Average Normalized Difference Vegetation Index Values Research Articles

Assessing Golden Tides from Space: Meteorological Drivers in the Accumulation of the Invasive Algae Rugulopteryx okamurae on Coasts

Massive accumulations of invasive brown algae Rugulopteryx okamurae are exacerbating environmental and socio-economic issues on the Mediterranean and potentially Atlantic coasts. These golden tides, likely intensified by global change processes such as changes in wind direction and intensity and rising temperatures, pose increasing challenges to coastal management. This study employs the Normalized Difference Vegetation Index (NDVI), with values above 0.08 from Level-2 Sentinel-2 imagery, to effectively monitor these strandings along the coastline of Los Lances beach (Tarifa, Spain) in the Strait of Gibraltar Natural Park from 2018 to 2022. Los Lances beach is one of the most affected by the R. okamurae bioinvasion in Spain. The analysis reveals that wind direction determines the spatial distribution of biomass accumulated on the shore. The highest average NDVI values in the western patch were observed with south-easterly winds, while in the eastern patch, higher average NDVI values were recorded with south-westerly, westerly and north-westerly winds. The maximum coverage correlates with elevated temperatures and minimal rainfall, peaking between July and October. Leveraging these insights, we propose a replicable methodology for the early detection and strategic pre-shore collection of biomass, which could facilitate efficient coastal cleanup strategies and enhance biomass utility for biotechnological applications. This approach promises cost-effective adaptability across different geographic areas impacted by golden tides.

Estimation of Soil Moisture from Multispectral Remote Sensing Data

Soil moisture is an important parameter in agricultural and environmental studies and affects the exchange of water and energy at the boundary between the earth’s surface and the atmosphere. Accurate estimation of spatial-temporal changes in soil moisture is critical for numerous studies in the agricultural sciences. Vegetation indices, based on combinations of digital data of reflected light in the visible and near-infrared range, are suitable for the detection of water stress in plants. Therefore, they are widely used to monitor and detect drought conditions, but their accuracy is dependent on different vegetation types. The main objective of this research was to develop a novel procedure for the estimation of soil moisture using multispectral remote measurements. This study has been done using a multispectral camera Survey 3W Red+Green+NIR, which records images in three spectral channels: green (550 nm), red (660 nm) and near-infrared (850 nm). Eleven soil samples with different water content were investigated. SNAP (Sentinel Application Platform) software was used to process the captured multispectral images. It allows an easy calculation of various vegetation indices. The suitability of the normalized difference vegetation index (NDVI) for the assessment of soil moisture was evaluated. The average NDVI values did not indicate a well-established trend in relation to the SWC. A new simple vegetation index NIR/Green was successfully used for the assessment of soil moisture. The new NIR/Green index gives consistent results in relation to the real soil water content and could be used for mapping of the soil moisture with multispectral cameras.

Exposure to Neighborhood Greenness and Hypertension Incidence in Adults: A Longitudinal Cohort Study in Taiwan.

There are few studies on the health effects of long-term exposure to neighborhood greenness in a longitudinal setting, especially in Asian countries with high population densities. This study investigates the association between long-term exposure to neighborhood greenness and hypertension among adults in Taiwan. We selected 125,537 participants ( of age) without hypertension from Taiwan who had joined the standard medical examination program between 2001 and 2016. Neighborhood greenness was estimated using the normalized difference vegetation index (NDVI), derived from satellite images at a resolution of . The 2-y average NDVI value within a circular buffer around participants' residences was calculated. A time-varying Cox regression model was used to investigate the association between neighborhood greenness and incident hypertension. Mediation analyses were performed to examine whether the association was explained by air pollution, leisure-time physical exercise, or body mass index (BMI). Compared with living in areas within the first quartile of neighborhood greenness, living in areas within the second, third, and fourth quartiles of neighborhood greenness was found to be associated with a lower risk of hypertension, with hazard ratios (HRs) and 95% confidence intervals (CIs) of 0.95 (95% CI: 0.91, 1.00), 0.95 (95% CI: 0.90, 0.99), and 0.93 (95% CI: 0.88, 0.97), respectively. Each 0.1-unit increase in the NDVI was associated with a 24% lower risk of developing hypertension (0.76; 95% CI: 0.66, 0.87), with this associations being stronger among males and those with higher education levels. This association was slightly mediated by BMI but not by air pollution or leisure-time physical exercise. Our findings suggest the protective effects of neighborhood greenness on hypertension development, especially in males and well-educated individuals. Our results reinforced the importance of neighborhood greenness for supporting health. https://doi.org/10.1289/EHP13071.

