NDVI Values Research Articles

INVESTIGATION OF GREEN SPACE COOLING POTENTIAL ON LAND SURFACE TEMPERATURE IN ANTALYA CITY OF TURKEY

Abstract. The aim of this study is to investigate the potential cooling effect of urban green spaces on land surface temperature (LST). Two significant green areas and their surroundings in the Konyaaltı district of Antalya province were selected as the sample area. Antalya is one of the most important tourist destinations in Turkey and has experienced rapid population growth along with increasing construction. Because the sample area is located in the Mediterranean Climate Zone, the research datasets were limited to a 6-month time series covering May and October, as recommended by previous studies. In the study, cloudless Landsat 8 satellite images were utilized to obtain vegetation density and land surface temperature. After making atmospheric corrections to the satellite images, the researchers used a series of algorithms to calculate NDVI and LST values from Landsat 8's NIR, Red, and thermal bands. A linear cross-section was assigned from the maps, and statistical analysis was performed by applying the Pearson correlation test to the data obtained from the pixels in contact with the section. The analysis revealed that there is a strong opposite correlation between plant density and land surface temperature, and the land surface temperature decreases as the plant density increases. The cooling effect of green areas range from 0.13–1.04 °C, although it varies according to the month. The 1st sample area had an average cooling effect of 0.77 °C, while the 2nd sample area had an average of 0.28 °C cooling effect. Combining the results from both sample areas, the researchers found that urban green spaces provide a cooling effect of 0.52 °C during the hot months of the spring and summer seasons in the Mediterranean climate zone. The results suggest that green areas play an important role in cooling today's cities that have turned into concrete jungles. Therefore, it is suggested that increasing the presence of urban green areas through green infrastructure planning can not only enhance the cooling effect of green spaces in urban areas but also help combat global climate change.

Grassland cut detection based on Sentinel-2 time series to respond to the environmental and technical challenges of the Austrian fodder production for livestock feeding

The relationship between yield and quality of grassland fodder is an important factor for livestock farmers to consider when deciding the timing of grassland cuts. Even for experienced farmers, estimating this relationship is difficult because external factors such as weather can affect the growth rate of plants. Satellite remote sensing techniques have good potential for the modelling of fodder biomass and quality. However, more accurate grassland cut dates are needed. Unfortunately, current cut detection methods vary in both form, function and accuracy and a common framework for cut detection does not yet exist, especially for in-season cut estimation. The aim of this study is to develop an improved methodology for Austrian fodder production systems that can be used for both post-season and in-season cut detection. In the first case, cuts are detected at the end of the vegetation period and used for post-season assessment at national and regional levels. In the second case, cuts are detected in near-real-time during the season, and they can be used to model yield and quality. The approach is tested across the Austrian landscape on mown grasslands using freely available Copernicus Sentinel-2 imagery on the Google Earth Engine (GEE). The method uses a fitted NDVI curve obtained by iteratively smoothing the upper envelope of the actual observations using the Whittaker smoother. Cuts are detected by meeting a threshold in the difference between the fitted NDVI curve and the actual observed NDVI values. Additional innovative steps were developed to reduce the impact of cloud cover on the results for timely and accurate cut detection, including a gradient boosting algorithm to detect false positive cuts due to missed clouds. A set of rules for the method was trained on 97 plots and validated on a further 502 plots over three years (2020, 2021 and 2022). The method performed well in plots with two to four cuts per season, but occasionally struggled in more intensively managed systems, most likely due to a rule preventing two cuts within 27 days. A pooled post-season cut date detection f-score of 0.79 suggests that the method can accurately detect cuts, with absolute detection errors of <5 days. The main misclassifications were the results of (thin and semi-transparent) clouds being erroneously detected as cuts. With an f-score of 0.80 and total absolute delayed detection of approximately 6 days, the in-season cut date detection performs similarly to the post-season configuration. This indicates a high potential for near-real-time applications. Our results moreover indicate a general improvement of existing methodologies despite performing the cut detection across the fragmented, mountainous and diverse Austrian landscape. Future work will include grazed pastures and natural grassland with less intensive human activity.

