Average Normalized Difference Vegetation Index Values Research Articles

Landsat8 위성영상을 이용한 지표면 온도와 식생피복의 관계 분석 : 대구광역시를 대상으로

This study used Landsat 8 satellite imagery to identify land surface temperature (LST) and to create a normalized difference vegetation index (NDVI) for Daegu City for analysis of different land cover types. LST for Daegu showed distinctive patterns depending on the NDVI and land cover had negative correlations with LST and NDVI. In addition, urban and industrial areas accounted for the top 20% of LSTs while forests and waters accounted for the bottom 20%. Forest areas had a high NDVI value of 0.447, average NDVI values were highest for deciduous and lowest for coniferous forests. The results of this study provide a better understanding of the role of vegetative cover in the thermal environment in urban areas which has deteriorated due to climate change. This information will help validate urban planning policies that protect green spaces.

High altitudinal vegetation dynamics including treeline ecotone in Langtang National Park, Nepal

Identification of high altitudinal vegetation dynamics using remote sensing is important because of the complex topography and environment in the Himalayas. Langtang National Park is the first Himalayan park in Nepal representing the best area to study vegetation change in the central Himalaya region because of the high altitudinal gradient and relatively less disturbed region. This study aimed at mapping vegetation in Langtang National Park and its treeline ecotone using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI). Two treeline sites with an altitude of 3927 and 3802 meters above sea level (masl) were selected, and species density was measured during the field survey. The linear slope for each pixel and the Mann Kendall test to measure significant trends were used. The results showed that NDVI has significantly increased at the rate of 0.002yr-1 in Langtang National Park and 0.003yr-1 in treeline ecotone during 2000-2017. The average 68.73% equivalents to 1463 km2 of Langtang National Park are covered by vegetation. At the same time, 16.45% equivalents to 350.43 km2 are greening, and 0.25%, i.e., 5.43 km2 are found browning. In treeline ecotone, the vegetation is mostly occupied by grasses, shrublands and small trees where the NDVI was found from 0.1 to 0.5. The relative changes of NDVI in barren lands are negative and vegetative lands above 0.5 NDVI are positive between 2000 and 2017. The dominant treeline vegetation were Abies spectabilis, Rhododendron campanulatum, Betula utilis and Sorbus microphyla, with the vegetation density of 839.28 and 775 individuals per hectare in sites A and B, respectively. The higher average NDVI values, significantly increased NDVI, and higher density of vegetation in both A and B sites indicate that the vegetation in treeline ecotone is obtaining a good environment in the Himalayas of Nepal.

Quantifying Influences of Natural and Anthropogenic Factors on Vegetation Changes Based on Geodetector: A Case Study in the Poyang Lake Basin, China

Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions.

Quantitative Contributions of Climate Change and Human Activities to Vegetation Changes in the Upper White Nile River

Vegetation changes in the Upper White Nile River (UWNR) are of great significance to the maintenance of local livelihoods, the survival of wildlife, and the protection of species habitats. Based on the GIMMS NDVI3g and MODIS normalized difference vegetation index (NDVI) data, the temporal and spatial characteristics of vegetation changes in the UWNR from 1982 to 2020 were analyzed by a Theil-Sen median trend analysis and Mann-Kendall test. The future trend of vegetation was analyzed by the Hurst exponential method. A partial correlation analysis was used to analyze the relationship of the vegetation and climate factors, and a residual trend analysis was used to quantify the influence of climate change and human activities on vegetation change. The results indicated that the average NDVI value (0.75) of the UWNR from 1982 to 2020 was relatively high. The average coefficient of variation for the NDVI was 0.059, and the vegetation change was relatively stable. The vegetation in the UWNR increased 0.013/10 year on average, but the vegetation degradation in some areas was serious and mainly classified as agricultural land. The results of a future trend analysis showed that the vegetation in the UWNR is mainly negatively sustainable, and 62.54% of the vegetation will degrade in the future. The NDVI of the UWNR was more affected by temperature than by precipitation, especially on agricultural land and forestland, which were more negatively affected by warming. Climate change and human activities have an impact on vegetation changes, but the spatial distributions of the effects differ. The relative impact of human activities on vegetation change accounted for 64.5%, which was higher than that of climate change (35.5%). Human activities, such as the large proportion of agriculture, rapid population growth and the rapid development of urbanization were the main driving forces. Establishing a cross-border drought joint early warning mechanism, strengthening basic agricultural research, and changing traditional agricultural farming patterns may be effective measures to address food security and climate change and improve vegetation in the UWNR.

