NDVI Values Research Articles

Characterizing Khetri copper mine environment using geospatial tools

Mines result in land use and land cover (LULC) change due to degradation of natural resources and establishment of new infrastructure for ore extraction and beneficiation. The present study was carried out to, with objectives, (1) characterize LULC change (from 1975 to 2017) in Khetri copper mine region, (2) spatial distribution of pollution indices and (3) spectral response of elemental concentration of soil and groundwater using Landstat and ASTER satellite data. The study was designed to fulfil the objectives and for the same NDVI values were calculated for LULC classification and generated maps were analyzed for landscape pattern. Spatial distribution of pollution indices calculated using geochemical data of soil and groundwater was plotted to understand the impact of contamination on landscape pattern. The correlation of spectral response of Landstat bands with heavy metals concentration was plotted to assess their possible use in quantification of heavy metals. Results show constant increase in settlements, mines and open area while vegetation cover has decreased. Landscape and class level metrics (number of patch, patch density, aggregation index and landscape shape index) indicate increase in the fragmentation of landscape in recent years. Shannon’s Evenness Index indicates increase in uniformity in landscape and it is attributed to loss of vegetation and agriculture patches. Pollution indices, Pollution Load Index for soil is high near the overburden materials and Index of Environmental Risk (IER) and Contamination Index for ground water is high near abandoned mines. Spectral bands 5 and 6 (SWIR 1) show significant negative correlation, and 9 (Cirrus) shows significant positive correlation with metal concentration in soil and water suggesting the possible use of remote sensing in assessment of metal concentration at ground level. Thus, it can be concluded that mines significantly influence the landscape pattern and remote sensing could be used for the assessment and predication of heavy metal contamination at broader scale in a cost-effective way.

Physiological differences at different growth stages of wheat and their effect on yield and yield attributing traits

India being the second largest wheat producer in the world provides 13% of global wheat supply. Traditional breeding approaches are limited by the low genetic variability, complexity of stress tolerance traits and yield components and the lack of efficient selection techniques under stress conditions. Although the physiological tolerance indices are not directly related to yield but their use might prove increasingly useful. Integration of physiological traits complimented with efficient breeding methodologies will drive the present research to the required wheat yields. A set of twelve wheat lines were used to study the effect of different physiological parameters on the overall yield and its component traits. Progressive increase in NDVI values was noticed during the vegetative phase and a definite decline in values was observed during milk development to ripening stages in the genotypes. In high yielding genotypes a sharp decline of NDVI values was observed from anthesis to milk development stage. This set of genotypes generally showed a slower increase in NDVI values from seedling growth stage to inflorescence emergence stage and faster decline from inflorescence stage to ripening stage. The grain filling occurs in high temperatures and genotypes showing faster utilization of green matter showed higher yield.

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

RETRACTED: Analysis on the spatial ecological distribution model of landscape plant community

Because the spatial pattern of plant communities in garden landscapes usually exhibits highly non-random distribution characteristics, the accuracy of the analysis results is not high. For this purpose, a spatial ecological distribution model of landscape landform plant communities is designed. Based on the remote sensing images of landscape plant communities, the study area was obtained. The grassland communities in this area were classified and calculated by geostatistics, and the semi variogram values of typical samples of different vegetation types were obtained. According to the calculation results, the spatial terrain factors of landscape plant community are extracted, and the annual NDVI value is taken as the ecological vitality index of landscape green vegetation, the NDVI level is divided, the spatial ecological distribution model of landscape plant community is constructed, and the evaluation index system is generated, so as to complete the spatial ecological distribution model analysis of landscape plant community. The results show that the method of this study accurately analyzes the area of coniferous and broad-leaved mixed forest, mountain meadow, road, construction land, water area land and slope grade, which is slightly different from the actual value, indicating that the method has certain reliability.