Explore Vegetation Coverage of Shandong Province based on Remote Sensing

Aiming at the exploring of vegetation coverage in Shandong province, this paper provided a method based on remote sensing. Firstly, the Landsat8 OLI image was selected to be the data resources, and then, the remote sensing image of Shandong Province was separated from the original image by using vector data as a tool. And then, Using the method of band match, the NDVI value of Shandong Province was calculated, and then the FVC value of Shandong Province was obtained using the formula. The FVC distribution map of Shandong Province was plotted using resampling method. Finally, the vegetation coverage of each category was calculated using pixel statistical method. The research results of this article can provide effective reference for environmental protection and sustainable development in Shandong Province.

Plant Growth and Soil Water Content Changes under Different Inter-Row Soil Management Methods in a Sloping Vineyard.

The main objective of this study was to investigate soil-plant-water interactions based on field measurements of plant reflectance and soil water content (SWC) in different inter-row managed sloping vineyards. The following three different soil management applications were studied: tilled (T), cover crops (CC), and permanent grass (NT) inter-rows. We measured SWCs within the row and between rows of vines. Each investigated row utilized 7 to 10 measurement points along the slope. Topsoil SWC and temperature, leaf NDVI and chlorophyll concentrations and leaf area index (LAI) were measured every two weeks over the vegetation period (May to November) using handheld instruments. We found that management method and slope position can significantly affect the soil's physical and chemical properties, such as clay or soil organic carbon contents. Cover crops in the inter-row significantly reduced average SWC. The in-row average topsoil SWCs and temperatures were lower in all study sites compared to the values measured in between rows. Significantly higher SWCs were observed for the upper points compared to the lower ones for CC and T treatments (58.0 and 60.9%, respectively), while the opposite was noted for NT. Grassed inter-row grapevines had significantly lower leaf chlorophyll content than the other inter-row managed sites (p < 0.001). The highest average leaf chlorophyll contents were observed in the T vineyard (16.89 CCI). Based on slope positions, the most distinguishable difference was observed for the CC: 27.7% higher chlorophyll values were observed at the top of the slope compared to the grapevine leaves at the bottom of the slope (p < 0.01). The leaf NDVI values were not as profoundly influenced by slope position in the vineyard as the chlorophyll values were. For overall LAI values, the T treatment had significantly lower values compared to NT and CC (p < 0.001). Moderate correlations were observed between NDVI and LAI and soil nitrogen and carbon content. In general, we found that both inter-row management and slope position can significantly influence soil parameters and affect plant growth, and consequently can accelerate plant stress under sub-optimal environmental conditions such as prolonged drought.

Analysis of Changes in Mangrove Land Cover in West Langsa District, Langsa

This study aims to analyze changes in land cover and mangrove crown density in Langsa Barat District for the period 2000-2020. The method used in this study is the maximum likelihood classification (MLC) to determine changes in mangrove land cover while determining mangrove density using the NDVI equation. Based on the results of the analysis of changes in mangrove land cover there was an increase and decrease in area. In 2000 the area of ​​mangrove land was 1,039.17 ha, then increased in area in 2005 amounted to 1.325,59 ha (27.56%). In 2010, the mangrove area decreased by 1.145,22 ha (15.75%). In 2015, the mangrove area increased to 1,357.10 ha (18.50%), and finally, in 2020 there was an increase in the mangrove area to 2.027.05 ha (49.37%). While the analysis of mangrove crown density using NDVI value from 2000 to 2020, the highest crown density value was in 2020 with the category of the dense crown density of 1,509.40 ha (74.46%), while the medium crown density value was recorded in 2020 namely 517.49 ha (25.53%). Meanwhile, the lowest sparse category canopy density value was recorded in 2020 with an area of ​​0.17 ha (0.01%).

Assessment of Physicochemical Parameters in Two Winegrapes Varieties after Foliar Application of ZnSO4 and ZnO.

