Spatio-temporal Changes Of Vegetation Research Articles

Spatiotemporal Variations of Vegetation and Its Response to Climate Change and Human Activities in Arid Areas—A Case Study of the Shule River Basin, Northwestern China

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 a–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 a–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 a–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB.

Vegetation cover change in Ugam Chatkal National Park, Uzbekistan, in relation to climate variables during the post-Soviet period (1991-2022)

This paper presents a comprehensive study relating to the vegetation cover change in Ugam Chatkal National Park (Uzbekistan) and its relation to climate change during the post-Soviet period (1991-2022). The study utilises remote sensing technology, specifically the Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), to monitor spatio-temporal changes in vegetation. Landsat satellite imagery and meteorological data, including temperature and precipitation records form the basis of the analysis. The research aims to understand the impact of climatic factors, such as air temperature, soil temperature and precipitation on vegetation cover. Statistical methods, for example Pearson’s correlation analysis are employed to determine the strength and direction of relationships between these variables. The study reveals that both NDVI and SAVI are strongly correlated with air and soil temperatures, indicating the significant influence of these climatic factors on vegetation health and growth. The findings suggest that changes in vegetation cover in the Ugam Chatkal National Park are closely tied to climate change, with air temperature revealing a substantial correlation with time, indicating a trend towards increasing temperatures. The study also forecasts future climatic and vegetation trends, predicting an increase in air temperature, precipitation and vegetation cover over the next four decades. In particular, the research highlights the magnitude of monitoring and understanding the complex interactions between climate change and vegetation dynamics, which are crucial for environmental management and policy-making in the region.

中亚地区2001–2020年250m及2020年30m分辨率植被生长季NDVI数据集

Central Asia is the largest arid and semi-arid region in the Northern Hemisphere, and its ecological environment is extremely fragile and susceptible to the effects of global climate change. Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its unique geographic location. Vegetation serves as a significant indicator of the ecological environment, and the spatial and temporal distribution pattern, as well as the change trend of vegetation, are important indicators in evaluating the ecological status of the region. The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to study vegetation, which characterizes the spatio-temporal changes of vegetation. This dataset utilized MODIS13Q1 to generate long-term time series growing season mean NDVI data with a spatial resolution of 250m in Central Asia from 2001 to 2020. To obtain the 2020 growing season mean NDVI data with a higher spatial resolution of 30m, the Cubist algorithm based on rule segmentation regression was utilized to fuse the Landsat data and MODIS data. Meanwhile, this dataset carried out quality control of data products from three aspects: data source quality control, model training optimization, and model independent verification to ensure the accuracy and reliability of data. The generation of this dataset provides powerful data support for the analysis of vegetation dynamic change and spatial pattern in Central Asia.

Research on the Spatio-Temporal Changes of Vegetation and Its Driving Forces in Shaanxi Province in the Past 20 Years

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km2, accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km2, accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

Impacts of altitudinal ecohydrological dynamic changes on water balance under warming climate in a watershed of the Qilian Mountains, China

