NDVI Vegetation Research Articles

ОЦЕНОЧНЫЕ ПРИЗНАКИ ВИНОГРАДОПРИГОДНЫХ ЗЕМЕЛЬ ПО СПЕКТРАЛЬНЫМ ПАТТЕРНАМ

The purpose of the study is to determine the necessary set of estimated signs of vineyards with terroir properties based on the generalization of literary sources and the setting up of field experience in the application of spectral indices from satellite images. The use of spectral data on the state of soils, plants and the environment makes it possible to evaluate the growth force of bushes and the yield of vineyards by difference normalized indices. Spectral analysis of vineyards using satellite images allows obtaining data on the vegetative and moisture variability of vine plantations with a frequency of 2-7 days. The correct interpretation of spectral satellite images is possible when they are verified by ground studies of soil indicators, vegetation of bushes by phenophases, agricultural work performed in the aisle, soil, air and leaf moisture. In the future, verification of satellite and ground-based data will reduce the number of route-field surveys and laboratory tests. To solve this problem, spectral data from the public satellite platforms Sentinel-2 and Landsat-8 must be calibrated using ground samples from sample plots of vineyard lands with terroir properties, which are defined as spectral patterns (samples) of vineyards. The presence of a relationship between soil moisture, leaf area and yield makes it possible, on the basis of the normalized NDVI vegetation index and NDMI soil moisture, to determine the micro-sites of vine plantations of different productivity and quality of grape harvest. Microzoning of the area according to such parameters as physical and chemical composition, soil moisture, morphometry, exposure and slope slopes makes it possible to identify viticultural areas with terroir properties. Identification of the boundaries and areas of vineyards with terroir properties using spectral raster satellite images with vectorization of difference soil layers in a geographic information system allows you to quickly assess viable lands, the productivity of various areas of vineyards and optimize agricultural work in accordance with a precision approach in viticulture.

ОЦЕНОЧНЫЕ ПРИЗНАКИ ВИНОГРАДОПРИГОДНЫХ ЗЕМЕЛЬ ПО СПЕКТРАЛЬНЫМ ПАТТЕРНАМ

The purpose of the study is to determine the necessary set of estimated signs of vineyards with terroir properties based on the generalization of literary sources and the setting up of field experience in the application of spectral indices from satellite images. The use of spectral data on the state of soils, plants and the environment makes it possible to evaluate the growth force of bushes and the yield of vineyards by difference normalized indices. Spectral analysis of vineyards using satellite images allows obtaining data on the vegetative and moisture variability of vine plantations with a frequency of 2-7 days. The correct interpretation of spectral satellite images is possible when they are verified by ground studies of soil indicators, vegetation of bushes by phenophases, agricultural work performed in the aisle, soil, air and leaf moisture. In the future, verification of satellite and ground-based data will reduce the number of route-field surveys and laboratory tests. To solve this problem, spectral data from the public satellite platforms Sentinel-2 and Landsat-8 must be calibrated using ground samples from sample plots of vineyard lands with terroir properties, which are defined as spectral patterns (samples) of vineyards. The presence of a relationship between soil moisture, leaf area and yield makes it possible, on the basis of the normalized NDVI vegetation index and NDMI soil moisture, to determine the micro-sites of vine plantations of different productivity and quality of grape harvest. Microzoning of the area according to such parameters as physical and chemical composition, soil moisture, morphometry, exposure and slope slopes makes it possible to identify viticultural areas with terroir properties. Identification of the boundaries and areas of vineyards with terroir properties using spectral raster satellite images with vectorization of difference soil layers in a geographic information system allows you to quickly assess viable lands, the productivity of various areas of vineyards and optimize agricultural work in accordance with a precision approach in viticulture.

Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices

Timely and accurate crop yield estimation is important for adjusting agronomic management and enseuring agricultural sustainability. Machine learning (ML) algorithms provide new opportunities to integrate agronomic information with ground-based and satellite data and develop flexible yield predictive models. In particular, satellite-based vegetation indices and evapotranspiration provide robust proxies for crop yield estimations in the absence of measurements; nevertheless, most prior model development efforts have focused on using only vegetation indices due to the simplicity of the process. Additionally, the contribution of input categories (i.e., field, meteorological, and satellite data) and the use of appropriate proxies, aligned with the crop growth stages, in developing yield predictive models have not been adequately investigated. To address these challenges, we employed two ML techniques, Random Forest (RF) and extreme gradient boosting algorithm (XGB), to estimate wheat yield using meteorological variables, satellite-driven actual evapotranspiration (ETa), and vegetation indices (VIs). ETa was separately computed using the surface energy balance concept and the METRIC model. The models were first trained and tested in the study area using three input combinations: i) meteorological variables, ii) satellite data, and iii) an ensemble of meteorological and satellite data. Then, the best-performing model was further evaluated using two independent datasets. We found ETa to be particularly important in improving the accuracy of the model predictions. Among the vegetation indices, EVI, EVI2, and NDVI during May, and among the meteorological data, growing degree days during the grain filling stage plus minimum temperature in the stem elongation stage had the highest contributions to yield predictions. Both ML algorithms generated relatively accurate results, where XGB was marginally more accurate than RF, considering an average mean absolute error of 0.39 t ha-1 for XGB and 0.50 t ha-1 for RF. Normalized root-mean-square errors of the ensemble, satellite-derived and meteorological-derived models in XGB were 0.05, 0.07, and 0.10, respectively. Nevertheless, both algorithms’ performances deteriorated in predicting the yield values beyond the range of the training set, though XGB could handle the extrapolation process more efficiently than RF.

Long‐term and seasonal variation of open‐surface water bodies in the Yellow River Basin during 1990–2020

AbstractOpen‐surface water bodies are essential terrestrial water sources and ecosystem service providers, and their evaluation is necessary for effective water management. In this study, 36 000 Landsat images of the Yellow River Basin (YRB) were collected, and a multiple index (Modified Normalized Difference Water Index, Normalized Difference Vegetation Index and Enhanced Vegetation Index, i.e. MNDWI, NDVI and EVI) method was used to identify open‐surface water bodies in the flood and dry seasons during 1990–2020. Our results showed that during these 30 years, YRB surface waters were characterized by spatial and seasonal heterogeneities. The open‐surface water coverage in the upper and middle reaches of the YRB was 8.74‰ and 4.24‰, respectively, less than the average coverage in the whole YRB of 9.45‰. Surface water area was significant larger in flood seasons than in dry seasons, while during 1990–2020, the surface water area increased by 27.5% in the flood season and by 58.9% in the dry season, reaching 7239.2 km2 (flood season) and 6654.8 km2 (dry season) in 2020. Meanwhile the seasonal variation has decreased. Climate factors (winter temperature and summer precipitation) positively correlated with open‐surface water only in the source region of the YRB. In other regions, anthropic factors (e.g. water conservation projects) strongly affected open‐surface water bodies by increasing their spatial area overall and decreasing seasonal differences in their coverage.

Impacts of Climate Change and Human Activities on NDVI in the Qinghai-Tibet Plateau

Vegetation changes and factors have a profound influence on the local ecology, the economy, and the long-term durability of human construction. This study focuses on the impacts of climate change and human activity on vegetation changes on the Qinghai-Tibet Plateau and aims to develop a dataset of human activity levels on the plateau. Sen and Mann-Kendall trend analysis was used to evaluate the spatial distribution of vegetation NDVI and its trends, as well as the lagged response of plant growth to climatic circumstances. Using a geodetector model, the effects of meteorological and anthropogenic intensity data were examined. The study’s findings show that, although anthropogenic influences and ecosystem vulnerability caused a decline in the region’s vegetation, a stable climate and a healthy ecosystem supported the growth of vegetation. From 2000 to 2017, the area where vegetation grass improved significantly accounted for the highest proportion, reaching 34.22%. Different anthropogenic intensities are distributed spatially, and this interplay of anthropogenic intensities and climatic factors affects the distribution of vegetation greenness more than each element acting alone. The study of how human activity and climate change affect vegetation greenness can offer practical recommendations for maintaining the Qinghai-Tibet Plateau’s natural ecology. It is crucial to responsibly safeguard the Qinghai-Tibet Plateau’s environment in response to the nation’s ecological civilization.