Projected Climate Change Effects on Global Vegetation Growth: A Machine Learning Approach

In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative.

Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data.

Based on the Landsat normalized difference vegetation index (NDVI) and the NDVI product of MODIS, this study synthesized two kinds of time-series images. The features were selected according to the characteristics of the time series, and the random forest algorithm was used for classification. Based on the classification results and GIS spatial analysis, the temporal and spatial changes in vegetation cover in the Yellow River Delta from 2000 to 2020 were studied. The results showed that from 2000 to 2020, the vegetation first increased and then decreased, and the dynamic degree of land cover change was generally low. The monthly average minimum NDVI values during the vegetation growth period mostly occurred before 2010, and the maximum values occurred after 2010. From the spatial perspective, the average vegetation area of the Yellow River Delta accounted for 31.54% of the total study area; specifically, the spatial pattern of vegetation distribution was relatively fixed, and the fixed vegetation area accounted for 63.90% of the total vegetation area. The spatial distribution had significant differences, and the vegetation was distributed radially from the center of the Yellow River to the periphery, with significant fragmentation found outside the watershed. The Yellow River had a strong interference with vegetation growth, and the stable vegetation distribution areas were concentrated near the Yellow River. The correlation coefficient between vegetation distribution and the location of the Yellow River was - 0.9964.

Permafrost degradation services for Arctic greening

As an important component of the climate system, permafrost responds significantly to climate change, and its impact on the ecosystem cannot be ignored. In this study, we analyzed the temporal and spatial variation trends of the normalized difference vegetation index (NDVI) in Arctic permafrost regions and revealed the correlation between the active-layer thickness (ALT), soil temperature, and NDVI change. Using the partial correlation method, we assessed the ecological regulation service of permafrost to the ecosystem. The results showed that both the average annual maximum and summer NDVI values in the Arctic region followed a significant increasing trend from 1982 to 2015. The average correlation coefficient (ACC) between Arctic NDVI and ALT was 0.35, followed by the ACC (0.33) between NDVI and soil temperature at 7–28 cm depth, and had a lower ACC (0.31) at 0–7 cm ALT. When the precipitation and snow water equivalent (SWE) remained unchanged, the partial correlation between NDVI and ALT was 0.711, which was a significant positive correlation. It also showed that permafrost degradation was the dominant factor controlling Arctic NDVI increase, whereas precipitation and SWE had little effect. The study revealed the impact of permafrost on NDVI change, deepened our understanding of the importance of permafrost degradation for ecosystem services, and effectively filled the gap that tundra ecosystem services value has been ignored in the global ecological service value assessment.

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3°C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30°C and 29.91°C, respectively, with a mean LST of 25.1°C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31°C and 25.35°C, respectively, with a mean LST of 21.33°C. There was a 3.8°C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Relationship Between Normalized Difference Vegetation Index (NDVI) and Rice Growth Phases in Danda Jaya Swamp Irrigation Area Regency Barito Kuala