One-third of the world's population is suffering from "hidden hunger" due to micronutrient deficiency. Zinc is acquired through diet, leading its deficiency to the development of disorders such as retarded growth, anorexia, infections, and hypogeusia. Accordingly, this study aimed to develop an agronomic workflow for Zn biofortification on two red winegrapes varieties (cv. Castelão and Syrah) and determine the physicochemical implications for winemaking. Both varieties produced in Setúbal (Portugal) were submitted to four foliar applications of ZnSO4 or ZnO (900 and 1350 g ha-1, respectively), during the production cycle. At harvest, Zn biofortification reached a 4.3- and 2.3-fold increase with ZnO 1350 g ha-1 in Castelão and Syrah, respectively (although, with ZnSO4 1350 g ha-1 both varieties revealed an increase in Zn concentration). On a physiological basis, lower values of NDVI were found in the biofortified grapes, although not reflected in photosynthetic parameters with cv. Syrah shows even a potential benefit with the use of Zn fertilizers. Regarding physical and chemical parameters (density, total soluble solids, dry weight, and color), relative to the control no significant changes in both varieties were observed, being suitable for winemaking. It was concluded that ZnSO4 and ZnO foliar fertilization efficiently increased Zn concentration on both varieties without a negative impact on quality, but cv. Castelão showed a better index of Zn biofortification and pointed to a potentially higher quality for winemaking.

Оценка трансформации ландшафтной структуры прибрежной полосы водохранилищ с использованием данных дистанционного зондирования Земли

Developing the reservoirs’ coastal strip in the Moscow oblast is an acute problem, and it has become especially critical during the recent three decades. To assess the impact of the coastal landscape structure transformation on the reservoirs eutrophication, we employed the vegetation index NDVI, which correlates with the chlorophyll A content in the water. Five water bodies were chosen as research objects and the materials of the Landsat 5 and 7 missions for the period from 1985 to 2020 with five-year intervals were used. The spatial and temporal dynamics of biotopes were monitored in 1 km wide coastal strip, and the values of the vegetation index were counted in the area. We have created time series of average index values with the increasing trend approximated through linear and polynomial functions. The dynamics of open and forest biotopes are not the same, and only the built-up area constantly expands. Regression analysis has shown the relationship between the coastal landscape structure and NDVI values. Growing development of areas significantly causes an enhancement of the mentioned amounts.

Assessment of Carbon Sequestration and Sink Potential: A Surrogate for Ecosystem Carrying Capacity in Dolvi Region, India

The socio-economic development activities are aimed to improve the quality of life of the inhabitants along with industrial growth of the region. The development activity proposing expansion or new construction is anticipated to affect environment and hence any ecosystem existing in the vicinity of development activity needs to be assessed. In this study, an attempt has been made to assess the changes in existing ecosystem services due to expansion of industry and construction activities in the Dolvi region. For assessing the carrying capacity of region in such scenario indicators like NDVI, NDWI, Vegetation NDWI and forest ecosystem services such as carbon stock are estimated using geospatial techniques. To account for the possibility and effect of development on forest ecosystem, remote sensing and field analysis was carried out to estimate the status of indicators along with carbon sequestration and sink potential for the region. The NDVI value of 0.87 show healthy vegetation existing in the region. The NDWI has indicated the presence of water indicating region is not under water stress. From the current biomass estimation, the study region shows presence of forest which has biomass as high as 8 Mg/ha at terrestrial vegetation and 5 Mg/ha at mangrove vegetation. This biomass is a result of carbon sequestered in the region as 84.7 Mg/ha. The study confirmed that region has high Carbon stock with sink potential to absorb emissions from the development activities including construction and expansion of the industry in the region.q

Spatiotemporal dynamics of land use/land cover changes and its drivers in Bilate watershed, central rift valley, Ethiopia