Phenology Metrics for Vegetation Type Classification in Estuarine Wetlands Using Satellite Imagery

While the efficiency of incorporating phenology features into vegetation type classification, in general, and coastal wetland vegetation classification, in particular, has been verified, it is difficult to acquire high-spatial-resolution (HSR) images taken at appropriate times for vegetation identification using phenology features because of the coastal climate and the HSR satellite imaging cycle. To strengthen phenology feature differences, in this study, we constructed vegetation phenology metrics according to vegetation NDVI time series curves fitted by samples collected from the Linhong Estuary Wetland and Liezi Estuary Wetland based on Gao Fen (GF) series satellite images taken between 2018 and 2022. Next, we calculated the phenology metrics using GF series satellite imagery taken over the most recent complete phenology cycle: 21 October 2020, 9 January 2021, 19 February 2021, and 8 May 2021. Five vegetation type classifications in the Linhong Estuary Wetland were carried out using single images of 21 October 2020 and 8 May 2021, along with their combination and the further addition of phenology metrics. From our comparison and analysis, the following findings emerged: Combining the images taken in 21 October 2020 and 8 May 2021 provided better vegetation classification accuracy than any single image, and the overall accuracy was, respectively, increased from 47% and 48% to 67%, while the corresponding kappa was increased from 33% and 34% to 58%; however, adding phenology metrics further improved the accuracy by decreasing the effect of some confusion among different vegetation types, and the overall accuracy and kappa were further improved to 75% and 69%, respectively. Though some problems remain to be further dealt with, this exploration offers helpful insights into coastal wetland vegetation classification using phenology based on HSR imagery.

MANGROVE COVER-BASED VEGETATION INDICES MAPPING USING PLANETSCOPE DATA IN TIEN YEN DISTRICT QUANG NINH PROVINCE

This study focused on mangrove cover mapping by using vegetation indices in Tien Yen district. Mangrove ecosystems play a vital role in many coastal regions, protecting against erosion and storm surges, providing nursery habitats of aquatic organisms, and offering livelihoods to local communities. Besides, this study aims to evaluate the use of PlanetScope data based on vegetation indices for mapping mangrove cover. This study collected and analyzed high-resolution PlanetScope satellite imagery (3m x 3m) to calculate eight different vegetation indices, including CMRI, NDVI, NDWI, GNDVI, BNDVI, SAVI, IPVI, and EVI2. These indices were used to map the mangrove cover in Tien Yen District and to evaluate the accuracy of each vegetation index. The results showed that among all of the indices used, NDVI had the highest accuracy of 96.3% in mapping the mangrove cover in Tien Yen district, followed by CMRI. Besides, the study emphasizes the importance of utilizing remote sensing techniques in supporting conservation efforts and establish sustainable management practices for mangrove forests in Tien Yen district. Additionally, the study presents an overview of the current situation and policy recommendations for the integrated management of mangrove forests, and provides information on sustainable mangrove ecosystem management in the context of climate change adaptation and mitigation in Tien Yen district, Quang Ninh province.

Drought Monitoring Using Remote Sensing Data

The paper deals with the application of the remote sensing for drought monitoring. A way of their application in practice are shown through example for monitoring of drought that is done for three different area in Bulgaria with different pattern, for the same time (July) and for eleven randomly selected years from 1994 to 2009. For the purpose, NDVI, LAI and FPAR vegetation indexes are used. According to Intergovernmental Panel on Climate Change (IPCC) drought and floods are the most severity events threatening the country so attention is paid on drought monitoring.

