Normalized Difference Vegetation Index Index Research Articles

Artificial intelligence tools and vegetation indices combined to estimate aboveground biomass in tropical forests

Measurement of forest biomass is a time-, money-, and labor-consuming activity. Developing methodological approaches for quantifying biomass in natural ecosystems has motivated countless investigations in several vegetation types worldwide. In this study, we developed a model to estimate the above-ground biomass (AGB) in a study site spatially located in Western Pará state, Brazilian Amazon. We applied artificial neural networks and remotely sensed data for adjusting our model based on forest inventory data. We tested four vegetation indices retrieved from a Landsat-5 thematic mapper scene and assessed their correlations with the field measured AGB. We trained 286 artificial neural networks using the vegetation stratus (two phytophysiognomies: dense lowland ombrophilous forest and dense submontane ombrophilous forest) and the normalized difference vegetation index (NDVI) and aerosol-free vegetation index as predictors variables of the AGB. We selected the artificial neural network model showing higher correlation coefficient and concentration of residuals in the center classes, which indicates higher predictive capabilities of biomass estimation. The generated model architecture (4-13-1) was composed by 4 predictor neurons in the input layer, 13 neurons in the hidden layer, activated by logistic functions, and a single neuron (the AGB in ton · ha − 1) in the output layer, activated by an identity function. We observed that the NDVI and aerosol free vegetation index showed the best performance to estimate the AGB in the study area. Using artificial neural network and a Landsat-5 image combined, we were able to accurately predict (estimation error of ∼20 % ) AGB in tropical forest. This is a promising methodological approach that can be applied to assess ecosystem services related to carbon stock in tropical regions.

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

ANÁLISE NORMALIZADA DO ÍNDICE DE DIFERENÇA DE VEGETAÇÃO UTILIZANDO ÍNDICES DE NDVI E SAVI NA UNIDADE DE CONSERVAÇÃO SERRA DA BORBOREMA PARQUE NATURAL MUNICIPAL, CAMPINA GRANDE, PARAÍBA, BRASIL

The nature conservation units (UC) are specially protected territorial spaces (ETEP), with creation, implementation and management provided for by Law 9985/2000, which established the National System of Conservation Units (SNUC). Over 28% of the Brazilian territory is set aside for nature conservation, in a universe of over 2,300 established Units. Paraiba, Northeast region, has 15 (fifteen) Protected Units, representing less than 1% of its territory, and the Municipal Natural Park of Serra da Borborema, classified as Full Protection UC, located in the eastern part of the city of Campina Grande, at coordinates Lat. (S) 07º23'250" and Log. (W) 35º84'331", is the only local UC. Objective: to analyze the vegetation cover of the Park through the: Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), in the period from June to September 2020. Theoretical Frame: It was used citations and authors, respecting the quadrennium 2019 and 2022, being the main ones listed here: Ministry of Environment, WWF, IUCN, IBAMA and, Felix-Trigueiro & Fontgalland, favoring the understanding of concepts related to the protected area and the environmental conservation in the Poeta's Park in Campina Grande -Brazil, and the studies of Ponzoni et al. for the weighting of the derivations of sensorial studies of vegetation. Method: two forms of index analysis were proposed: data and geoprocessed images from LandSat8 satellite, provided by the US Geological Survey - USGS, using Qgis 3.10 software. Results and Conclusions: despite the difference in the representation of minimum and maximum values between the indices evaluated in the thematic maps, as a result, the NDVI and SAVI vegetation indices were close to 1, so that it can be inferred that from the analysis of the biophysical resource "vegetation cover" of the locality, it can allow the conservation of the area for the maintenance of its ecosystem services available to the region, despite the anthropic pressure suffered at the site. Implications of the research: The research is classified as extremely appropriate to the interests of the Post-graduate Program of the Federal University of Campina Grande and also corroborates as literature for other research groups in the Northeast that are involved with protected areas and analysis of vegetation index. Originality and value: The work is pointed out as essential because its differential are the analyses through NDVI and SAVI indexes adapted to the complex of the poet's park in Campina Grande-Brazil.