Rice Crop Phenology Model to Monitor Rice Planting and Harvesting Time using Remote Sensing Approach

Rice is one of the most significant food commodity products in Indonesia. The production of rice in 2019 reached 49.8 million tons. On a global scale, rice is consumed by half of the human population around the world. This study will support the development of sustainable natural resources management, which is an important thing to the realization of the Sustainable Development Goals in zero poverty and zero hunger. Remote sensing is a useful instrument to monitor natural resources. This study used Sentinel-2 imageries to extract rice phenology using vegetation indices (NDVI and NDWI), then acquired the planting and harvesting time using the temporal analysis. The NDVI value is showing a parabolic curve regarding the planting stage of the rice. The value of NDVI is high in the transplanting stage but decreases in the harvesting phase. Besides that, in the seedling and transplanting stage, NDWI has a higher value than NDVI. However, in tillering until the harvesting phase, NDWI has a similar characteristic but lower value than NDVI. Based on the spatial and temporal distribution of rice planting and harvesting date, it is known that climate is not a resistant factor, especially the irrigated rice field. Nevertheless, in the rainfed rice field, the planting time depends on climate conditions.

Автоматизоване розпізнавання міської рослинності та водойм за знімками супутника «Jilin-1А»

The results of testing the developed techniques for automatized recognition of vegetation and water bodies on the urban territory by multispectral images from the Jilin-1А satellite are given.The research included automatized recognition of vegetation and water bodies on the selected observation territory based on images with super high spatial resolution in the visual and infrared range and consequent comparison of the obtained results with the results of visual decoding. The obtained results of processing the images from the Jilin-1А satellite in accordance with the proposed techniques confirmed the sufficiently high accuracy of automatized edge enhancement of recognized objects as compared to the results of interactive visual recognition of these images. Different test areas provided a good separation of vegetation and water types with the same thresholding customization. The accuracy of automatized classification of vegetation and water bodies (without considering the standard errors) for different test areas was within 81...92%, and values of kappa-coefficient were within 0.68 to 0.85. Comparison of normalized index images received from Jilin-1А and Sentinel-2A satellites showed slight discordance in NDVI values and significant discordances for NDWI and MNDWI that are caused by the usage of different spectral channels (SWIR and NIR). These discordances can be sufficiently reduced when using correction coefficients.Analysis of the influence of output image resolution reduction (from 10 to 8 bit) and subsequent informational compressing (JPEG lossy and JPEG2000 lossless) on results of automatized recognition of vegetation and water bodies confirmed the validity and efficiency of these techniques. The volume of saved and transmitted files significantly decreased (in 80…100 times) with a slight reduction of classification accuracy (by 1...2 %). The proposed techniques make it possible to increase significantly the efficiency and probability of renewing maps of big cities and to reduce financial expenditures as compared to the traditional ground GPS-surveying and aerosurveying. The high-level automatization of image processing and minimization of necessary calculations (as compared to techniques that use complex classifiers and neural networks) allow to implement the developed technique as a geographic information web service that satisfies the needs of a wide circle of government services and commercial structures and can be useful for megalopolis population and tourists.

Classification and Recognition of Crops Based on GIS Image

Today, due to the rapid development of science and technology, new science and technology constantly change the status of agriculture, precision agriculture is developing rapidly. Precision agriculture is the combination of three new technologies: Global Positioning System (GPS), Remote Sensing Technology (RS) and Geographic Information System (GIS). In the 3S technology, GIS technology is the basis of realizing the other two technologies. It is a platform to achieve precision agriculture, supporting the input, analysis and output of agricultural resources data and other functions of precision agriculture. Classification and recognition of crop types is one of the most basic applications of precision agriculture. It can extract information from remote sensing images, accurately identify crop types and determine crop growth. In this paper, 7200 remote sensing image samples are divided into two groups: learning group and testing group. Combined with GIS image and RS technology, the characteristics of these four crops were summarized and counted through feature image extraction and feature data analysis of 5200 study group samples in GIS system. 2000 samples in the test group were used to calculate the recognition rate. Through experiments, we can know the recognition rate of peanut, spring maize, sweet potato and summer maize. The highest recognition rate is 98.60%, the lowest recognition rate is 90.20%, and the total recognition rate is 94.10%. It shows that in this experiment, the crop recognition rate of remote sensing image is very high in the application of classification and recognition based on GIS crop images. During the period when the spectral characteristics are very obvious, the recognition rate of crops in remote sensing images is higher, and the total recognition rate is increased by 3.98%. The NDVI values of crops in remote sensing images were analyzed and calculated, and the NDVI values of four crops in each growing period were obtained. By studying the NDVI value, we can determine the types of four crops and their growth period, and the accuracy of determining the types of crops is higher than that of determining the growth period of crops. Through the classification and identification of crop species and crop growth period, two experiments verify that the combination of GIS image and remote sensing image can improve the recognition accuracy of crop classification and identification, and the use of geospatial data in GIS can identify the differences between crops. By combining GIS images with remote sensing images, the development of precision agriculture can be promoted, and the reference for agricultural production and agricultural structure adjustment in designated areas can be provided.