MANGROVE MONITORING USING NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI): CASE STUDY IN NORTH HALMAHERA, INDONESIA

Community-based ecotourism is an appropriate approach which is effectively preserved mangrove sustainability. As an effort to identify the priority areas of mangrove development, mangrove density levels need to be analyzed. The distribution of mangroves with the lowest average density value needs to be prioritized as a preservation and conservation strategy through community-based ecotourism concept. This article aims to identify the distribution of mangroves using the normalized difference vegetation index (NDVI) model in North Halmahera Regency, Indonesia. Also, discuss the sustainable livelihoods' perspective. This study adopted a mixed-method. Data processing is divided into two stages: first stage, mapping the distribution of mangroves based on density levels; second stage, triangulation. Landsat 8 operational land imager (OLI) in 2013 and 2021 were used as primary data to get the NDVI value, and categorized mangrove density level in Pilawang Cape, Kumo Island, Kakara Island, Maiti Island, and Tagalaya Island. The findings show that Pilawang Cape has the lowest NDVI value. The average NDVI value in 2021 is 0.22 which can be categorized as a rare mangrove forest according to the mangrove standard damage criteria. Thus, Mangrove area in Pilawang Cape needs to be prioritized as a preservation and conservation strategy through community-based ecotourism concept.

Using MODIS-NDVI Time Series to Quantify the Vegetation Responses to River Hydro-Geomorphology in the Wandering River Floodplain in an Arid Region

Vegetation, hydrology and geomorphology are three major elements of the floodplain ecosystem on Earth. Although the normalized difference vegetation index (NDVI) has been used extensively to characterize floodplain vegetation growth, vigour and biomass, methods for quantifying the various distinct responses of floodplain vegetation to hydro-geomorphological changes in different lateral belts in arid regions are still needed. In this study, the Linhe reach was divided into four lateral belts based on their hydro-geomorphological characteristics, and the Moderate Resolution Imaging Spectroradiometer (MODIS)-NDVI time series statistical indicators were used to characterise the distinct changing the patterns of vegetation growth in different belts. The response of floodplain vegetation to river hydro-geomorphology in each belt was analysed. The result showed that the average maximum NDVI value in the regular inundation area was 0.23 and higher than that in the other lateral belts. The correlation between the water persistence time and peak NDVI value in the regular water inundation area was significant (ρ = 0.84), indicating that in contrast to highly frequent or extremely rare water inundation, regular water inundation provides significant benefits to floodplains. Continuous or highly frequent inundation may cause decreased vegetation productivity. Overall, our results suggest that the vegetation greenness response to the river hydro-geomorphology is different from the river to the edge of the floodplain. Thus, a better understanding of the interactions between the floodplain vegetation and river hydro-morphology and river water resource management in arid-region floodplains.

Monitoring Vegetation Patterns at the Syrdarya River Basin from 2000 to 2015 using MODIS Data

The dependence of vegetation condition dynamics as expressed by Normalized Difference Vegetation Index (NDVI) from hydro-climatic factors (Multiyear precipitation, land surface temperature) in the Syrdarya River Basin (SRB) was analyzed for the period of 16 years from 2000 to 2015. The analysis demonstrated a different correlation between NDVI and hydrometric parameters. According to experimental analyses, the average NDVI values reached a maximum in April and minimum in October, while the annual average values of land surface temperature were observed maximum in June and minimum in October. Correlation between precipitation and NDVI was positive and extraordinarily strong in Spring while the correlation between Land Surface Temperature (LST) and NDVI was found negative and strong. Correlation between LST and NDVI changed from positive in spring to negative in summer due to an increase in seasonal temperature and found a decrease of vegetation cover throughout the Syrdarya river basin. Desert vegetation area in plain part of SRB decreased while NDVI of cropland area in Syrdarya and Shu river basins remained the same or increased. Hydro-climatic factors negatively affected a decrease in vegetation cover, which leads to desertification processes.

