for both Landsat 5 and Landsat 8-OLI were taken in September. LST and NDVI maps have been generated to determine the changes in LTS during the monitoring period. The results showed that the minimum value of LST increased by 4°C, and the mean surface temperature increased nearly by 2°C between 1990 and 2018. The average LST has been rising at a rate of 0.071°C/y.

Utilization of unmanned aerial vehicles for zonal winemaking in cool-climate Riesling vineyards

Individual vineyards can vary spatially for several viticultural attributes, including water stress, nutrient status, growth/vigour and disease—which can, in turn, impact berry composition and resulting wine products. The goal of this study was to determine if vineyard variability detected by remote sensing using an unmanned aerial vehicle (UAV) could be used to zonally harvest vineyard blocks and produce wines that are sensorially differentiable. The specific hypothesis was that remote sensing would detect vineyard variation in viticultural variables and associate this variation with differences in wine sensory attributes based upon zonal harvesting. In six commercial Riesling vineyards across the Niagara Peninsula in Ontario, Canada, a UAV collected multispectral data, which were used to calculate the normalized difference vegetation index (NDVI). Grapevines (≈ 80) in a grid pattern were geo-located within each block and vineyard UAV NDVI maps were used for zonal harvesting of geo-located vines in areas corresponding to high vs. low NDVI. Wines made from these zones were then compared chemically and sensorially. Overall, wines created from high vs. low NDVI zones differed inconsistently in their basic wine composition. Sensorially, for certain sites and vintages, panellists distinguished between wines made from high vs. low NDVI zones using a sorting task. UAV NDVI demonstrated the ability to determine areas within a vineyard block that could produce wines that were sensorially distinguishable from one another.

Crop Monitoring of Paddy Field Using Landsat 8 OLI

There are times where paddy farmers faced crop yield production that does not meet their expectations although the crop has been well-tended. Especially when the growing and harvest seasons, farmers will be able estimate on cutting some production costs while increasing crop yields with crop yield monitoring and mapping if they could measure the different vegetitation indices of crop field. Therefore, to test the efficiency of this study, the crop health was monitored using vegetation index values obtained from multispectral remote sensing images, such as the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The objectives were to estimate the NDVI and SAVI values of a paddy field in Sabak Bernam, Selangor and create a crop health map using the calculated NDVI and SAVI during its resting period (January, February, July, August) and growing up period (April) in 2020. This study used Landsat 8 OLI images at a resolution of 30 meters. The data was then processed using Erdas Imagine and ArcGIS Pro to estimate the NDVI and SAVI values, as well as produce crop health maps. As a result, the estimated NDVI and SAVI crop health maps were displayed. In comparison to NDVI, SAVI estimates a greater vegetation index as the value of adjustment factor (L) applied in the SAVI can reduce the soil noise compared to the NDVI. Furthermore, the computation of planted areas revealed that some changes had occurred. The planted area decreased in a specific month, indicating the harvesting season.