Examination of vegetation indices based on multitemporal drone images

In Hungary, the use of drones (UAV) has been showing an increasing trend over the past few years. The available UAV’s and the camera systems placed a significant influence and, in some cases, can even limit, their effective and reliable - also appropriate from the data security point of view - application in the assessment of plant growing areas. In addition to the generation of the data, the extractable information content of the produced visual and non-visual data can also be greatly influenced by the processing and analysis methods. During our research, we took multitemporal aerial photographs of winter wheat at different times of the vegetation period. In our article, we will present our results for the NDVI, GNDVI, and VDVI vegetation indices, which include the processing of aerial photographs taken with two different UAVs, highlighting our analyses of the correlations between the discrete and nondiscrete bands of the aerial photographs.

Использование данных дистанционного зондирования SENTINEL-2B для мониторинга последствий разливов нефти

The article provides a comparative analysis of the results of calculating vegetation indices based on remote sensing data from various survey systems in order to determine the suitability of using Sentinel-2B satellite images to assess the degree of damage to natural components affected by oil spills. As the main criteria for assessing the informative capacity of remote sensing data, their spectral and spatial resolution are considered. The results of experimental studies characterizing the spatial distribution of the values of the NDVI and SAVI vegetation indices in the oil spill area are presented, on the basis of which the advantages and disadvantages of the Landsat and PlanetScope survey systems are determined when monitoring the state of open soils, including those contaminated with oil products. In order to increase the reliability of the results of the study, it is proposed to use modifications of the considered vegetation indices.

Studying the growth rate of plants according to the values of the vegetation index

The article discusses a method for assessing the growth rate of plants, as a variant of understanding the ripening time and understanding the harvesting time of an agricultural crop. In most well-known software products, as a rule, they operate with averaged data on the field area, which is a very rough approximation, since fields can have different topography, water supply, soil types, etc., which affects the growth rate of plants. This article presents an approach to assessing the growth rate of an agricultural crop based on the dynamics of the NDVI vegetation index for each pixel of a satellite image. Multispectral images of the MSI Sentinel-2 device are taken as satellite data. The results of the assessment of the vegetation index and growth rate of agricultural crops, as well as the values of temperature, humidity and total precipitation for four months (May, June, July and August) are shown. The proposed results can be used in various farms to estimate the time of ripening and, accordingly, the harvesting of crops. Keywords: Sentinel-2 multispectral instrument, NDVI vegetation index, winter wheat, growth rate estimation.

Влияние условий наблюдения на точность расчёта вегетационного индекса растительности NDVI по данным дистанционного зондирования Земли

Влияние условий наблюдения на точность расчёта вегетационного индекса растительности NDVI по данным дистанционного зондирования Земли

Utilization of Planetscope Imagery for Carbon Stock Estimation of Standing Green Open Space in Jebres Sub-District, Surakarta

The availability of green open space in urban areas is decreasing in line with the increase in population and land use change, which affects carbon stocks and CO2 levels in the air. The less green open space, the less carbon stock is stored. This study aims to determine the spatial distribution of green open space with PlanetScope images and estimate the carbon stock of standing green open space in Jebres, Surakarta. The methods used were allometric equation and linear regression analysis. Sample plots in the field were determined by Stratified Random Sampling. Planetscope images could map green open space with an accuracy rate of 91.3% and an area of 2.0257 km2 consisting of green open space in grass, shrubs, and stands. The results of biomass estimation with NDVI vegetation index and biomass measurement in the field were 0.17563 to 1.93544 tons/pixel, resulting in an estimated carbon stock of 0.0000046 tons/pixel - 0.768 tons/pixel. The accuracy test in this study uses the Standard Error of Estimation (SEE) with a SEE value of 0.256367, a minimum error of 2.35%, and a maximum error of 3.95%, thus proving that Planetscope images can estimate green open space carbon stocks accurately.