Response of vegetation ecosystem to climate change based on remote sensing and information entropy: a case study in the arid inland river basin of China

Vegetation is sensitive to regional climate change especially in the arid area, and the effects of global warming on regional climate and ecological vegetation are complex and varied. Spatio-temporal variation of vegetation is closely related to the changing climate, detecting the correlation between the variation of climate and vegetation characteristics in time and spatial dimension is important for understanding the effects of climate change on vegetation ecosystem. Therefore, remote sensing and information entropy were proposed in the study to estimate the spatio-temporal variation of vegetation with normalized difference vegetation index (NDVI) and meteorological elements in the Nalenggele river basin, northwest China. The average NDVI value of the study area showed an increasing trend but more than 40% of the vegetation characteristic in this area showed a worse trend from 1987 to 2016. Temperature is the relatively prominent factor that affects the vegetation variation in this area, but only 12.25% of the vegetation showed strong correlation with temperature. The increasing temperature promoted the growth of vegetation in the oasis region and also caused the degradation of vegetation in the Gobi desert region to some extent. The variation of precipitation and humidity are not the key factors that control vegetation growth, less than 2% of the vegetation showed strong correlation with these two meteorological elements. The results of this study support the conclusion that the fragile vegetation ecosystem in the vast Gobi desert areas shows deteriorating trend in the past several decades closely to the changing climate.

Impacts of soil erosion and forest quality on water quality in Samanalawewa watershed, Sri Lanka

Soil erosion is one of the serious environmental concerns in Sri Lanka which affects on water quality of watersheds. Forest quality is an important indicator of the healthiness of forests which often contribute to mitigate the adverse impacts resulting from watershed disturbances and to improve water quality in river watersheds. Thus, the prime aim of this study is to evaluate and map the rate of soil erosion and forest quality of the Samanalawewa watershed and to establish their relationship with water quality of the watershed. Soil erosion and forest quality of 12 sub-watersheds of Samanalawewa watershed were evaluated using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), InVEST Sediment Delivery Ratio (SDR) and Carbon models developed by the NatCap-Project, Sanford University-USA and also using the Normalized Difference Vegetation Index (NDVI), respectively. Water quality parameters viz. temperature, pH, electrical conductivity, total dissolved solids, dissolved oxygen, biochemical oxygen demand, alkalinity, hardness, nitrate–N, phosphate-P were analyzed in monthly basis for water samples collected from 12 sampling locations (n = 12) in the sub-watersheds within month of May–July 2019. Pearson correlation was conducted to establish the relationships between water quality parameters and soil erosion and forest quality/NDVI of each sub-watershed. The results of the study indicated that the soil erosion of the watersheds ranges from 0 to 3555.7 t/ha/yr with an annual average of 139.9 t/ha/yr, which is ~ 28 times greater than the soil loss tolerance (T) in Sri Lanka. The average NDVI values (forest quality) of the sub-watersheds varied between − 0.2907 and 0.4628. All the water quality parameters in the study area were within the standards limits of SLS (614:2013). Estimation of vegetation cover of the study area using the InVEST Carbon model revealed that the above ground biomass is the dominant carbon storage among the other carbon pools. No significant correlation (p < 0.05) was found between the water quality parameters and the soil erosion rates in the watershed possibly attributed to the mitigation effects of the healthy forest cover within the study catchment. This was further confirmed by the significant negative correlation (r2 = 0.521, p = 0.008) established between the average NDVI values and nitrate–N levels of the river water of the catchment. The findings of the study provided the empirical evidence of the contribution of the healthy forest cover on the improvement of watershed water quality. The outcome of the research is useful for formulating watershed management policies and planning for watershed management