Pola Keterkaitan Perubahan Distribusi Kepadatan Vegetasi dengan Penggunaan Lahan DAS Pesisir, Case Study : DAS Garang

<p><em>The diversity and density of vegetation is key in managing sedimentation throughout the watershed, especially in the central and downstream regions. Measures of vegetation diversity and density that can be used are the Normalized Difference Vegetation Index (NDVI) which is a measure of vegetation greenness (chlorophyll levels), Normalized Difference Water Index (NDVI) to measure vegetation wetness levels, and soil adjusted vegetation index (SAVI) for low canopy vegetation cover. For watersheds that stretch long to downstream or coastal areas, the measure of land diversity needs to be supplemented by using the Normalized Difference Built-Up Index (NDBI) indicator that is useful for land use planning. Information on this vegetation index can be obtained by processing a satellite imagery map. The benefits of processing vegetation indexes in watershed management are becoming increasingly important in the era of climate change, especially with regard to efforts to harmonization of relationships between environmental elements that include the diversity of the interests of the resident population. That's why this research was done in order to find the link between natural factors including the impacts of climate change and humans. The test began by looking for changes in the NDVI, NDWI, SAVI, and NDBI indexes from the last 5 years with the Global Indicator Spatial Association (GISA) analysis using Moran's I Global Index, followedby looking for the association with the pattern of population change and the percentage of the area of the settlement. The results showed a link between moran's I Global index change pattern of population change and the spread of residential developments. The tendency of the distribution of this settlement area is an important point in analyzing the influence of its dispersal patterns in a watershed so that the continued impact of potential erosion and sedimentation triggered will be the main consideration in watershed management.</em></p>

Exploring the relationship between soil chemical composition and NDVI index using AI

This scientific article presents the results of research focused on developing a method for predicting the Normalized Difference Vegetation Index (NDVI) based on soil chemical composition using a multilayer artificial intelligence (AI) model. This method aims to improve the accuracy and predictive capability of land resource assessment, as well as the impact of chemical factors on vegetation. The study involved collecting soil chemical composition data in various conditions, providing a wide range of information for analysis. For NDVI assessment, a key indicator of vegetation condition, data from modern Earth observation satellite systems were used. The central aspect of the research is the multilayer AI model based on the Rosenblatt perceptron, capable of detecting complex nonlinear relationships between soil chemical parameters and NDVI. The training algorithm was tuned for maximum accuracy and generalization of results. The results show that the developed model provides high accuracy in NDVI predictions, making it an important tool for agriculture, ecology, and sustainable land use. These findings highlight the potential of using AI and soil data to optimize agricultural production, monitor ecosystems, and manage land resources.

Spatial and Temporal analysis (2008-2017) of droughts and their effects on livestock in the Brazilian semi-arid region.

In this study a temporal analysis (2008-2017) was conducted to examine the occurrence of droughts and the spatial distribution of livestock herds in the Brazilian semi-arid region. Maps of vegetation cover, classes of normalized difference vegetation index (NDVI) and normalized difference drought index (NDDI) were used. Image processing took place on the Google Earth Engine platform along with QGIS Software (2.18). Two historical series were created to compare the variation between two herds and the dry monitoring, one called the 1st series (2008-2012) and 2nd series (2013-2017) which were compared using a paired t-test. The comparative analysis showed that the classes of extreme drought and exceptional drought covered the greatest land areas in the 2nd series, while abnormal drought covered the greatest land area in the 1st series. The analysis of herds, a reduction in the cattle herd and an increase in goat and sheep herds were noted. This study considers the efficacy of NDDI and NDVI indices to monitor the distribution of droughts in the Brazilian semi-arid region. The geoprocess of Sidra data enabled an analysis of the spatial distribution and temporal variation of the herds in the semi-arid region and how drought may have impacted livestock dynamics.

Assessment of hail damages in maize using remote sensing and comparison with an insurance assessment: A case study in Lombardy