Land use/land cover (LULC) changes influence the ecological function, consequence on ecosystem services, which are tightly linked to human wellbeing. However, quantification of the LULC changes and identifying the underlying factors remain patchy particularly in developing nations, despite this information is crucial to propose a feasible restoration action. Therefore, this study investigates the land use/land cover changes and its drivers in central Rift Valley, Ethiopia. GIS and Remote sensors i.e. Landsat 5 (TM), and Landsat 8 (OLI/TIRs) imagery sensors acquired from USGS, and field observation were used. Using the supervised classification method and the support of ArcGIS 10.5 and ERDAS IMAGINE 2014, all images were classified into various land cover types. Focus group discussions, key informant interviews, and structured questionnaire surveys were used to investigate the drivers of LULC change. NDVI was used to detect the vegetation cover change. Woodland, grassland, and barren lands were the major LULC types identified in this study. After 28 years, the woodland cover increased from 20.6% to 40.2% whereas the barren land decreased from 43.4% to 22.6%. Grassland showed very slight increment, i.e. from 35.9% to 36.9%. This implies that area enclosure plays a significant role in the restoration of degraded lands. The highest NDVI values (0.6) were determined in the year 2022 at the end of the classification. Focus group discussants and key informants confirmed that human-induced factors were the major drivers of LULC changes in the study area. Our findings indicated that human interventions are the key determinants of land use/land cover dynamics, and as a result, enforcement of the law and public education campaigns to change human behavior in support of the area enclosure approach are essential to restoring degraded land for the benefit and wellbeing of humans and nature while also advancing the achievement of the global goals.

Crop acreage and yield mapping of groundnut crop in erstwhile Mahabubnagar District using RS and GIS

An investigation was carried in the erstwhile Mahabubnagar district of Telangana during rabi 2019-20 aiming the estimation of groundnut crop acreage and yield. The crop area was estimated using the satellite images of Landsat-8-OLI sensor from September to February covering the entire crop growth period by performing an unsupervised image classification technique with 300 classes, 300 iterations and a convergence threshold of 0.99. The groundnut yield was estimated by developing the regression equation using crop-cut yield data and NDVI values of the corresponding GPS locations. The crop area was estimated to be 57,865 ha with producer’s and user’s accuracy of 100 and 90% respectively, and a relative deviation of 28.6% when compared with actual ground estimates of the Department of agriculture. The crop yields were estimated with an R2 value of 0.71 and a correlation coefficient of 0.87

Reviewing the Status of Droughts, Early Warning Systems and Climate Services in South India: Experiences Learned

Drought is one of the most challenging disasters that impact the natural and cultural ecosystems across the world, especially in the climate dependent sectors of arid and semi-arid areas. The aim of this article is to share the experiences gained and enhance the readers’ awareness on the status of drought and process of the early warning systems (EWS) in south India. Drought status of three agroecologically different states is included in this article, such as Kerala, Tamil Nadu and Telangana. As far as Tamil Nadu is concerned, Karur, Thuthukudi, Krishnagiri, Namakkal, Trichy and Thirunelveli districts are water scarce compared to other districts in the state. The districts such as Wayanad, Thiruvananthapuram, Idukki and Palakkad in Kerala have received lesser rainfall compared to the other parts of the state during the period 1981 to 2019. In Telangana, the mandals such as Nagarkurnool, Jogulamba-Gadwal, Wanaparthy, Mahabubnagar Nalgonda and Yedadri are frequently hit by dry spells and droughts. As a case study, weather early warning dissemination, carried out at Parambikulam Aliyar basin, Coimbatore, Tamil Nadu, during Khariff and Rabi seasons, using IMDs medium and extended range forecast is also elaborated in particular in the article. As far as the accuracy of forecast is concerned, probability of false detection (false alarm rate) was found to be 0.81 for Khariff and 0.30 for Rabi season, indicating the need for better performance in the accuracy of dry spell early warning, disaster preparedness and response. In-spite of this, access to early warning has supported the farmers during harvest and land preparation with a utility score of 72% and 59%, respectively. In Parambikulam Aliyar basin, remote sensing products such as MODIS-NDVI, NDWI and TWI was also used to identify the real-time progression of monthly vegetative condition for Kharif and Rabi seasons. NDVI values were used to monitor the district level vegetation condition and compared it with the drought year 2016, the difference in area under barren land was 76% less during Khariff, 2021 and 44% during Rabi, 2021.This study is a compilation of lessons learned from different states and the existing knowledge and practice in early warnings, and recommends the need for a holistic approach in drought and dry spell monitoring along with better accuracy and dissemination to minimize climate-related shocks in agriculture.