Application of satellite images for assessing winter wheat and spring barley crops on the average NDVI value

The article presents data on the state of the surveyed fields of winter wheat and spring barley of the experimental farm “Integration” of the Orel state agrarian university using satellite images. The dynamic values of the Normalized Difference Vegetation Index (NDVI) were obtained by analyzing satellite images of the experimental plots, starting with early flowering phase of agricultural crops. The corresponding maps were prepared based on these data; the data visualization and access to them were implemented on the GeoMixer base of the WebGIS platform. Digital processing of the information was provided with the digital processing program of the Earth remote sensing ScanEx Image Processor V.5.0. The boundaries of the experimental plots were determined by vector pathes (polygons) built into the digital environment of the composite control system (CСS) of the agricultural organization “AgroNetwork Technologies” (ANT). This software is the basis of the digital platform for the experimental farm of the university. The NDVI values for spring barley in the control zone are very low and amount to about 0.5 in the middle of the season, which predicts the yield to be twice lower than the optimal one.

Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States.

Introduction:Redlining, a racist mortgage appraisal practice of the 1930s, established and exacerbated racial residential segregation boundaries in the United States. Investment risk grades assigned ago through security maps from the Home Owners’ Loan Corporation (HOLC) are associated with current sociodemographics and adverse health outcomes. We assessed whether historical HOLC investment grades are associated with 2010 greenspace, a health-promoting neighborhood resource.Objectives:We compared 2010 normalized difference vegetation index (NDVI) across previous HOLC neighborhood grades using propensity score restriction and matching.Methods:Security map shapefiles were downloaded from the Mapping Inequality Project. Neighborhood investment risk grades included A (best, green), B (blue), C (yellow), and D (hazardous, red, i.e., redlined). We used 2010 satellite imagery to calculate the average NDVI for each HOLC neighborhood. Our main outcomes were 2010 annual average NDVI and summer NDVI. We assigned areal-apportioned 1940 census measures to each HOLC neighborhood. We used propensity score restriction, matching, and targeted maximum likelihood estimation to limit model extrapolation, reduce confounding, and estimate the association between HOLC grade and NDVI for the following comparisons: Grades B vs. A, C vs. B, and D vs. C.Results:Across 102 urban areas (4,141 HOLC polygons), annual average 2010 NDVI was 0.47 (), 0.43 (), 0.39 (), and 0.36 () in Grades A–D, respectively. In analyses adjusted for current ecoregion and census region, 1940s census measures, and 1940s population density, annual average NDVI values in 2010 were estimated at (95% CI: , ), (95% CI: , ), and (95% CI: , ) for Grades B vs. A, C vs. B, and D vs. C, respectively, in the 1930s.Discussion:Estimates adjusted for historical characteristics indicate that neighborhoods assigned worse HOLC grades in the 1930s are associated with reduced present-day greenspace. https://doi.org/10.1289/EHP7495