In alpine areas of northwest China, one of the major concerns is the rapid warming and stimulated vegetation growth consume more water and reduce available water for downstream oasis development. Investigating the response of these ecohydrological dynamics to climate change is thus crucial, but is also challenging because of tremendous variability of vegetation, hydrology, and climate in elevation and complex interactions between them. Here, we performed numerical simulations in a mountainous watershed covering a range of contrasting climatic conditions and vegetation characteristics representative of the Qilian Mountains, China. The simulations were run with a dynamic global vegetation model LPJ-WHyMe to quantify spatiotemporal changes of vegetation (e.g., species and net primary production (NPP)) and hydrological components (e.g., runoff and evapotranspiration (ET)) in recent decades (1982–2018). The simulated results were compared with those derived from MODIS and observations. Results show that the favorable climate condition for vegetation growth appears around the freezing altitude (3000–3250 m asl) where the NPP, ET, and water use efficiency (WUE = NPP/ET) exhibit a ‘humped’ peak value while runoff increases with precipitation towards higher altitudes. The warming and moistening climate and elevated CO2 since 1982 have favored vegetation growth, leading to uphill migration of the treeline and the ‘humped’ peak to higher elevation (3250–4000 m asl). The climate warming and stimulated vegetation growth consumed 83 % of the increased precipitation (34.6 mm) in the whole catchment. The greatly increased ET and thus decreased runoff were found at high elevation (above 4000 m asl) due to the reduced freezing days (up to 43 days) in the warming climate. Meanwhile, the substantially elevated WUE and moistening climate can balance the increased ET in the vegetation occupation areas below 4000 m asl, leading to increased runoff. The results indicate that despite a little increase of runoff in the recent decades, stimulated vegetation growth and climate warming could reduce water resources availability in the mountains if climate warming continues and/or climate wetting ceases in future.

Rapid Vegetation Growth due to Shifts in Climate from Slow to Sustained Warming over Terrestrial Ecosystems in China from 1980 to 2018

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysiological parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (FPARANN) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. FPARANN provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (R2): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (FPARMCD15A2) with a greater R2 (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with FPARANN (1980–2003) and FPARMCD15A2 (2004–2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential.

Quantifying Dieback in a Vulnerable Population of Eucalyptus macrorhyncha Using Remote Sensing

A disjunct population of red stringybark (Eucalyptus macrorhyncha) trees in South Australia is experiencing increasing amounts of dieback. Because the population is considered vulnerable to extinction, we investigated spatiotemporal vegetation changes, quantified the extent of dieback, and determined how topography influences dieback using aerial and satellite imagery. Classification of vegetation health status using hyperspectral aerial imagery indicated that 37% (accuracy = 0.87 Kappa) of the population was unhealthy and potentially experiencing dieback. When correlating this classification with a digital terrain model (DTM), the aspect and amount of solar radiation had the strongest relationship with the presence of unhealthy vegetation. PlanetScope satellite-derived, and spectral index-based analysis indicated that 7% of the red stringybark population experienced negative vegetation health changes during a five-year period (2017–2022), with positive vegetation health changes (9.5%) noted on pole-facing slopes. Therefore, our integrated remote sensing approach documented the extent and spatiotemporal dynamics of dieback, suggesting it could be applied in other studies. Topographical aspects exposed to high-solar radiation were particularly vulnerable to dieback, and pole-facing aspects demonstrated some recovery between droughts. The influence of topography and maps of vegetation health can be used to guide future management and restoration of the population.

Spatio-temporal Analysis of Changes in Land Use-Land Cover for Conservation Efficacy of Urban Forest Reserve: The Case of Rau Forest Reserve, Tanzania

Changes in land use and land cover in different places around the globe have a significant impact on ecological structure and functions as well. Analysis of the changes in land use and land cover is useful in management activities, especially forest reserves as it reflects the interaction between adjacent communities and natural resources. Due to the rapid sprawl in urbanisation, there has been a rapid change in the human-land relationship. Rau Forest is situated in Moshi Municipality of Kilimanjaro Region in Tanzania. There have been several claims of a decrease in vegetation cover in this important forest reserve, although there is no documentation on such claims. It is on the basis of this background this study was conducted to explore the changes in land use and land cover in the Rau Forest reserve to show the management status of this ecosystem which is also located in an urban area where management activities have been a challenge. The land use and cover (LULC) Spatio-temporal changes in the Rau Forest were classified as Densely Vegetation, Slightly Vegetation, Grassland, Bare land, Built-up, and Waterbodies to evaluate qualitative and quantitative changes in this reserve through the use of GIS techniques and tools over time (1990, 2000 and 2019). Forest encroachment has driven changes in land use and land cover and transformed parts of the Rau Forest reserve into the urban settlement, and grazing lands which has ultimately led to gradual forest degradation. Landsat imageries for the year were used to identify the Spatio-temporal changes in vegetation in the area. This study shows the downward trend for the densely vegetated part of the reserve at the expense of expansion in the built-up area. The results demonstrate the urgent need for strong and more severe regulations concerning the protection of Rau Forest and other urban situated forest reserves against land use and land cover changes for the benefit of the present and future generations.