One of the factors that influence the success of rice productivity is rice growth, from land preparation to harvesting. With remote sensing technology, the phase of rice growth can be known more quickly. The purpose of this study was to analyze the NDVI value as a representation of vegetation density characteristics and analyze cluster data grouping based on the results of the identification of the rice growth phase using Sentinel-2 Imagery in the 2019 planting season cycle in Danda Jaya Swamp Irrigation Area. The method used in this study is a quantitative descriptive method with the interpretation of Sentinel-2 Image. Image data were collected in the form of time series in 2019 and downloaded via the Google Earth Engine (GEE) platform. Rice productivity was calculated using NDVI to identify plant vegetation density. Then calculate the statistical zone using a GIS application to get the average NDVI value per rice field plot. NDVI values were analyzed using the K-means Cluster method for class determination. These results support the hypothesis that the time series NDVI value in the sentinel-2 image with a high spatial resolution (10 m) computed from the Google Earth Engine produces a strong correlation with the rice growth phase. The next research development is to analyze the estimation model of rice productivity on Sentinel-2 Image by utilizing the relationship between rice productivity and NDVI.

Spatial distribution of permafrost degradation and its impact on vegetation phenology from 2000 to 2020

As global temperatures rise, permafrost is degraded. Permafrost degradation alters vegetation phenology and community composition, thereby affecting local and regional ecosystems. The Xing'an Mountains, located on the southern edge of the Eurasian permafrost region, are very sensitive to the impact of permafrost degradation on ecosystems. Climate change has direct effects on permafrost and vegetation growth, and analysis of the indirect effects of permafrost degradation on vegetation phenology based on the normalized difference vegetation index (NDVI) can explain the internal impact mechanisms of ecosystem components. Based on the temperature at the top of permafrost (TTOP) model, which was used to simulate the spatial distribution of permafrost areas in the Xing'an Mountains from 2000 to 2020, the areas of the three permafrost types showed a decreasing trend. The mean annual surface temperature (MAST) increased significantly at a rate of 0.008 °C year−1 from 2000 to 2020, and the southern boundary of the permafrost region moved north by 0.1–1 degrees. The average NDVI value of the permafrost region increased significantly in 8.34 % of the region. The significant correlations between NDVI and permafrost degradation, temperature and precipitation in the permafrost degradation region were 92.06 % (80.19 % positive, 11.87 % negative), 50.37 % (42.72 % positive, 7.65 % negative), and 81.59 % (36.25 % positive, 45.34 % negative), and were mainly distributed along the southern boundary of the permafrost region. A significance test of phenology in the Xing'an Mountains showed that the end of the growing season (EOS) and the length of the growing season (GLS) were significantly delayed and prolonged in the southern sparse island permafrost region. Sensitivity analysis showed that permafrost degradation was the main factor that affected the start of the growing season (SOS) and GLS. When the effects of temperature, precipitation, and sunshine duration were excluded, the regions with a significant positive correlation between permafrost degradation and SOS (20.96 %) and GLS (28.55 %) were located in both continuous and discontinuous permafrost regions. The regions with a significant negative correlation between permafrost degradation and SOS (21.11 %) and GLS (8.98 %) were mainly distributed on the southern edge of the island permafrost region. In summary, the NDVI changed significantly in the southern boundary of the permafrost region, and this change was mainly attributed to permafrost degradation.

Development of a Monitoring Method Using UAVs That Can Detect the Occurrence of Bark Stripping by Deer

The occurrence of bark stripping associated with increased deer densities can severely damage forests. Identifying trends in bark stripping is crucial for forest management, but such data are often difficult to obtain through field surveys. Therefore, this study aimed to develop an efficient monitoring method using unmanned aerial vehicles (UAVs) that can detect the occurrence of bark stripping and enable long-term monitoring. The area around the Ochiai Pass in Higashi-Iya Ochiai, Miyoshi City, Tokushima Prefecture, Japan, was selected as the study area for the survey of Abies homolepis, which was found to be significantly bark-stripped by deer in the field. The location and root diameter of A. homolepis were measured, and the percentages of bark stripping and tree growth were visually determined. Simultaneously, normalized difference vegetation index (NDVI) and visible light orthomosaic images were produced using a UAV. A canopy polygon of A. homolepis was created, and the average value of the NDVI within the polygon was calculated. Where the bark stripping rate at the root edge was greater than 75%, the number of “partially dead” and “dead” trees increased significantly, indicating that bark stripping by deer was the primary cause of the death of A. homolepis in Ochiai Pass. In addition, the mean value of the NDVI was significantly lower, with a bark stripping rate of 75% or higher, indicating that the NDVI of the canopy of A. homolepis can be used to estimate individuals with a high bark stripping rate at the root tips, that is, those with a high probability of mortality. Furthermore, by extrapolating the results of the tree-by-tree survey to the nontarget A. homolepis, we detected 46 (8%) A. homolepis with an average NDVI value of 0.8 or less (i.e., those with a bark stripping ratio of 75% or higher and a high probability of mortality). Therefore, the utilization of remote sensing technology via UAVs, as demonstrated in this study, proves to be a potent means for monitoring the incidence of bark stripping.

Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening

Most nature and health research use the normalized difference vegetation index (NDVI) for measuring greenness exposure. However, little is known about what NDVI measures in terms of vegetation types (e.g., canopy, grass coverage) within certain analysis zones (e.g., 500 m buffer). Additionally, exploration is needed to understand how to interpret changes in average NDVI (e.g., per 0.1 increments) exposure in relation to changes in vegetation amount and types. In this study, we aimed to explore what vegetation types and amounts best explain the average NDVI and how changes in average NDVI values indicate changes in different vegetation coverages. We used spatial modeling to sample mean NDVI and percentages of vegetation for sample locations within the Greater Manchester case study area. We fitted linear, nonlinear, and mixed multivariate and univariate generalized additive models (GAMs) for multiple spatial scales to identify the relationships between NDVI and vegetation amount and types. Our results showed that the relationships between NDVI and individual vegetation types mostly follow nonlinear trends. We found that canopy and shrubs coverage exhibited a greater influence on mean NDVI exposure values than grass coverage at 300 and 500 m indicating that NDVI values are sensitive to certain types and amounts of vegetation within various buffer zones. We also identified increment in mean NDVI exposure values at lower, mid, and high ranges might be associated with varying changes in total greenspace percentage and individual vegetation types. For instance, at 300 m buffer, an increment of mean NDVI in the lower range (e.g., from 0.2 to 0.3) is associated with an about 17% increase in greenspace percentage. Overall, interpreting changes in NDVI values for urban greening interventions would require careful evaluation of the relative changes in types and quantities of vegetation for different buffer zones.

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

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

Correlation Between Soil Nitrogen Content and NDVI Derived from Sentinel-2A Satellite Imagery

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) has recognized the Balinese agricultural irrigation system known as subak as part of the world's cultural heritage. Subak is the driver of Bali’s agricultural and tourism sectors and, therefore, must be preserved. Population growth triggers the conversions of land functions, from subak to built-up lands, such as those transpiring in Denpasar City. On the other hand, with the population continuously increasing, the demand for food becomes inevitably higher. This has caused farmers to intensify their agricultural practices through, for instance, applying chemical fertilizers excessively-potentially decreasing soil fertility. An example is urea fertilizer that contains a macronutrient, i.e., nitrogen (N). This study aimed to analyze the soil N content and its correlation with rice growth using the Normalized Difference Vegetation Index (NDVI). The Kjeldahl method was conducted to measure the N levels in the soil laboratory. NDVI was extracted from remote sensing data, namely Sentinel-2A imagery, on a cloud computing platform, Google Earth Engine (GEE), using Band 8 (NIR) with a wavelength of 0.842 m and Band 4 (Red) with 0.665 m. The results showed that the N levels varied from 0.09% to 0.31% and the average NDVI values ranged from 0.47 to 0.54. There is a strong correlation (r = 0.75 to 0.78) between the NDVI values derived from the Sentinel-2A Satellite Imagery and the soil nitrogen content. Spatially, based on the analysis results of the 2019‒2021 data, parts of existing subak systems, i.e., Subak Kerdung, Mergaya, Padanggalak, and Sembung, have high soil N contents and NDVI values.