СОРТОВАЯ СПЕЦИФИЧНОСТЬ ОПТИКО-БИОЛОГИЧЕСКИХ СВОЙСТВ И ФИТОМЕТРИЧЕСКИХ ПАРАМЕТРОВ ПОСЕВОВ ОЗИМОЙ ПШЕНИЦЫ

Identification of varietal specificity of opto-biological properties of field crops is necessary for adequate interpretation of satellite images, rapid determination of phytometric parameters of crops, indication of the level of their development and justification of corrective technological solutions. The aim of the research was to evaluate the opto-biological properties and phytometric parameters of diverse biological varieties of winter wheat; identify their relationship and inter-variety variability in high-tech reference crops based on satellite and ground monitoring. Field studies were conducted in 2019-2020 on the site of competitive ecological variety testing laid on the experimental field of the Orenburg State Agrarian University in the zone of the southern steppes of the Orenburg Cis-Urals (Preduralye). During the study, we found that agrocenoses of different winter wheat varieties differ markedly in opto-biological properties expressed in normalized difference vegetation index (NDVI) values. Varieties ‘Grom’, ‘Don 95’, ‘Bystritsa’, ‘Spartak’, ‘Stanichnaya’ and ‘Sintetik’ were characterized by the lowest average NDVI values during the growing season (0.57-0.59). Higher NDVI values (0.67) were observed in the crops of ‘Zhemchuzhina Povolzhya’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’ varieties. The variability between the varieties according to the lowest (0.51 – ‘Grom’, 0.52 – ‘Don 95’) and the highest (0.69 – ‘Kolos Orenburzhya’, 0.70 – ‘Pionerskaya 32’, 0.69 – ‘Orenburgskaya 105’) NDVI values during the maximum assimilation apparatus development reached 0.17-0.19 units (phase of full spring tillering). By the heading, when NDVI was 0.78–0.79 and 0.85 units, respectively, this difference somewhat leveled but, in general, remained significant – at the level of 0.08-0.11 units (10.9–14.9 %). The largest leaf area (30 thousand m2/ha) was typical for the crops of ‘Saratovskaya 17’, ‘Kolos Orenburzhya’, ‘Orenburgskaya 105’, ‘Rifey’ and ‘Pionerskaya 32’. There was no direct inter-variety relationship between the leaf area and NDVI of winter wheat crops (R 2= 0.38).

Relationship between Boro Rice Production and MODIS Derived NDVI for Rice Production Forecasting: A Case Study on Bangladesh

The present investigation illustrates an inclusive approach to extract remotely sensed Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) (AQUA/TERRA) imageries to find out a relationship with Boro rice production for forecasting crop production in the context of Bangladesh. This study utilizes AQUA/TERRA MODIS reflectance data (250 m resolution) for the month of March (Peak-greenness period) to calculate the average NDVI values by following MODIS based algorithm at district level during 2011-2016. The linear regression analysis of calculated average NDVI and BBS estimated Boro rice production statistics reveals a significant positive relationship due to maximize photosynthetic activities. Among the regression equations from (2011-2016), the highest regression coefficients R2=0.87 and R2=0.85 for AQUA and TERRA MODIS data have been found respectively in 2015. Therefore this regression equation can be used for future estimation of Boro rice production at country scale. However, further testing and simulation of this regression model is required to generate Boro rice production forecasting dataset on timely basis. Hence this study summarizes that, NDVI based regression equation may be an effective process to forecast the Boro rice production which can play an important role in decision-making process relevant to the food security issues of Bangladesh.&#x0D; The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(1), 2019, P 33-40

A remote sensing study of spatiotemporal variations in drought conditions in northern Asir, Saudi Arabia.

Changes in vegetation land cover are influenced by, and therefore an indicator of, climatic conditions. The aim of this study is to investigate the relationship between vegetation cover changes and drought events in a small-scale area. Six Landsat images during 1987-2019 were used to extract information about the vegetation land cover changes using the normalized difference vegetation index (NDVI) and the fractional vegetation cover (FVC) in Balqarn Governorate in the northern mountains of Asir, Saudi Arabia. Two climatic parameters, temperature and precipitation, were used as time series for the same period and were decomposed to investigate the seasonal and trend changes for each parameter. The two parameters were also used to calculate the standardized precipitation evapotranspiration index (SPEI) to conduct an in-depth analysis of the drought events influencing vegetation cover. The results showed that the state of the vegetation coverage of the study area remained at a medium level with an average NDVI value, but the FVC values showed evidence of dynamic variability associated with drought and moisture events. The SPEI showed that the study area has been undergoing a long-duration drought event since 2004, ranging from light to severe drought, which was consistent with the time series decomposition results. This investigation has revealed that drought drives changes in vegetation cover and is expressed on small geographic scales as changes in the vegetation cover structure. The framework described here is simple and can be used to evaluate and manage drought risks.