Studies have shown that the quantification of hail damage is generally inaccurate and is influenced by the experience of the field surveyors/technicians. To overcome this problem, the vegetation indices retrieved by remote sensing, can be used to get information about the hail damage. The aim of this work is the detection of medium-low damages (i.e., between 10 and 30% of the gross saleable production) using the much-used normalized difference vegetation index (NDVI) in comparison with alternative vegetation indices (i.e., ARVI, MCARI, SAVI, MSAVI, MSAVI2) and their change from pre-event to post-event in five hailstorms in Lombardy in 2018. Seventy-four overlapping scenes (10% cloud cover) were collected from the Sentinel-2 in the spring-summer period of 2018 in the Brescia district (Lombardy). An unsupervised classification was carried out to automatically identify the maize fields (grain and silage), testing the change detection approach by searching for damage by hail and strong wind in the Lombardy plain of Brescia. A database of 125 field surveys (average size 4 Ha) after the hailstorm collected from the insurance service allowed for the selection of the dates on which the event occurred and provided a proxy of the extent of the damage (in % of the decrease of the yield). Hail and strong wind damages ranged from 5 to 70%, and they were used for comparison with the satellite image change detection. The differences in the vegetation indices obtained by Sentinel 2 before and after the hailstorm and the insurance assessments of damage after the events were compared to assess the degree of concordance. The modified soil-adjusted vegetation index outperformed other vegetation indices in detecting hail-related damages with the highest accuracy (73.3%). On the other hand, the NDVI resulted in scarce performance ranking last of the six indices, with an accuracy of 65.3%. Future research will evaluate how much uncertainty can be found in the method’s limitations with vegetation indices derived from satellites, how much is due to errors in estimating damage to the ground, and how much is due to other causes. Highlights - The discovery rate of damaged fields improved. - MSAVI outperformed NDVI and other vegetation indices, identifying 73.3% of occurrences. - Estimation of damage from remote sensing was more accurate for fields severely affected >50%. - In low-intensity hail events (<50 canopies affected), the MSAVI provided a detailed picture of the damage across the field. - The proposed approach is promising to develop a ‘sampling map’ for detailed on-ground assessment.

Dynamics of climate changes and its effect on the performance of cereals according to satellite data

Aim. To study the effect of climate changes on the performance of cereals. Methods. Remote, statistical, and analytic methods. Climate changes were determined by the sum of radiation temperatures of the earth’s surface, calculated using the data of the infrared range (10.3–11.3; 11.4–12.4 μm) of the high precision radiometer AVHRR of the meteorological satellites of the Earth, NOAA. The effect of climate changes on the state of vegetation, including the phenological parameters, such as the beginning, the end, and the length of the vegetation peri- od, was determined by the Normalized Difference Vegetation Index (NDVI) obtained using the data of infrared (0.72–1.1 μm) and red (0.58–0.68 μm) ranges of the abovementioned radiometer. The satellite data are openly accessed on the website of STAR NESDIS NOAA – Satellite Applications and Research of NOAA’s National En- vironmental Satellite Data Information Services of the National Ocean and Atmosphere Research Department of the USA – http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/. The dynamics of СО2 concentration in the air was determined using the satellite data of AIRS/Aqua (https://disc.gsfc.nasa.gov/datasets/AIRS3C2M_005/summary), the precipitation dynamics – using the data of ERA5 ECMWF/Copernicus Climate Change Service (https://devel- opers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY#description). The association be- tween the performance of cereals and satellite data was found by the method of correlation and regression analysis. Results. Using the satellite data, it was found that in 1982–2021, the sum of radiation temperatures of the earth’s surface reliably increased in the vegetation period (April–September), and the amount of precipitation tended to decrease in the zones of Polissia, Forest-Steppe, and Steppe. The highest tempo of warming was observed in the territory of the Steppe. In general, the warming had a positive effect on the state of vegetation, according to NDVI, and on the performance of cereals. However, in the recent decade (2010–2020), the positive effect of the warming on the vegetation state decreased, and according to the NDVI trend, there has been a tendency towards the negative effect of the further increase in the temperature, which is especially remarkable for the Steppe and Forest-Steppe. On the contrary, the warming in the Polissia zone created the conditions for the increase in the performance of agri- cultural cereals. Conclusions. From 1982 till 2021, there was a strong tendency towards the increase in the sum of radiation temperatures of the earth’s surface in the vegetation period and the decrease in the precipitation amount in Polissia, Forest-Steppe, and Steppe. According to the NDVI data, due to the warming, the length of the vegeta- tion period increased by 14–21 days on average, which had a positive effect on the vegetation. Since 2010, the acceleration in the warming tempo has been observed. The average annual increase in the sum of the radiation tem- peratures of the earth’s surface in the vegetation period is 15–16 °С – for Polissia, 14–18 °С for the Forest-Steppe, and 18–20 °С for the Steppe. According to this tendency, if the current regularity in the temperature increase in 1982–2021 is preserved in the nearest future, the temperature of the vegetation period will additionally increase, and the average sum of temperatures of the earth’s surface will reach 4000 °С in Polissia, which was notable for the Forest-Steppe in 1982–2021, 4440–4600 °С – in the Forest-Steppe, and up to 5800–5900 °С in the Steppe. In 1982–2000, there was a strong regularity of the positive effect of the warming during the vegetation period both on the vegetation state according to the NDVI index, and the performance of cereals in all three natural climatic zones. However, after 2000, due to the negative effect of high summer temperatures on the crops, the effect of the warming on vegetation considerably decreased, which was especially notable for the Steppe where the increase in NDVI compared to the previous period was on average 9.41 % in 1992–2001, and in 2012–2021 – 1.78 %.