Spatio-Temporal Changes in Vegetation in the Last Two Decades (2001–2020) in the Beijing–Tianjin–Hebei Region

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers – spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth – for Kazakhstan during 2000–2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Assessment of the responses of spatiotemporal vegetation changes to climatic variability in Bangladesh

Assessment of the responses of spatiotemporal vegetation changes to climatic variability in Bangladesh

Automatic detection of vegetation cover changes in urban-rural interface areas

The present work started from the need to streamline the process of monitoring changes in vegetation in the in urban-rural interface fuel management bands, defined by Portuguese legislation as areas where the existing biomass must be totally or partially removed. The model developed uses a time series of Sentinel 2 satellite images to search for changes in the vegetation cover in a 100 m buffer around built-up areas. The use of satellite data allows analysing large areas and speeds up the task of identifying the places where fuel management took place and the places where there is a need to carry out such management. The objective of the proposed method is to give a script in Python language that can verify the cleanliness of vegetation in the fuel management ranges through multi-temporal analysis of satellite images.• The paper presents a step-by-step procedure for a Sentinel 2 time series vegetation index analysis.• Automated routine to detection of spatiotemporal vegetation changes based on statistical parameters.• Used Python language to do geoprocessing analysis.

Relationship between extreme climate indices and spatiotemporal changes of vegetation on Yunnan Plateau from 1982 to 2019

Extreme weather events have led to significant changes in vegetation over the past few decades. Extreme climate indices and net primary productivity (NPP) of vegetation on Yunnan from 1982 to 2019 were calculated based on Rclimdex 1.0 software and the Carnegie-Ames-Stanford approach (CASA) model, respectively. The relationship between them was studied on annual and monthly scales. The interaction detector was used to calculate the contribution rate of extreme climate factors and synergistic effect on NPP. The results showed that：(1) Annual NPP showed a significant increasing trend in southeast Yunnan, while it showed a decreasing trend in central and northwest Yunnan. (2) Extreme temperature indices (TXx, TXn, TNx, TNn) showed significant increasing trends (P < 0.01), while cold day index (Tx10p) and cold night index (Tn10p) showed significant decreasing trends (P < 0.01), and the warming trend is more obvious. Individual precipitation intensity increases while the continuous precipitation decreases. (3) The correlation between extreme temperature indices and NPP was lower than that between extreme precipitation factors. Extreme precipitation indices had a significant impact on vegetation NPP and dominated the pattern of spatial distribution. Extreme precipitation indices had the excellent explanatory power for the NPP of shrub, grassland, and agricultural vegetation. NPP of Alpine vegetation was remarkably affected by the maximum Tmin (TNx) (q = 0.868). (4) PD values of interactions between extreme climate factors are higher than that of an individual factor. The synergistic effect of extreme climatic factors on NPP in northeast Yunnan is more significant. The interaction between the two extreme climate factors significantly impacts the NPP of agricultural and alpine vegetation. The results may help deeply understand and predict the impact of extreme climate factors on the different sub-regional ecosystems on Yunnan.