Spatial and temporal variation of normalized difference vegetation index (NDVI) in the Uben river basin of Gujarat

The Normalized Difference Vegetation Index (NDVI) is important indicators for monitoring of vegetation status. The remote sensing based bio-physical variable maps were used to estimate NDVI in Uben river catchment and command area of Gujarat state, India. Multi date satellite images of Landsat-8 for Rabi season, year 2018-19 were used in the ArcGIS 10.3 software to derive the Normalized Difference Vegetation Index (NDVI). The maximum NDVI in the study area was 0.566 on February 19, 2019.The minimum NDVI in the study area was -0.327 on February 19, 2019. The average NDVI value ranges from -0.132 to 0.368 for wheat crop, 0.092 to 0.294 for coriander crop, 0.098 to 0.204 for cotton crop and 0.083 to 0.103 for waste land.The average NDVI value for wheat and coriander crop was increase from Initial stage to Mid-crop growth stage and then decreased during End season stage. The cotton crop shows higher NDVI value in December i.e. 0.204 then decreased due to harvesting of cotton. The average NDVI values in waste land were less as compared to agricultural land with crop and forest area due to no vegetation and exposed soil.

Assessment of NDVI variations in responses to climate change in the Horn of Africa

The variation of normalized difference vegetation index (NDVI) mainly depends on the change of climatic factors (precipitation and temperature). This study is assessing and analyzing the inter-annual and seasonal change of NDVI in the Horn of Africa (HOA) (1982–2013). The relationship between climatic factors and NDVI values of global Inventory monitoring Modeling System (GIMMS) from 1982 to 2013 was analyzed by means of ordinary least squares, vegetation slope, simple linear regression and correlation coefficients. In addition, the land cover data of the (MODIS) Medium Resolution Imaging Spectroradiometer (i.e. MCD12Q1) are used to assess land-use change from 2001 to 2013 and GIMMS land cover data also applied to extract annual NDVI maximum from 1982 to 2013. The results show that NDVI is proportional to precipitation and inversely proportional to temperature, which precipitation decreased insignificantly while temperature increased significantly for the last 3 decades. In general, NDVI values gradually rise from spring to rainy season and fall steadily from autumn to winter. The NDVI of grassland, savanna and shrub land is more sensitive to climate change than the NDVI of forest and woodland. In grassland, savanna and shrub, NDVI gradients are positive, while in desert areas negative changes are recorded, while forests and woodlands are in transition between stable and positive NDVI gradients. The average NDVI value of each vegetation cover gradually increased from 1982 to 1998 and then slowly decreased from 1998 to 2013 due to precipitation variation. Forest and woodland cover were declined by approximately 0.4% and 1.28% in the past 2 decades. Thus, in the Horn of Africa, land degradation, such as deforestation and desertification, is a serious environmental problem.

An Approach to the Temporal and Spatial Characteristics of Vegetation in the Growing Season in Western China

Since the implementation of the great western development strategy in 2000, the ecological environment in the western region of China has been significantly improved. In order to explore the temporal and spatial characteristics of vegetation coverage in the western region, this paper adopted the method of Maximum Value Composite (MVC) to obtain the mean Normalized Difference Vegetation Index (NDVI) of vegetation on the basis of the Moderate-resolution Imaging Spector audiometer (MODIS) data of 2000/2005/2010/2015/2018. Thereafter, the spatio-temporal differentiation characteristics of vegetation in western China were analyzed. The results show that: (1) According to the time characteristics of vegetation coverage in the western region, the average annual NDVI value of vegetation coverage in the growing season in the western region fluctuated between 0.12 and 0.15, among which that of 2000 to 2010 fluctuated more greatly but did not show obvious change trend. (2) Based on Sen trend and Mann-Kendall test analysis, the area of vegetation coverage improvement in the western region from 2000 to 2018 was larger than that of significant vegetation degradation. (3) From the perspective of global autocorrelation coefficient, Moran’s I values were all positive from 2000 to 2018, which indicates that the vegetation coverage in the west showed strong positive autocorrelation in each period. According to the average value and coefficient of variation of vegetation coverage, the vegetation coverage was lower in 2000, its internal variation was smaller, and the vegetation coverage increased with time. According to the local spatial autocorrelation analysis, the vegetation coverage levels in different regions varied greatly. (4) The standard deviation ellipse method was used to study the spatial distribution and directional transformation of vegetation. It makes the result more intuitive, and the three levels of gravity center shift, direction shift, and angle shift were considered: the vegetation growth condition in the spatial aggregation area improved in 2015; the standard deviation ellipses in 2000 and 2018 overlapped and shifted eastward, which indicates that the vegetation coverage conditions in the two years were similar and got ameliorated.