Analyzing Drought Conditions, Interventions and Mapping of Vulnerable Areas using NDVI and SPI Indices in Eastern Ethiopia, Somali Region

Analysis of a long-term temporal and spatial drought conditions in Somali region was done using Standardized Precipitation Index (SPI) and Coefficient of Variation (CV) for 6 meteorological stations and Normalized Difference Vegetation Index (NDVI) analysis were performed by enhancing images from Landsat 5 TM of two different years. CV was computed for Belg season, Deyr season and yearly basis. Rainfall variability for Belg and Deyr was higher than annual variability. Characterization of the temporal variation of drought using 3-months and 6-months SPI for each meteorological station were analyzed and plotted against long time series in yearly basis. The NDVI values and SPI indices were able to show extent and variability of drought. The 3-months SPI results revealed that maximum frequency of severe drought events were experienced in Gode (7 times), followed by Hurso (6 times) within the study period. The 6-months SPI results show that less frequency of dry and wet events were observed in the entire study period as compared to 3-months SPI results. The frequency of any drought events (moderate to extreme) occurrence in the study area is once in every two years at all stations. Whereas, severe and extreme drought event in majority of study area is commenced nearly once in three years with the exception of Degahabur having once in five years. Overall, a preliminary comparison between results obtained using the SPI and NDVI shows a general good agreements.

Evaluation of tillage & fertilization in Carthamus tinctorius L. using remote sensing

The present study aimed to assess the efficacy of slow-release fertilization and the tillage on the performance of Carthamus tinctorius L. apply via the utilization of the Normalized Difference Vegetation Index (NDVI). The study lasted from November 24, 2020, to June 16, 2021, and it was conducted in the experimental field of the Agricultural University of Athens. The experiment was set up in a split-plot design with four replications, two main plots [conventional tillage (CT) and no-tillage (NT)], and three sub-plots [untreated control (C), urea (U), and urea with nitrification and urease inhibitors (UI+NI)]. Using a multispectral camera (Parrot Sequoia) mounted on a UAV and a ground-based sensor, we were able to acquire crop reflectance data in the red and near-infrared electromagnetic spectrum necessary to calculate the NDVI index (Green Seeker). The information was sent into a geographic information system for analysis (ArcMap). Descriptive statistics were computed, geostatistical analysis was conducted, and the correlation between the reflection data from the two sensors was investigated for the statistical analysis of the data. The highest average NDVI values were recorded in the plots treated: A) with no soil treatment and urea fertilization, and B) with normal soil treatment and urea and nitrification inhibitor fertilizer. On the contrary, the highest yields were reported in CT and UI+NI.

The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

This study presents vegetation land cover mapping based on Remote Sensing (RS) data processing, including satellite imagery scenes from Sentinel-2A, ASTER, and Landsat, in order to show the spatio-temporal correlation between the climate and environmental setting in Morocco. The main objective of this study is to contribute on land use planning and ecosystem protection. The distribution of vegetation in the study area was analyzed based on the satellite image processing and computing Normalized Difference Vegetation Index (NDVI). The study was conducted on different satellite imagery (Sentinel-2A, ASTER and the Landsat TM5, ETM+, and OLI) using SAGA GIS and SNAP software, to achieve two specific objectives: (1) conducting a comparative analysis of the NDVI calculation results from three satellite images (i.e., sentinel-2A, ASTER, Landsat 8); (2) highlighting the dynamics of the NDVI index from 1984 to 2020 by processing 35 satellite images from Landsat data Archive using SAGA GIS software, and then correlating these changes with other vegetation indices for the Marrakech-Haouz region provided by NOAA, namely: Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and NDVI indices. This study shows a repetitive fluctuation in the decrease of NDVI mean value from 1984 to 2020, due to the contrasting climate setting of the study area and the influence of the alternation of humid and dry periods in the High Atlas of Marrakech. In addition, the presented results underline that open-source software, such as SAGA GIS and SNAP can provide satisfactory results for vegetation coverage dynamics.