Parcel-Level Mapping of Horticultural Crops in Mountain Areas Using Irregular Time Series and VHR Images Taking Qixia, China as An Example

Accurate and reliable horticultural crop classification results are an important part of agricultural management. At present, research on orchard classification based on optical images in complex mountain areas is vulnerable to the influence of cloudy weather, especially in the summer, which leads to a lack of key phenological characteristics. To solve this problem, a parcel-level orchard mapping experiment with an irregular time series was carried out in Qixia City, China. Firstly, the candidate parcels in the study area were extracted from VHR images with a spatial resolution of 0.55 m based on RCF and DABNet models. The F1 score and area-based intersection-over-union (IoU) of the parcel extraction results were calculated. When the boundary buffer radius was 1 m, the F1 score was 0.93. When the radius was 2 m, the F1 score was 0.96. The IoU was 0.872, which shows the effectiveness of the parcel extraction method. Then, based on Sentinel-2 data, the NDVI, EVI, and SAVI vegetation indexes were calculated to construct an irregular time series. A two-dimensional CNN model was used for classification. In order to verify the effectiveness of this method, the study also constructed regular time series for the study area and conducted classification experiments using the 2DCNN and LSTM as classifiers, respectively. Confusion matrices were constructed for the classification results, and the overall accuracy was calculated. The results show that the overall accuracy of the method based on irregular time series is 97.76%, with a kappa coefficient of 0.96, higher than the other experiments, which indicates that the classification method based on irregular time series is effective and can make full use of the fragmented spectral features. Finally, based on the candidate parcels and pixel-level classification results of the study area, the crop categories of the parcels were filled to achieve accurate parcel-level mapping of horticultural crops in complex mountain areas. This method can provide a theoretical reference for orchard crop mapping and serves the development of regional precision agriculture.

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

Digital surface model (DSM) has been widely available for mapping and was also sometimes used for mapping vegetation height. The authors conducted a preliminary study to evaluate the potential use of DSMs derived from ASTER, ALOS, and SRTM for estimating vegetation cover density in mountainous area. This study used NDVI and SAVI vegetation indices, in addition to forest cover density (FCD) model as references for evaluation. A DSM-based volume index (Volindex) concept is introduced, which is the product of the canopy height model (CHM) and the pixel area. CHM was derived from the value difference between the DSM and the reference DEM. The Volindex model was then compared with the NDVI, SAVI and FCD. We found that all DSM-based Volindex models are not accurate enough to represent the vegetation cover density, although the ALOS Palsar-based Volindex could reach 41.53% accuracy and was finally used to predict the vegetation cover density.

Optical Parameters of the Tilia Cordata Mill. Assimilation Apparatus

Morphometric indicators of trees' assimilative apparatus, including their reflection and light-absorbing properties, largely determine their physiological processes. Leaf initiation and development depend on chlorophyll resulting from photosynthesis, water absorption, its movement through a tree and evaporation. The aim of the paper is to evaluate leaf optical properties of Tilia cordata Mill. It was found that optical coefficients of leaf blades vary depending on the time of sample collection: at the end of the growing season, the leaf blade absorptance (Ab) ranges from 68.9% to 72.4%. During the intensive formation of Tilia flowers, the Ab coefficient decreases to 59.2%. It starts to rise before the beginning of the Tilia fruit maturation. The transmittance, Tr, is also lower at the period of intense flower development. Estimating NDVI (vegetation index) and LAI (leaf area index) from Sentinel-2 satellite imagery showed that the ratio between these indices (NDVI-LAI) changes during the entire growing season. The NDVI-LAI correlation varied up to the strongest one during the entire phenological cycle.