Vegetation Dynamics and Their Response to Climate Changes and Human Activities: A Case Study in the Hanjiang River Basin, China

The Hanjiang River Basin (HJRB) is an important water conservation and ecological barrier area for the South–North Water Transfer Central Project. The quantitative analysis of regional differences in vegetation changes and their main drivers is important for the monitoring of the ecological environment of the basin and formulation of ecological protection measures. Based on MODIS13Q1 data from 2000 to 2020, spatiotemporal variation characteristics of vegetation in the HJRB were analyzed using Theil–Sen + Mann–Kendall, the Hurst index, and correlation analysis. Then, we detected the drivers using an optimal parameter geographic detector. The results showed that from 2000 to 2020, the average NDVI value increased from 0.651 to 0.737, with a spatial distribution pattern of “high in the northwest and low in the southeast”, and 88.68% of the study area showed an increase in vegetation cover, while 5.80% showed a significant degradation. The positive persistence of future vegetation changes is stronger than the negative. It may show a slowdown or degradation trend, among which the vegetation restoration along the Han River and urbanized areas need to be strengthened. The factor detector indicated that the main factors influencing vegetation change were topography and climate, for which the most influential variables, respectively, were elevation (0.1979), landform (0.1720), slope (0.1647), and soil type (0.1094), with weaker influence from human activity factors. The interaction test results showed that the interaction of various geographic factors enhanced the explanatory power of vegetation changes and showed mainly nonlinear and two-factor enhancements. The dominant factor varies between sub-basins; for example, the interaction between wind speed and land use conversion was the dominant factor in the middle reaches of the HJRB; the dominant factor in the lower reaches of the HJRB was expressed as the interaction between land use conversion and temperature. Finally, the effects of the range or category of different drivers on vegetation growth were systematically analyzed. The results of the study contribute to the understanding of the dynamic changes of vegetation based on a comprehensive consideration of the interaction of topography, climate, and human activities, taking into account the totality and variability of the geographical environment, and provide a reference for the ecological restoration and rational use of vegetation resources in the HJRB.

Variation in nitrogen partitioning and reproductive stage nitrogen remobilization determines nitrogen grain production efficiency (NUEg) in diverse rice genotypes under varying nitrogen supply.

Nitrogen (N) is an important macronutrient needed for grain yield, grain N and grain protein content in rice. Grain yield and quality are significantly determined by N availability. In this study, to understand the mechanisms associated with reproductive stage N remobilization and N partitioning to grain 2 years of field experiments were conducted with 30 diverse rice genotypes during 2019-Kharif and 2020-Kharif seasons. The experiments were conducted with two different N treatments; N deficient (N0-no external N application, available soil N; 2019-234.15 kgha-1, 2020-225.79 kgha-1) and N sufficient (N120-120 kgha-1 external N application, available soil N; 2019-363.77 kgha-1, 2020-367.95 kgha-1). N application increased the NDVI value, biomass accumulation, grain yield, harvest index and grain N accumulation. Post-anthesis N uptake and N remobilization from vegetative tissues to grain are critical for grain yield and N harvest index. Rice genotypes, Kalinga-1, BAM-4234, IR-8384-B-B102-3, Sahbhagi Dhan, BVD-109 and Nerica-L-42 showed a higher rate of N remobilization under N sufficient conditions. But, under N deficiency, rice genotypes-83929-B-B-291-3-1-1, BVD-109, IR-8384-B-B102-3 and BAM-4234 performed well showing higher N remobilization efficiency. The total amount of N remobilization was recorded to be high in the N120 treatment. The harvest index was higher in N120 during both the cropping seasons. RANBIR BASMATI, BAM-832, APO, BAM-247, IR-64, Vandana, and Nerica-L-44 were more efficient in N grain production efficiency under N deficient conditions. From this study, it is evident that higher grain N accumulation is not always associated with higher yield. IR-83929-B-B-291-3-1-1, Kalinga-1, APO, Pusa Basmati-1, and Nerica-L-44 performed well for different N use efficiency component traits under both N deficient (N0) and N sufficient (N120) conditions. Identifying genotypes/donors for N use efficiency-component traits is crucial in improving the fertilizer N recovery rate and site specific N management.