Green Space Exposure and Obesity in the Mexican Adult Population.

Green space or natural vegetation may reduce obesity risk by increasing opportunities for physical activity or reducing stress and exposure to other pollutants. Obesity prevalence in Mexico is ranked among the highest in the world. However, research on the association between green space and obesity in Mexico is lacking. We used data from the National Nutrition Survey in Mexico (2018-2019), a nationally representative sample of Mexican adults. The analytical sample included participants between 20-59 years of age (n = 12,631). We assessed exposure to green space using a 30 m resolution Landsat satellite Normalized Difference Vegetation Index (NDVI) from 2018. Linear regression models examined associations between NDVI and body mass index (BMI), adjusting for confounders. The mean age of the study sample was 38 (SD 0.19) years. Participants living in areas with the highest green space exposure had the lowest education level (53.51%) and socioeconomic status (28.38%) and were located in central (33.01%), south (30.37%), and rural areas (21.05%). Higher residential exposure to green space was associated with a mean decrease in BMI of -1.1 kg/m2 (95% CI: -1.59, -0.68). This is one of the first studies in Latin America to suggest a protective association between green space and obesity among Mexican adults.

Monitoring and Integrating the Changes in Vegetated Areas with the Rate of Groundwater Use in Arid Regions

Frequent water table measurements are crucial for sustainable groundwater management in arid regions. Such monitoring is more important in areas that are already facing an acute problem with excessive groundwater withdrawal. In the majority of these locations, continuous readings of groundwater levels are lacking. Therefore, an approximate estimate of the rate of increase or decrease in water consumption over time may serve as a proxy for the missing data. This could be achieved by tracking the changes in vegetated areas that generally correlate with changes in the rate of water use. The technique proposed in this paper is based on two remote sensing datasets: Landsat 7 and 8 from 2001 to 2021, and Sentinel 2A from 2015 to 2021, as well as five vegetation indices: Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Transformed Vegetation Index (TVI). The findings have shown that the datasets chosen performed best for small-scale land farms at the research location, which was chosen to be the El-Qaa plain, in the southwestern corner of the Sinai Peninsula in Egypt. Landsat 7 data with a resolution of 30 m revealed a substantial increase in land farms from 2.9 km2 in 2001 to 23.3 km2 in 2021. By using the five indices based on Sentinel 2A data, vegetated areas were categorized as heavy, moderate, or light. In addition, the expansion of each class area from 2015 to 2021 was tracked. Additionally, the NDVI index was modified to better reflect the arid environment (subsequently naming this new index as the Arid Vegetation Index: AVI). Rough scenarios of the increase in water consumption rate at the research site were generated by observing the increase in vegetated areas and collecting rough information from the farmers regarding the crop types.

Using UAV to Identify the Optimal Vegetation Index for Yield Prediction of Oil Seed Rape (Brassica napus L.) at the Flowering Stage

Suitability of the vegetation indices of normalized difference vegetation index (NDVI), blue normalized difference vegetation index (BNDVI), and normalized difference yellowness index (NDYI) obtained by means of UAV at the flowering stage of oil seed rape for the prediction of seed yield and usability of these vegetation indices in the identification of anomalies in the condition of the flowering growth were verified based on the regression analysis. Correlation analysis was performed to find the degree of yield dependence on the values of NDVI, BNDVI, and NDYI indices, which revealed a strong, significant linear positive dependence of seed yield on BNDVI (R = 0.98) and NDYI (R = 0.95). The level of correlation between the NDVI index and the seed yield was weaker (R = 0.70) than the others. Regression analysis was performed for a closer determination of the functional dependence of NDVI, BNDVI, and NDYI indices and the yield of seeds. Coefficients of determination in the linear regression model of NDVI, BNDVI, and NDYI indices reached the following values: R2 = 0.48 (NDVI), R2 = 0.95 (BNDVI), and R2 = 0.90 (NDYI). Thus, it was shown that increased density of yellow flowers decreased the relationship between NDVI and crop yield. The NDVI index is not appropriate for assessing growth conditions and prediction of yields at the flowering stage of oil seed rape. High accuracy of yield prediction was achieved with the use of BNDVI and NDYI. The performed analysis of NDVI, BNDVI, and NDYI demonstrated that particularly the BNDVI and NDYI indices can be used to identify problems in the development of oil seed rape growth at the stage of flowering, for their precise localization, and hence to targeted and effective remedial measures in line with the principles of precision agriculture.