Long-term exposure to residential surrounding greenness and incidence of diabetes: A prospective cohort study

Exposure to residential greenness might affect population health through increasing physical activity and social engagement, improving mental health, and reducing harmful environmental exposure. However, evidence on the association of greenness with risk of diabetes is still controversial. In this study, we recruited a total of 22,535 participants aged ≥18 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations between residential greenness and risk of diabetes incidence. Residential greenness was estimated using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Continuous Field (VCF). We also calculated cumulative average NDVI, EVI and VCF values, and changes in NDVI, EVI and VCF during the follow-up period. We used Cox proportional hazards models controlling for demographic characteristics, lifestyles, individual socioeconomic status, history of diseases and particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) to examine hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assessed physical activity, body mass index (BMI) or PM2.5 as potential mediators. During 84,992.64 person-years of follow-up, a total of 1,154 incident cases of diabetes occurred. In multivariable models, living in the highest quartile of cumulative average NDVI, EVI and VCF within 250-m buffer was associated with 57% (HR = 0.43, 95% CI: 0.36, 0.52), 62% (HR = 0.38, 95% CI: 0.32, 0.45), and 55% (HR = 0.45, 95% CI: 0.38, 0.54) reduction in diabetes risk compared with the lowest quartile, respectively. Results remained similar for NDVI, EVI, and VCF within 500-m and 1000-m buffers. Stratified analyses showed stronger association for residential greenness and diabetes among older people. The association between greenness and diabetes did not appear to be mediated by physical activity, PM2.5 or BMI. Our findings suggested that higher residential greenness was significantly associated with lower risk of diabetes.

Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.

A Remote Sensing-Based Analysis of the Impact of Syrian Crisis on Agricultural Land Abandonment in Yarmouk River Basin.

In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013–2021) were compared to the phenological profiles for the period before the crisis (1986–2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China.

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.

Tourism Infrastructure Development and Transformation of Vegetation Index in Dodola Island of Morotai Island Regency

The development of tourism infrastructure without control can cause changes in the environmental vegetation index and harm a tourism destination. This study aims to analyze changes in the vegetation index on Dodola Island from 2013-2021 to develop tourism infrastructure. The approach used is remote sensing, namely the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) using Landsat 8 Operational Land Imager (OLI). NDVI is an index of the greenery or photosynthetic activity of vegetation. Vegetation actively carrying out photosynthesis will absorb most of the red waves of sunlight and reflect higher near-infrared waves. In addition, SAVI is a soil vegetation index adjusted for soil pixels' effect using a vegetation density function. The results of this study indicate a perfect correlation between the NDVI average value and the SAVI average value of 0.996 so that it can be used as data to analyze changes in the vegetation index value on Dodola Island from 2013-2021. The average NDVI value of Dodola Island in 2017 showed a significant decrease to 0.16 from 0.35 in 2016. Likewise, the average SAVI value of Dodola Island in 2017 showed a reduction of 0.12 from 0 .27 in 2016. Although in 2021, the average NDVI value will increase to 0.32 and the average SAVI value to 0.27, stakeholders need to form institutions and establish policies as a preventive measure for the decline in the vegetation index worse than in 2017. Based on the Decree of the State Minister of the Environment No. 201 of 2004 concerning the characteristics of the damage to mangrove forests, the condition of the mangrove area of ​​Dodola Island in 2017 is categorized as damaged and sparse, so that mangrove rehabilitation is necessary. Based on the results of this study, a community-based mangrove ecotourism approach is recommended as an alternative method of sustainable tourism development on Dodola Island.