Dynamics of NDVI and its influencing factors in the Chinese Loess Plateau during 2002–2018

Understanding the spatio-temporal changes of vegetation and its climatic control factors can provide an important theoretical basis for the protection and restoration of eco-environments. In this study, we analyzed the normalized difference vegetation index (NDVI) in the Chinese Loess Plateau (CLP) from 2002 to 2018 via trend analysis, stability analysis, and Mann-Kendall mutation test to investigate the change of vegetation. In addition, we also used the skewness analysis and correlation analysis to explore the contribution of climate change and human activities on regional vegetation changes. The results indicated that the overall increasing trend of NDVI from 2002 to 2018 was significant. The areas showing increased NDVI were mainly distributed in the southeastern CLP and the irrigation districts of the Yellow River to the north and west of the CLP, while the areas showing decreased NDVI were concentrated in the desert of the western Ordos Plateau, Longzhong Loess Plateau, and the built-up and adjacent areas. Precipitation was the dominant factor contributing to vegetation growth in the CLP, while vegetation was less dependent on precipitation in the irrigation districts. The increasement of NDVI has led to a prolonged response time of vegetation to water stress and a lag effect of less than two months in the CLP. The effect of temperature on NDVI was not significant; significant negative correlations between NDVI and temperature were found only in the desert, the Guanzhong Plain, the southern Liupan Mountains, and the southeastern Taihang Mountains, owing to high temperatures, urban heat islands, and large cloud cover in mountainous areas. Affected by the “Grain for Green Program” (GGP), NDVI in the CLP increased from 2002 to 2018; however, the increasing trends of NDVI for different vegetation cover types were significantly different owing to the difference in background status. The increasing contribution rate of NDVI in the CLP mainly came from crops and steppes. Urban not only led to the destruction of vegetation but also had radiation effect causing negative impact of NDVI around the cities. This resulted in the aggravation of the negative bias of NDVI with time in the CLP. The results provide a long-term perspective for regional vegetation protection and utilization in the CLP.

Detection of spatio-temporal changes of vegetation in coastal areas subjected to soil erosion issue

Coastal soil erosion can be recognized as the most alarming environmental issue since, causing shoreline retreat, reduces the area available for plant habitat survival, highly influencing their health status, and, consequently, limiting their ability in beach front properties protection. A deep knowledge of vegetation changes is required to identify the proper strategy to be adopted to face soil erosion problems in coastal areas. Therefore, the current paper is aimed to quantitatively examine the spatio-temporal changes suffered by the vegetation in the coastline of Siponto in Apulia Region (Southern Italy) covering a time period of about forty years. LANDSAT images from 1975, 2006, 2011 and 2018 were collected, atmospherically corrected and, finally, processed to generate binary classification maps of vegetation by applying the Composite Vegetation Index, a novel index based on the interpolation of Red, Green and Near-Infrared bands, suitable for catching both cellular and metabolic features of vegetation. Then, the generated binary classification maps were compared using the Vegetation Index Differencing technique, a post-classification change detection technique. The results showed an increase in vegetation extension cover and density overall the entire examined period. That phenomenon appeared more and more prominent between 1975 and 2006, where an increment of vegetated areas extension of about 88% were registered. Combining of the novel vegetation index, developed ad-hoc in the current research, and Vegetation Index Differencing approach shows promising results in vegetation classification and comparison over the time. Indeed, the method allows the fast vegetation extraction, great processing time saver. Nevertheless, spatial resolution of Landsat Images limits the classification of small and low-density vegetated areas. Therefore, future work should plan to test the proposed approach at a more detailed scale.

Vegetation- and Environmental Changes on Non-Reclaimed Spoil Heaps in Southern Poland.