Spatial correlation of groundwater level with natural factors using geographically weighted regression model in the Choushui River Alluvial Fan, Taiwan

The groundwater of the Choushui River alluvial fan in Central Taiwan has been overexploited for a long time. It is essential to understand the factors governing changes in groundwater level (GWL) for the use of water resources. In this study, we first conducted a Mann–Kendall test to identify significant trends in the regional GWL and obtained its spatial characteristics using the Moran’s I index in the Choushui River alluvial fan. Furthermore, we established a geographically weighted regression (GWR) model to explore the spatial correlation between natural factors and GWL in dry and wet seasons from 1999 to 2019. The long-term trend analysis shows that the GWL of the Choushui River alluvial fan decline significantly. The Moran’s I index shows that the spatial distribution of GWL had a positive correlation in both dry and wet seasons. GWR model indicate that the GWL are affected by drainage density (Dd), slope (S), normalized difference vegetation index (NDVI), and precipitation (P) during the dry season, while Dd, S, NDVI, and wetness index (WI) have an effect on the GWL during the wet season. These results can not only describe the model applicability for exploring the relationship between natural factors and GWL but also be used as references for future regional water resource utilization and management.

NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) IN THE EVALUATION OF PHENOLOGICAL DYNAMICS IN DACTYLIS GLOMERATA L. AND MEDICAGO SATIVA L

The fodder species Medicago sativa L. and Dactylis glomerata L. are appreciated for their yield, due to their high perenniality and the large number of scythes in a year, as well as for their different use. In recent years, fodder crops and grasslands have been given a special place in precision agriculture, and by using remote sensing and implicitly the Normalized Difference Vegetation Index (NDVI), the vegetation status of crops is appreciated. In this context, the paper aims to capture the time dynamics, in different phenological stages, of the species Dactylis glomerata L. and Medicago sativa L., based on spectral information extracted from the analysis of PlanetScope satellite images, with a spatial resolution of 3 m. carried out in the experimental lots of the Lovrin Agricultural Research and Development Station - taking into account two plots cultivated with Dactylis glomerata L. (1.22 ha) and Medicago sativa L. (0.8 ha), located in the second and third year of vegetation. The analysis of the spectral information, materialized by NDVI values, with the data collected in the field regarding the dynamics of crop development (BBCH Code) led to the extraction of some useful conclusions for the agricultural practice regarding the health status of the two crops. NDVI values in Dactylis glomerata L. and Medicago sativa L. were different depending on crop evolution. Thus, at the end of winter, the NDVI index had the same average values for both crops (0.746), validated by field observations on crop health. Later on, for the Medicago sativa L. crop, the values of the NDVI index are increasing until sprout phenophase, when green mass was mowed for fodder. In the seed culture of Dactylis glomerata L. the values of the NDVI index are also increasing in the vegetative phase and reach the maximum threshold of 0.833, on 12.05.2020, in the phenophase - 50% of flowering (BBCH), after which it there is a decrease in them alonside with the change of the color of the plants as a result of the appearance of spikelets and later their ripening. The values of the NDVI index immediately after harvest in the two crops are different, being determined by their biological peculiarities.

Land use / land cover changes monitored by NDVI index in Rangamati, Bangladesh for the last four decades

The study of land use/land cover dynamics has been increasingly important in the research of earth surface natural resources. The normalized difference vegetation index (NDVI) is a widely used method for observing land use/land cover change detection. The surface land resources are easily interpreted by computing their NDVI. This study aimed at analyzing Land Use/Land Cover (LULC) changes between 1977 and 2019 in the Rangamati district, Bangladesh using reclassify the NDVI values of the Landsat satellite image and identifying the main drivers to change LULC by household survey. Five different years of Landsat images were used to extract the NDVI values January of 1977, 1989, 2000, 2011 and 2019. The NDVI values are initially computed using the user define method to reclassify the NDVI map to create land use land cover map and change detection. The highest NDVI value was found in 1977 (0.88) which indicates healthy vegetation at that time and thereafter it followed a decreasing trend (0.79 in 1989, 0.74 in 2000, 0.71 in 2011 and 0.53 in 2019) which shows a rapid vegetation cover change in the study area. Analysis of the household survey revealed that population growth, migration from plain land, rapidly urbanization, Kaptai Dam, migration policy of government, high land price, unplanned development, development of tourism industry, firewood collection and poverty have been identified as the major drivers of LULC changes in the study area. Furthermore, analysis of NDVI confirms that the forest vegetation area is being decreased and settlement area and sparseness of vegetation are being increased. The accuracy of the NDVI-based classified images is assessed, using a confusion matrix where overall classification accuracy and Kappa coefficient are computed. The overall classification accuracy was 84% - 90% with corresponding Kappa statistics of 80% - 88% for TM and OLI-TIRS images, respectively. The study serves as a basis of understanding of the LULC changes in the southeastern part of Bangladesh. Bangladesh J. Agri. 2019-2021, 44-46: 127-140