近60年来印江河流域极端气候演变及其对净初级生产力和归一化植被指数的影响

极端气候事件的发生改变了区域水热条件，并影响着生态环境变化。然而，目前长时间尺度上极端气候的演变规律及其对生态环境的影响尚不明晰。采用Mann-Kendall趋势及突变检验法、连续小波变换和Hurst指数法揭示了喀斯特槽谷印江河流域极端气候的变化趋势、突变时间、周期性特征和未来演变规律，并利用Lindeman-Merenda-Gold模型定量评估了极端气候溶变对生态环境变化的影响。结果表明：（1）印江河流域极端气温显著上升，降雨量增多，呈现湿热多雨的气候特征。未来极端气温事件持续等级将更高，持续强度也更强。（2）同类型极端气候具有潜在的关联性，但不同类型极端气候间的影响较小，且多呈负相关。（3）印江河流域平均净初级生产力（NPP）和归一化植被指数（NDVI）在2000-2015年间呈现相反的变化趋势，NPP平均值为598.53 g C m^-2 a^-1，平均减少速率为-3.32 g C m^-2 a^-1。NDVI平均值为0.59，平均增长速率为0.0013/a。（4）冷持续指数（CSDI）、平均温差（DTR）、系统阈值结冰日数（ID6.4）和生长期长度（GSL）对NPP的贡献较大，贡献率分别12.64%、11.50%、11.05%和7.4%。其中，CSDI、DTR、GSL对NPP变化表现为负贡献，而ID6.4则表现为正贡献。大部分极端气候指数对NPP变化的贡献都不超过5%。ID6.4对NDVI变化的影响最大，暖夜日数（TN90p）、热持续指数（WSDI）和暖昼日数（TX90p）相对次之，贡献率分别为13.67%、13.54%、12.95%和10.02%。总体上看，在2000-2015年间，印江河流域湿热多雨的气候对NDVI增长有促进作用，但是由于气温升高和降雨量增加的原因，对NPP累积产生了较大的负面影响。研究结果可为喀斯特槽谷流域气候预测、水资源管理及生态环境恢复提供参考依据。;The occurrence of extreme climate events has changed the regional hydrothermal conditions and affected the changes of ecological environment. However, at present, the evolution of extreme climate and its impact on ecological environment are unclear. Therefore, the methods of Mann-Kendall test, continuous wavelet transform, and Hurst exponent were applied to analyze the change trend, abrupt time, periodicity and future evolution of extreme climate events in Yinjiang River basin. Then, the Lindeman-Merenda-Gold model was used to quantitatively evaluate the impacts of extreme climate events to ecological environment change. The results were obtained:(1) The extreme temperature events in the Yinjiang River basin increased significantly, and the rainfall also increased, showing a hot and rainy climate. In the future, the level of extreme temperature events will be higher and the intensity will be stronger; (2) The same type of extreme climate had a potential correlation, but the influence of different types of extreme climate was small, and most of them showed negative correlation; (3) The average net primary production (NPP) and normalized difference vegetation index (NDVI) of the Yinjiang River basin showed an opposite trend from 2000 to 2015. The average NPP was 598.53 g C m^-2 a^-1 with the average reduction rate of -3.32 g C m^-2 a^-1 while the average NDVI value was 0.59 with an average growth rate of 0.0013/a; (4) The cold spell duration indicator(CSDI), diurnal temperature range (DTR), icing days defined by system threshold (ID6.4), and growing season length (GSL) contributed significantly to NPP, with contributions of 12.64%, 11.50%, 11.05%, and 7.4%, respectively. Among them, the CSDI, DTR, and GSL showed negative contribution to NPP changes, while ID6.4 showed positive contribution. Most extreme climate indices contributed less than 5% to the NPP changes. ID6.4 had the greatest impact on NDVI, followed by warm nights (TN90p), warm spell duration indicator (WSDI), and warm days (TX90p), with contribution rates of 13.67%, 13.54%, 12.95%, and 10.02%, respectively. On the whole, the study stressed that the hot and rainy climate in the Yinjiang River basin promoted the growth of NDVI during 2000-2015, but caused a significantly negative impact on the accumulation of NPP due to the increase of temperature and rainfall. The results can provide reference for climate prediction, water resources management and ecological environment restoration in a basin of karst trough valley.

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.