ANALISIS HUBUNGAN LAND SURFACE TEMPERATURE (LST) DAN INDEKS KERAPATAN VEGETASI (NDVI) DAS WANGGU, SULAWESI TENGGARA

LST and NDVI analysis in Das Wanggu utilizes Landsat 9 satellite remote sensing. LST calculations usde thermal band 10 and NDVI used Red band (Band 4) and InfraRed Band (Band 5). There are 5 LST classes, 17.25 oC -19.66 oC with an area of 908.16 Ha, 19.66 0C-22.08 oC covering 6973.71 Ha, 22.08 oC -24.49 oC covering 21748.26 Ha, 24.49 oC -26.90 oC covering an area of 4235.37 Ha, and 26.90 oC -29.31 oC with a wide coverage of 81.18 Ha, while NDVI values obtained 3 classes namely NDVI &lt;0.2 of 1783.643 Ha, NDVI with a range of 0.2 – 0.5 covering an area of 28617.74 Ha, and areas with NDVI &gt; 0.5 covering an area of 3544.87 Ha. The amount of LST is highly dependent on the type of land cover and land use. NDVI indicates the presence of vegetation in the study area. A negative relationship was found between LST and NDVI in Das Wanggu with a Correlation Coefficient of -0.179.

Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches

Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.

Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data.

Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use-land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city's north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.

Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?

Biogeography is a key concept associated with microbial terroir, which is responsible for the differentiation and uniqueness of wines. One of the factors influencing this microbial terroir is the vegetation, which in turn is influenced by climate, soil, and cultural practices. Remote sensing instruments can provide useful information about vegetation. This study analyses the relationship between NDVI, calculated using Sentinel-2 and Landsat-8 satellite images of different veraison dates, and microbial data obtained in 2015 from 14 commercial (organic and conventional) vineyards belonging to four Designations of Origin (DOs) from Galicia (northwest Spain). Microbial populations in grapes and musts were identified using PCR techniques and confirmed by sequencing. Statistical analyses were made using PCA, CCA, TB-PLS, and correlation analyses. This study confirms that the NDVI is positively correlated with the diversity of yeasts, both in grapes' surface and must samples. Moreover, the results of this study show: (i) Sentinel-2 images, as well as Landsat-8 images, can establish differences in NDVI related to yeast terroir in grapes and musts, as it is the most relevant DO factor, (ii) Sentinel-2 NDVI and yeast biogeography are moderately to strongly correlated, (iii) Sentinel-2 achieved a better delimitation of the DOs than Landsat-8 and can establish more accurate differences in NDVI-yeast terroir correlations, and (iv) a higher NDVI was associated with the yeast biogeographical patterns of the DOs with higher species richness (S) consisting of weakly fermenting yeasts (Hanseniaspora uvarum, Pichia spp., Starmerella bacillaris, and Zygosaccharomyces spp). However, NDVI values did not correlate well with biogeographic patterns of yeasts previously studied at frequency level (proportion or percentage of each species) in each particular DO. This study suggests that satellite imagery has the potential to be a valuable tool for wine quality management and a decision-making instrument for DO regulators and winegrowers.

Monitoring of Drought Stress in Chinese Forests Based on Satellite Solar-Induced Chlorophyll Fluorescence and Multi-Source Remote Sensing Indices