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China.

BackgroundVegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change.MethodsIn this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation.ResultsThe Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%.ConclusionsOur results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

CANOPY REFLECTANCE MEASUREMENTS USED TO ESTIMATE LATE BLIGHT (PHYTOPHTHORA INFESTANS) INFLUENCE ON POTATO YIELD CANOPY REFLECTANCE MEASUREMENTS USED TO ESTIMATE LATE BLIGHT (PHYTOPHTHORA INFESTANS) INFLUENCE ON POTATO YIELD

"The present study was conducted to investigate potato late blight (Phytophtora infestans) influence on leaf chlorophyll level using datasets extracted from multispectral data captured at the canopy level. Field experiments were carried out to the National Institute of Research and Development for Potato and Sugar Beet Brasov, Romania in 2014-2016 period. It was used a complete randomized block design with four replicates, two planting distances between plants on row (25 and 30cm) and different fungicides, control technologies. Normalized Difference Vegetation Index (NDVI) was introduced to achieve a spectral vegetation index that can separate the vegetation from the uncovered ground. It is defined as the ratio between the infrared bands-red differences and sum them. Due to the different growth conditions, the effects of resulting variants from combinations of factors -varieties-planting density-late blight control technology-on the tubers yield have manifested with different intensity. In two of three years, the average NDVI value of plants grown at a density of 53.3 thousands hill/ha was significantly lower than the values determined at a density of 44.4 thousands hill/ha (NDVI 0.817 compared to 0.859 in 2014 and 0.905 compared to 0.895 in 2015). The correlation between the average values of NDVI yields and tubers were positive."

한반도 식생의 녹색화에 대한 시계열적 분석

The vegetation is feedback on environmental change due to global warming. Also, the growth status of vegetation and the coverage area of vegetation are greatly affected by the environmental changes. The quantitative change of vegetation growth status is the primary task of vegetation response to environmental changes. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) based Normalized Difference Vegetation Index (NDVI) and CRU climate data are used to analyze the spatio‐temporal characteristics of vegetation greening evolution and its response to climate change from 1982 to 2015 in the Korean Peninsula by applying the partial correlation and trend analysis. The results show that the average NDVI value of the Korean Peninsula in the period 1982‐2015 was 0.68, among which the average NDVI in North Korea and South Korea was 0.69 and 0.67, respectively. The NDVI of the Korean Peninsula between 1982 and 2015 increased by 0.6x10SUP‐3/SUP yearSUP‐1/SUP. The increasing trend prior to and after 1998 was 2.5×10SUP‐3/SUP yearSUP‐1/SUP and 0.9×10SUP‐3/SUP yearSUP‐1/SUP, respectively. During the 1982‐2015 years, the NDVI of DPRK and South Korea have grown mainly with the trend of 0.2×10SUP‐3/SUP yearSUP‐1/SUP and 1.1×10SUP‐3/SUP yearSUP‐1/SUP, respectively. According to the analysis of NDVI and climatic factors, the distribution of NDVI in the three‐time series of Korean Peninsula is consistent in spatial distribution. According to the results of partial correlation analysis of climate factor and NDVI distribution in Korean Peninsula, the region has significant partial correlation with temperature change. The climate factor of temperature is the main driver of NDVI change, which plays a key role in controlling NDVI change accumulation.