Study on soil physical and chemical properties and carbon and nitrogen sequestration of grassland under different utilization modes

This research investigated the effects of natural grassland and artificial grassland degradation on soil physical and chemical properties, in particular the change in carbon and nitrogen contents and carbon sequestration. Representative areas of natural grassland, grazed artificial grassland and mown artificial grassland in Ar Horqin Banner, Inner Mongolia, were selected for study. Parameters measured in the study area were soil moisture content, soil bulk density, soil microbial content and soil nutrient levels. Growth status of the study sites was also evaluated using the NDVI value and in general the ranking for growth status under the different utilization modes was: mown artificial grassland>grazed artificial grassland>natural grassland. Soil water content in the 0-20 cm depth ranked: grazed artificial grassland>mown artificial grassland>natural grassland, and the differences were significant (P mown artificial grassland>natural grassland. The soil microorganism nitrogen content of the grazed artificial grassland for the 0-10 cm depth was (28.45±8.30) mg•kg-1, which was significantly higher than values in the natural grassland and mown artificial grassland (P grazed artificial grassland>mown artificial grassland, and soil carbon and nitrogen storage of natural grassland and grazed artificial grassland for the 10-30 cm depth were significantly higher than that of mown artificial grassland (P<0.05). The performance characteristic of grazed artificial grassland was soil carbon and nitrogen accumulation, while that of mown artificial grassland was carbon and nitrogen loss. In addition, with increase in soil depth, the carbon and nitrogen storage of natural grassland and mown artificial grassland show a decreasing trend. Thus it can be seen that the establishment of artificial grassland and human intervention such as seeding and irrigation will effectively improve soil quality and grassland growth status, but to a certain extent there may be a negative effect on soil carbon and nitrogen sequestration potential.

Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.

The use of precision farming technologies in the cultivation of winter wheat in the conditions of the Orel region

The analysis of the use of precision farming systems for the cultivation of winter wheat of the Moskovskaya 39 variety in the climatic and soil conditions of the Orel region of the Russian Federation is carried out. The value of the vegetation index NDVI was used as a reference indicator for determining the amount of macronutrients introduced. The effect of applied fertilizers on winter wheat plants was found to be stimulating for their targeted distribution. The application of fertilizers without taking into account the needs for this site led to uneven plant development, lodging, a decrease in yield and an increase in the cost of production.

Spatiotemporal Evolution Characteristics of Ecosystem Service Values Based on NDVI Changes in Island Cities

Vegetation plays a dominant role in and serves as the structural foundation of island-land ecosystems. Island cities, as sea-land ecological complexes, are able to somewhat reflect the general realities of island ecological environments. By using the Landsat series remote-sensing images and an ecosystem service value (ESV) evaluation system, in this paper, the temporal and spatial evolution characteristics of the NDVIs and ESVs of China’s 12 island counties over the past three decades are analyzed. According to the research results, (1) The average NDVI of China’s island counties dropped from 0.380 to 0.347 in 1990-2018, reflecting a continuous downtrend. The NDVIs of Changhai County and Changdao County show significant changes, with decreases of 45.24% and 37.57%, respectively. (2) The total ESV of China’s island counties showed a “V”-shaped change trend from 1990-2018; the minimum value reached USD 362 million in 2010, then essentially recovered to the 2000 level in 2018. (3) The high and low values of NDVI and ESV in island counties over the years were distributed with the spatial characteristics of high values in the center of the islands and low values at the edges of the islands. The island counties north of the Yangtze River showed the largest changes in NDVI and ESV. (4) Forest cover is the main factor that drives ESV reduction in China’s island counties, followed by grassland cover. This study provides an effective method to study the changes in the NDVI and ESV of island cities and proposes the strengthening of island vegetation protection and the promotion of sustainable development in the ecological environments and economies of island cities.