Vegetation Growth Trends of Grasslands and Impact Factors in the Three Rivers Headwater Region

Areas of grassland improvement and degradation were mapped and assessed to identify the driving forces of change in vegetation cover in the Three Rivers headwater region of Qinghai, China. Based on linear regression at the pixel level, we analyzed the vegetation dynamics of the grasslands of this region using MODIS NDVI data sets from 2000 to 2010. Correlation coefficients were computed to quantitatively characterize the long-term interrelationship between vegetation NDVI and precipitation/temperature variability during this period. The use of time series residuals of the NDVI/precipitation linear regression to normalize the effect of precipitation on vegetation productivity and to identify long-term degradation was extended to the local scale. Results showed that significant improvements occurred in 26.4% of the grassland area in the Three Rivers Headwater region between 2000 and 2010. The study area, which represents about 86.4% of the total grassland area of this headwater region, showed a general trend of improvement with no obvious trend of degradation.

A distance study of seasonal dynamics of the vegetation index (NVDI) of the Mongun-Taiga massif vegetation cover

This paper analyzes seasonal dynamics of the Mongun-Taiga massif vegetation cover from February to October 2022 based on the application of the NDVI vegetation index using Sentinel-2 satellite imagery data. To analyze the seasonal dynamics of the vegetation index of the Mongun-Taiga massif, 15 points on the valleys of five rivers were selected they are the Tolaity River (south side), the Holash River (west side), the Balyktyg River (north side), the Right-Mugur River (north-east side) and the Shara-Khoragai river (east side). The graphs of the vegetation cover of different exposures of the massif slopes show that the highest values are represented at the western side of the massif (the Holash River), then on the southern side (the Tolaity River) and then on the northern side of the massif (the Balyktyg River). The valleys of the Shara-Khoragai River (east side) and the Right-Mugur River (north-east side) have the lowest indicators of vegetation cover. Different curves of the vegetation cover graphs for different exposures of the massif explain that the climate in this area is very complex. Terrain, slope exposures, humidity, strength and direction of winds, sunlight, temperature, slopes of selected points, as well as watercourses affect vegetation cover differently. For a more thorough study, it is necessary to obtain higher-resolution images and conduct ground-based measurement work to assess the state of the vegetation cover of the massif and obtain accurate meteorological data.

Spatiotemporal variations of Vegetation change and its response to Climate change in Mongolia during 1982-2015

Using GIMMS NDVI growing season (April-October) data from 1982-2015, mean temperature and monthly precipitation data from 60 meteorological station observations for the same period, the vegetation cover change and climate change in the Mongolian region and its response were studied with the help of linear trend analysis and Pearson correlation method the relationship between vegetation cover change and climate change in Mongolia and its response was studied using linear trend analysis, Mann-Kendall trend analysis and Pearson correlation. The results show that the average NDVI of the growing season in Mongolia has gradually increased spatially from south to north over the past 34 years. Seasonally, there was an overall increasing trend of NDVI in all three seasons, and the influence of precipitation on vegetation NDVI was greater than the influence of temperature on it. Different vegetation types responded more to precipitation than to temperature, and alpine grassland vegetation significantly affected air temperature and precipitation.

The Impact of the Urban Built Environment on the Play Behavior of Children with ASD

Anxiety caused by the lack of social skills is the biggest problem faced by children with ASD. Playing can improve children's social skills and relieve anxiety. This study aimed to explore the influence of urban built environments on ASD children's play behavior. The participants in this study were 57 parents of children with ASD. An anonymous questionnaire was used to collect and analyze data. At the same time, retrospective semi-structured interviews with 31 parents of ASD children were performed to validate the data analysis results. The results showed that lower residential building density, higher residential greening and higher destination accessibility have positive effects on ASD children's play behavior. Excellent transportation facilities and high NDVI vegetation coverage have positive effects on the play behavior of children with ASD. More recreational facilities and recreational playability have positive impacts on the play behavior of children with ASD. The population density and number of children in the destination, as well as public facilities, influence the play behavior of children with ASD. The research results can promote the integration of this group into urban life and further promote social equity. At the same time, with the social needs of autistic children as an intermediary, it is expected to further explore new directions for sustainable urban development. Finally, combined with the research results, parents of ASD children are given proposals for how to increase the likelihood of children's play behavior by choosing appropriate urban built environments.