Greenhouse gas emissions have largely changed the global climate, leading to an increase in the frequency and extent of droughts. Forests are essential natural resources, and they play an important role in maintaining ecological security. Effectively monitoring drought stress in forests can help promote sustainable forestry development. Solar-induced chlorophyll fluorescence is a spectral signal released by vegetation photosynthesis after light absorption. In this study, we used solar-induced chlorophyll fluorescence data (SIF), canopy fluorescence yield (SIFyield) data, vegetation indices (NDVI, EVI), leaf area index (LAI), and fraction of absorbed photosynthetically active radiation (fPAR) to study forest drought stress in the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces in China, respectively. The temporal and spatial ranges of drought stress indicated by the Standardized Precipitation-Evapotranspiration Index (SPEI) values were used as a reference (SPEI ≤ −0.5 indicates the occurrence of drought). Firstly, the standardized anomalous values of SIF, SIFyield, NDVI, EVI, LAI, and fPAR were calculated. The temporal and spatial response abilities of each variable to drought stress were analyzed. Secondly, the correlation between each variable and the drought indicator SPEI was quantified. Finally, the validity and variability of SIF and other variables for drought monitoring were analyzed and verified with a random forest classification model. The results showed that on a temporal scale, SIFyield showed an earlier response to drought stress than other variables and the abnormal change of SIFyield was higher than other variables by 10% or more. Spatially, the range of drought areas indicated by SIFyield and SPEI had more coincident areas than other variables. The overall correlation between SIFyield and SPEI was also higher during the drought period, especially during late drought periods when other variables showed negative correlations. For SIFyield, the correlation coefficients of the Yunnan, Fujian, Shaanxi, and Heilongjiang provinces were 0.57, 0.43, 0.32, and 0.49, respectively. Additionally, the variable importance assessment using a random forest model also indicated that SIFyield is more sensitive to forest droughts. We concluded that SIFyield is an effective tool for monitoring forest drought stress in various regions of China and that it can provide a scientific basis for forest drought monitoring.

HEALTH ANALYSIS OF RICE PLANTS BASED ON THE NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE IN IMAGE OF UNMANNED AIRCRAFT (CASE STUDY OF MERAUKE - PAPUA SELATAN)

Remote sensing technology uses various vehicles including satellites, helicopters, aircraft, and Unmanned Aerial Vehicles (UAV) and Drones. Remote sensing technology is often used in agriculture, especially for monitoring rice fields, helping the age of rice and so on. In the current technological era, drone devices are vehicles that are often used to monitor rice fields which are considered effective, considering that the data obtained is the latest data during flights, this is also balanced with current developments in various fields, especially for capturing air, drones be an alternative choice than other alternatives that are considered conventional. Rice is an important cultivated crop because it is a staple food for 90% of Indonesia's population, and also for the people of Papua in Merauke, which is a national food storage area. However, the obstacle that is often experienced is interference from rice diseases. Therefore a fast and accurate analysis of the health of rice plants is needed using the NDVI or Normalized Difference Vegetation Index which is a method for comparing the level of greenness of vegetation originating from drone imagery, with the value of the NDVI we can know the classification of the health of rice plants. In this study the classification of rice plant health was divided into 4 classes. Very good health is in the NDVI value range 0.721-0.92, for good health the NDVI value range is between 0.421-0.72, and normal health NDVI values are in the range 0.221-0.42, while in poor health the NDVI value is 0.11-0.22. With the utilization of drone device technology, it is possible to analyze rice plants per hectare with a normal health classification with an area of 14,877,315 Ha. Whereas in the good health classification the area is 9,846,833 Ha and in the very good health classification the area is 8,922,892

Study on the variation of NDVI, SAVI and EVI indices in Punjab State, India

India is a country where the agricultural economy plays a surreal role in the economic, as well as social development of the country. After the independence, various things changed in the field of agricultural development in which the green revolution played an important role in making Indian people self-sufficient in food production especially states like Punjab shows great potential in agricultural activities due to their geographical circumstances. In the last 35 years change in cropping patterns has increased the land area for agriculture causing a rise in water demand for agriculture. The focal objective of the present study is to estimate the variation in the value of NDVI, SAVI and EVI indices in Punjab State, India. The line graphs were plotted to represent the monthly variation in NDVI, EVI, and SAVI indices for all the selected regions and it was observed that in the Gurdaspur region huge variation in the selected indices from 2017 to 2018. ANOVA results indicated that these indices have varied significantly in Amritsar, Ferozepur and Hoshiarpur. and it was also noted that NDVI and SAVI have a positive correlation according to the Pearson correlation table. These results indicate the changing land use and cropping pattern which shows the diminishing and increasing agricultural land.