The study focused on the changes in vegetation and soils on an undeveloped area of coal mine spoil heaps. The process of vegetation changes was evaluated on the basis of historical cartographic materials and fieldwork. Changes of vegetation in nearly 200 years are presented herein. The main purpose of this study is to present an analysis of spatio-temporal changes in vegetation and their influence on soil features. The diversity of ecological species in terms of habitat requirements, tendency of hornbeam communities formation, and the relationship between forest communities and soil features was found. The basic soil properties were examined under selected plant communities (pH, Corg, Nt), available forms of elements (P, K, Mg), and as plant nutrients and heavy metal occurrence (Fe, Zn, Mn, Co, Cd, Pb, Sr, Cr, Cu). The soil organic carbon (Corg) content varied from 3.17 ± 0.007% to 17.7 ± 0.21% and significant differences were observed between sites. The highest total nitrogen (Nt) content was recorded in the soils of the sites that were represented by Populus-Betula stands (0.60 ± 0.01%). Soil acidity (pH) varied greatly, ranging from acidic (pH = 4.1) to weakly acidic (pH = 5.9). The highest value for Mgavail (205.43 ± 0.5 mg·kg−1) was noted in the soils under Calamagrostis epegijos (L.) Roth community and for Pavail (184.07 ± 3.77) and Kavail (346.19 ± 2.92 mg·kg−1) under the Quercus-Pinus stand. On all sites, Zn was a dominant element and its concentration ranged from 526.1 to 1060.4 mg·kg−1. Obtained results show how important it is to study the issue of vegetation changes and the formation of the landscape within an industrial city. The described results are important for the management of urban greening issues. Human influence on the disintegration and development of the natural environment is clearly visible. Due to the diversity of former mining areas and their time of creation, the studied area is one of the most important experimental areas for the determination link between vegetation and soil.

Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar

In August 24, 2017, a massive outbreak took place in the Rakhine state of Myanmar which triggered a huge refugee influx to the Teknaf Peninsula, Bangladesh. To settle the refugees, makeshift camps were built in large numbers destroying huge amount of forest areas near the existing Kutupalong and Nayapara camps. Refugees have been encroaching the nearby forest covers to collect fuelwood and other purposes. These forest destructions have put the wildlife and biodiversity of the system in a substantial pressure as well as altering the land surface temperature (LST). This paper has examined the extent of vegetation change and the changes of LST from 2017 to 2019 throughout Kutupalong and Balukhali camp and adjacent areas using Landsat 8 images. Random forest algorithm and Plank equation were applied on images to identify vegetation change and LST, respectively. The overall and kappa accuracies for the maps of 2017 are 96% and 92%, respectively, while it stands at 94% and 88% for the 2019 image. Results derived from the analysis suggest that an estimated 1876 hectares of forested lands have been decreased in the study area. LST of the study area increased spatially throughout the whole region with a maximum value of 34 °C which is significantly higher than the pre-influx period. If this trend of forest-clearing activities continues, the place will become barren land soon and the LST will also increase. All these factors will ultimately trigger the climate change impacts and biodiversity loss of the area.

Effect of hydrological variation on vegetation dynamics for wintering waterfowl in China’s Poyang Lake Wetland

Poyang Lake wetland of China, with designated national nature reserves, is one of the most important habitats for waterfowl in the world. During the wintering season, it is home to various rare and endangered species with abundant plant resources. However, extensive disturbances from both global climate change and human activities, such as the construction of hydroelectric dams, have exerted severe impacts on the hydrological regime and the vegetation dynamics in this region. To evaluate the suitability of the habitat for wintering waterfowl, it is necessary to monitor the spatio-temporal changes of vegetation over the protected wetland, and explore the potential environmental driving factors of the changes during the past decades. In this study, the time-series MODIS Enhanced Vegetation Index (EVI) data from 2000 to 2018, were used to analyze the seasonal dynamics and interannual tendencies of vegetation coverage over the wintering period from October to January. The results showed that the largest monthly mean EVI generally occurred in November, and was related to the quantity of wintering waterfowl in Poyang Lake wetland. The mean EVI in November had a lagged response to both temperature and accumulated precipitation. Long-term trend analysis revealed that the average EVI of the reserve increased significantly in the winters since the new millennium. Over the past 18 years, the hydrological regime, particularly the water area, is recognized as the dominant environmental factor determining the interannual variation of EVI. From 2010 onwards there was also an increasing influence of temperature. These analyses would help in the creation of appropriate management strategies to protect the habitat of wintering waterfowl in Poyang Lake wetland.