基于MODIS NDVI的长江中游区域植被动态及与气候因子的关系

基于1999-2015年的MODIS NDVI时间序列遥感数据，应用趋势分析、变异系数、重标极差分析和偏相关分析等方法，分析了长江中游的植被时空变化特征及其与气象因子的关系。结果表明，长江中游地区NDVI均值总体上呈上升趋势（从0.72增加到0.80）。从空间分布来看，NDVI低值区域（0.1-0.5）占1.40%，高值区域（>0.7）占87.15%；NDVI空间格局呈"西高东低、北高南低"的分布特征，低值区域表现为以三省省会城市为中心向外辐射。Hurst指数显示，研究区大部分区域（60.54%）的NDVI变化趋势具有不确定性，持续性改善区域（34.78%）主要分布在西部山地区，持续性退化区域（3.26%）主要分布在人类活动频繁的较发达城市区域。在年际尺度上，研究区NDVI与各气象因子关系均不显著；月际尺度上，NDVI与降水、相对湿度和日照时数显著相关，降水和日照时数有明显的时滞性。区域内NDVI动态趋势以不确定性发展为主，城市群周边NDVI呈现持续退化的区域应该引起关注。;The middle reaches of the Yangtze River (including Hubei, Hunan, and Jiangxi Province) and the Central Triangle City Grop are located in the center of the Yangtze River Economic Belt and are one of the key national ecological secrity areas. The dynamic change of vegetation is affected by geographical environment and climate change, which is closely related to the future regional sustainable development. Based on MODIS NDVI time series remote sensing data from 1999 to 2015, the paper analyzed the dynamic characteristics of vegetation and its relationship with climatice factors in the middle reaches of the Yangtze River from 1999 to 2015 by applyingtrend analysis, coefficient of variation, rescaled range analysis, and partial correlation analysis, etc. The results showed that the average value of NDVI in the middle reaches of the Yangtze River showed an overall upward trend (increased from 0.72 to 0.80). From the perspective of spatial distribution, the low-value area of NDVI (0.1-0.5) was 1.40%, and the high-value area (>0.7) accounted for 87.15%, overall pattern of NDVI was high in the west and and north region and low in the east and south region. The low-value areas were radiated outward from the capital cities of the three provinces. Based on hurst index, the changing trend of NDVI was uncertain in most areas (60.54%) of the study area. The continuous improvement area (34.78%) was mainly distributed in the western mountain area, and the continuous degradation area (3.26%) was mainly distributed in the more developed urban areas with frequent human activities. On the interannual scale, no significant relationship between the NDVI and the meteorological factors were observed. From the correlation between vegetation and climate factors, the relationship between NDVI and meteorological factors was not significant on the interannual scale; on the monthly scale, precipitation, relative humidity and sunshine hours were the main factors affecting NDVI change, and precipitation and sunshine hours had obvious time lag; on the spatial correlation, the area of positive correlation between vegetation change and temperature and precipitation was larger than that of negative correlation, and the area of positive correlation with relative humidity was larger than that of negative correlation The area with negative correlation to humidity and sunshine hours was larger than that with positive correlation. The dynamic trend of NDVI in the middle reaches of the Yangtze River was mainly uncertain, and the regions around the urban agglomerations showing continuous degradation of NDVI should be concerned.