Change Detection of Vegetation Cover Using Remote Sensing and GIS – A Case Study of the West Coast Region of South Africa

This article investigates the possible permanent vegetation cover (VC) change over an extended time for five municipal regions in South Africa by applying satellite-acquired remote sensed normalized difference vegetation index (NDVI) values within a geographic information system (GIS), spatial (West Coast District) and time (1981 to 2019 and 2000 to 2020) context. The NDVI index measures surface reflectance and give a quantitative estimation of vegetation growth and biomass. The study found relevance in its application since VC change detection has taken prominence over the past number of years in terms of sustainable development. Methods of analysis include image mapping, temporal image differencing, Moran I statistic, and the Mann-Kendall trend test. In the main areas that recorded significant changes in their NDVI values (plus or minus 0.4 difference on their original NDVI value) over time, in general, have experienced substantial and permanent VC change. These areas are also spatially clustered and concentrated within specific areas within the wider district. However, these areas constitute only a minority of areas (less than 20%), whereas most of the areas within the district did not experience such significant and permanent change in VC. Instead, the changes that did occur in these majority of areas were related to seasonal variation, i.e., temporal changes.

Evolución espacial y temporal de cultivo del olivo por efecto del ataque de plagas, utilizando sensoramiento remoto y procesamiento de imágenes satelitales

Climate change, as well as the appearance of pests and diseases, are affecting olive plantations (Olea europaea L.) and the production of olives in the world, therefore, there is an urgent need for tools to help us identify the spatial and temporal evolution of the olive groves, regarding the attack of pests, in this case, the Orthezia olivicola and the olive borer Phloeotribus scarabaeoides. In this work, we use information from freely available satellite images that allowed us to carry out spatial and temporal analysis and the combination of vegetation indices. For the studied area, according to the values of the NDVI (Normalized Difference Vegetation Index), it was identified that the surface of diseased plants ranges from 42% to 68%, moderately healthy plants from 2% to 18%, and the state of the plantation considered as very healthy plants with a tendency to zero, which means that practically 100% of the olive trees are affected by some level. The temporal variation of the NDVI, DVI, SAVI, GNDVI, EVI2, and MSAVI indices, allowed us to establish the states of affectation as mild, moderate pest attack, the severity of the pest attack added to the water deficit, and very strong pest attack and state of permanent wilting.

Spatiotemporal estimation of gross primary production for terrestrial wetlands using satellite and field data

The main goal of this study was to determine the GPP at the Biebrza Wetlands for sedges, reeds and grasses habitats, applying satellite data. The ground measurements of Net Ecosystem Exchange (NEE) and respiration (RESP) were conducted at 26 sites at the peatland using chamber method during the vegetation growth from April to October 2015–2020. Applying round-the-clock registrations of Photosynthetically Active Radiation (PAR) and one ground measurement of NEE and RESP the simulation of the daily course of GPP was performed. GPP daily mean values were the object of estimation applying the models based on the satellite data derived from Sentinel 2, Sentinel 3 and MODIS. The main factors of photosynthetic carbon uptake – greenness and moisture were described by satellite vegetation indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Infrared Index (NDII), Accumulated Photosynthetically Active Radiation (APAR), evapotranspiration, latent heat and the differences of surface temperature (Ts) and air temperature (Ta). The models presented as a result of this research can be used applying solely the satellite data. The results of the analysis revealed that NDVI index is the driving factor for the assessment of GPP independently on the type of satellite data (r = 0.64) Up to 40% of GPP variance is explained by the variable connected with the vegetation greenness. The contribution of vegetation moisture indices is about two times smaller. The best fit has been achieved for the Sentinel-3 models with inclusion of the environmental parameters, as NDVI, temperature difference (Ts–Ta), latent heat. The differences of uptake the CO2 between the habitats depending on their phases of phenological development have been presented.