Analyzing of the spatio-temporal changes of vegetation and its response to environmental factors in north of Fars province, Iran

پوشش گیاهی، یکی از عوامل ضروری در ساختار و عملکرد اکوسیستم‌های خشکی و یکی از حلقه‌های اساسی زنجیر حیاتی آب-خاک-گیاه و اتمسفر[1] است. مطالعات متعدد ثابت کرده است پوشش گیاهی به تغییرات عوامل اقلیمی و ادافیکی حساس است. بر این اساس، تغییر در پوشش گیاهی و رابطه‌اش با عوامل مذکور، از اهمیت بسیاری برخوردار است. تحقیق حاضر به منظور بررسی تغییرات پوشش گیاهی و عوامل موثر بر آن، در حوضه خارستان استان فارس انجام شد. در این رابطه، تصاویر برگرفته از سنجنده ETM لندست 7، طی دوره 2000-2017 و داده‌های اقلیمی‌حاصل از 17 ایستگاه‌ هواشناسی استان استفاده شد. با استفاده از این تصاویر، تغیرات زمانی و مکانی NDVI و آنومالی آن استخراج شد. به منظور استخراج نقشه کاربری اراضی از روش طبقه بندی نظارت شده استفاده شد. در نهایت رابطه NDVI با عوامل اقلیمی، توپوگرافی و انسانی (کاربری اراضی) بررسی شد. رابطه بین این شاخص با عوامل اقلیمی‌و توپوگرافی بر مبنای روش‌های رگرسیون وزن‌دار فضایی[1] و حداقل مربعات معمولی[1] به‌دست آمد. در مجموع، تغییرات زمانی مبین روند افزایش آهسته NDVI بود. آنومالیNDVI در سال‌های قبل از 2008 روند مثبت و برای سال‌های بعد روند منفی را نشان داد. توزیع مکانی NDVI مبین یک روند افزایشی از شمال به سمت مرکز و جنوب غرب منطقه مورد مطالعه بود. نتایج حاکی از تاثیر دو دسته عوامل طبیعی و انسانی بر تغییرات پوشش گیاهی بود. NDVI در اراضی کشاورزی و مرتعی افزایش، همچنین پوشش گیاهی طبیعی بیشتر از پوشش‌های دست کاشت (اراضی کشاورزی و باغی) تحت تاثیر عوامل اقلیمی‌قرار گرفته است. علاوه بر این تغییرات پوشش گیاهی بر حسب ارتفاع، جهت و شیب هم تفاوت داشت. به طوری که از ارتفاع بیشتر از 2500 متر مقدار NDVI کاهش، در شیب‌های کمتر از ˚5، مقدار این شاخص افزایش و در جهت‌های شمال و شرق بیشتر از دامنه‌های جنوبی بود. روند کلی، نشان‌دهنده افزایش دما و کاهش بارندگی در طول دوره مورد مطالعه بود. با توجه به نقش تعیین کننده بارش در مناطق خشک و نیمه‌خشک می‌توان گفت نقش کنترلی بارش بر NDVI بیشتر از دما است. حداکثر همبستگی مثبت و منفی، بین متوسط بارش سالیانه و NDVI با استفاده از روش حداقل مربعات معمولی به ترتیب 93/0 و 83/0 مشاهده شد. همچنین حداکثر همبستگی منفی و مثبت بین NDVI و دما به ترتیب 65/0 و 5/0 بود. بیشترین مقدار ضریب تبیین مکانی (Local R2) بین NDVI با بارندگی و دما به ترتیب 45/0 و 44/0 بود که در بخش‌های مرکزی منطقه مشاهده شد. با توجه به نتایج، می‌توان اظهار داشت که عوامل محیطی نظیر ارتفاع، جهت، شیب، دما و بارش از فاکتورهای تاثیرگذار بر پوشش گیاهی در منطقه خارستان هستند.

Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000–2017

The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area.