ASSESSMENT OF THE ANTHROPOGENIC LOAD OF URBANIZED AREAS BASED ON EARTH REMOTE SENSING DATA

The high anthropogenic load characteristic of urban settlements entails the need for constant monitoring of factors that can potentially have a negative impact on the quality of the environment and the health of the population. Ground-based research methods used for spatial zoning of urbanized territories according to the level of anthropogenic load entail significant time costs, which, despite the high accuracy, significantly reduces their effectiveness. Remote sensing technologies have become a good alternative to ground-based methods. To assess the anthropogenic load of the cities of Central Russia (Voronezh, Lipetsk, Belgorod), an archive of multi-channel satellite images obtained from Landsat-7 and Landsat-8 satellites has been created. The satellite images are grouped into three periods (2001, 2016 and 2020). The processing of satellite images of the studied cities of Central Russia, as well as suburban areas, was carried out in the Scanex Image Processor software package. Spatial assessment of the ratio of the areas of anthropogenic-altered territories and the natural framework was made by determining the value of NDVI within cities and suburban ten-kilometer zones. For the analysis of satellite images of the above-mentioned time periods, equal areas of territories were allocated, where the NDVI indicators of the studied urbanized territories of the cities of Voronezh, Lipetsk, Belgorod, as well as suburban tenkilometer zones with subsequent spatial geoinformation zoning of territories according to this indicator were calculated. The obtained results made it possible to study a number of environmental quality parameters (the level of anthropogenic load, the natural framework of the territory, hydrological objects), as well as their dynamics over a twenty-year period.

Monitoring of rational use of the agricultural land: A case study of the Akmola region for 2018-2020 years period

The use of Remote Sensing methods and GIS technology which allow determining the dynamics and features of the rational use of agricultural land is competent. Up to now, there is no complete understanding of the assessment of the current state of unused agricultural land. It is important to monitor the effective use of arable land by farmers, which means timely sowing and rest of the soil. The degree of the rational use and non-use of agricultural land in the case of the Akmola region based on remote sensing data and satellite observations using GIS technology was determined and analyzed. Using the tool “Zonal Statistics”, the maximum, minimum, and average NDVI values of each field were calculated. The rich agricultural potential of the Egindykol region has a great ability to ensure the food security of the Republic of Kazakhstan, constantly improving the rational use of agricultural land.

Оценка связи NDVI степной растительности и радиального прироста сосны ленточных боров в засушливых условиях юга Западной Сибири

This article presents a spatial-temporal analysis of the NDVI vegetation series (based on the MODIS satellite data) and dendrochronological data obtained for the steppe regions of the Altai Territory. NDVI series are built for five polygons of natural and natural-anthropogenic steppe landscapes for the period 2000–2018. Experimental areas of natural landscapes are located in coastal and specially protected natural areas, natural and anthropogenic arable lands. Most of the points are located in the dry-steppe sub-provinces of the steppe zonal area, a smaller part is in the arid-steppe (including the dendrochronological area). The chronology of the tree ring width is built for the Scots pine in the steppe part of extrazonal belt pine forests. A positive trend in vegetation indices change and a weak positive trend for the tree-ring chronology of the pine were found for the NDVI series of almost all polygons for the study period. A more pronounced positive trend is characteristic of the area with the lowest average NDVI values, while a negative trend is characteristic of the area with the highest average values of the vegetation index. The correlation of the NDVI series averaged over polygons with each other ranged from 0.54 to 0.64 (significant at p &lt; 0.05). The dependence of the analyzed series on the dynamics of moistening of the territory was revealed. The correlation coefficients of the Selyaninov hydrothermal coefficient with the NDVI series ranged from 0.51 to 0.76, and with the tree-ring chronology was 0.63 (significant at p &lt; 0.05). A statistically significant relationship was established for some points between the chronology of the pine and the NDVI series, mostly related to dry steppe vegetation.

КОМПОНЕНТНЫЙ АНАЛИЗ СИСТЕМООБРАЗУЮЩИХ ФАКТОРОВ ГЕОСИСТЕМ ПАВЛОДАРСКОЙ ОБЛАСТИ

The article presents the results of identifying the system-forming factors of geosystems and their hierarchy in the Pavlodar region on the basis of the component analysis model, which allow us to obtain data on the structure of the relationships of parameters for the formation of information about the current state and forecast of the dynamics of geosystems in the development of the optimal structure of nature management. Separate quantitative indicators of geosystems obtained on the basis of cartographic materials, using the spatial interpolation method, are considered, and numerical values of the following parameters are used as initial data: duration of sunshine, radiation balance, average annual precipitation, average annual river runoff, maximum river runoff, coefficient of variation or variability of annual runoff, turbidity, average slope, average slope exposure, average NDVI value, etc.. In the calculation of the value matrix in geosystems, the "FACTOR" software is used to build components (programmer T. F. Savina on algorithms of L. M. Pavlichenko). Based on the analysis of scientific papers, the possibility of component analysis to combine statistical modeling and system analysis of specific objects (geosystems) is shown. The use of system analysis in the construction of component analysis is demonstrated, which allows you to identify the functions of the geosystem and rank them by their contribution to the total variance of the system, taking into account the nature of the relationship and self-organization of the system.

Assessment of the ecological stability of the landscapes of the Pavlodar region

There are presented main results of the assessment of the ecological stability of the landscapes of the Pavlodar region, performed by GIS. The assessment of the ecological stability of the study region was carried out using a series of indicators: the area of water resources, the area of specially protected natural territories, the average NDVI value, the area of buildings, the average population density, the length of transport routes, the area of arable land, as well as the area of man-made territories that have an impact on the natural environment, both positive and negative. There has been compiled map of the ecological stability of the landscapes of the study region.

Геоинформационный анализ изменчивости состояния природной среды после извержений вулкана Чикурачки по данным дистанционного зондирования земли

Explosive volcanic eruptions pose certain danger for natural environment, transport communications and other objects of human economic activity due to the fact that during such eruptions, up to several cubic kilometers of volcanic ash and aerosols can enter to atmosphere in long time. The research of extent of the impact of volcanic eruptions on surrounding area and the determination of their consequences contributes to reasonable assessment of volcanic hazard and possible risks in time conduct of economic activities and ensuring for safe location of settlements, enterprises, sea way and air lines. Chikurachki volcano is one from most active on territory of the Kuril Island Arc. In articles of volcanologists is information about eruptions in 1853–1859, 1958, 1961, 1964, 1973, 1986, 2002, 2003, 2005, 2007, 2008, 2015, 2016. The vegetation index (NDVI) used as means of assessing state of the natural environment. The boundary dividing areas with disturbed and healthy vegetation cover taken along isoline with NDVI value of 0.4. Schematic maps of the dynamics of boundary isolines according for the vegetation index from 1972 to 2020 has been compiled. The dynamics of vegetation cover in the north-eastern, south-eastern and southern sectors relative to Chikurachki volcano is revealed. The north-eastern sector experienced strong negative impact of eruption of the Chikurachki volcano in 1853, at later (2007 and 2015) only ash falls were observed in this direction. The vegetation cover outside the isoline NDVI = 0.4 has almost completely recovered to 2020. The south-eastern sector damaged during eruption of 1986, and was also subject for periodic tephra precipitation and ash falls during 2002–2016. Vegetation was completely destroyed at distance of 9 km from the crater of the volcano, and also valley forests on Tukharka River were destroyed. In southern sector in upper part of the Vernadsky Ridge, harsh growing conditions do not allow vegetation to recover for decades. At the same time, on eastern and western slopes of the ridge, after eruptions with deposition of tephra by layer of small thickness, alder elfin restoration few years. The results of the recearch can be used in forecasting volcanic hazards and rapid assessment of impact on natural environment of territories adjacent to volcanoes of this type as a result of volcanic eruptions.

Снижение значений NDVI в южных тундрах Ямала в 2001–2018 гг. коррелирует с численностью домашних северных оленей

Снижение значений NDVI в южных тундрах Ямала в 2001–2018 гг. коррелирует с численностью домашних северных оленей

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.