Water Shortage Drives Interactions Between Cushion and Beneficiary Species Along Elevation Gradients in Dry Himalayas
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Abstract It is challenging to understand the drivers of plant‐plant interaction patterns in dry mountains. However, such knowledge is important to assess alpine ecosystem resilience to climate change. In water‐limited ecosystems, leaf δ 13 C and satellite‐derived vegetation index (NDVI) may serve as reliable indicators of environmental severity to address plant responses to water availability. We hypothesized that in dry mountains, interaction intensity between cushion and beneficiary species increases with increasing δ 13 C and decreasing NDVI regardless of elevation, indicating the importance of water availability in driving plant interactions. We used relative interaction indices (RII) of species and individual numbers within and outside the canopy of three cushion plant species along three elevational transects in dry Himalayas, Nepal. Site‐specific NDVI was calculated from 30 m Landsat images. Thornthwaite moisture index was calculated for each elevation site. We observed nonlinear patterns in RII, δ 13 C, and NDVI with elevation. Intraspecific variation of δ 13 C was negatively correlated with moisture index and NDVI, while NDVI across sites was positively correlated with precipitation but not with temperature. RII within a cushion species was positively correlated with δ 13 C and negatively with NDVI when the effect of elevation was removed. In pooled data across cushion species and sites, RII was negatively correlated with precipitation and NDVI when the effect of temperature was removed. RII was uncorrelated with cushion size under the same environment. Leaf nitrogen showed no correlation with RII or δ 13 C. Our data show that water shortage is the main driver of plant interactions in the alpine belt of dry Himalayas.Keywords:
Elevation (ballistics)
This paper aimed to investigate the influence of climatic and topographic factors on the distribution of vegetation in the Virunga Volcanoes Massif using GIS and remote sensing techniques. The climatic variables considered were precipitation, Land Surface Temperature (LST), and evapotranspiration (ET), whereas the topographic factors considered were elevation and aspect. The dataset consisted of MODIS NDVI data, satellite-delivered precipitation, ET, and the LST. A 2014 Landsat 8 OLI image was used to produce a vegetation map of the study area, while DEM was used to derive the elevation attributes and to calculate the aspect angles. Moran’s I and Geographically Weighted Regression (GWR) Model was used to analyze the relationships between the climatic factors and NDVI changes over elevation and aspect. The results indicated that among the nine vegetation types inventoried in the area, the Mean NDVI varied from 0.33 to 0.59 and the optimal vegetation growth was found at an elevation between 2000 and 3900 m, with mean NDVI values larger than 0.50. The peak mean NDVI value of 0.59 was found at the elevation from 2100 to 2800 m. Vegetation growth was found to be more sensitive to elevation, as NDVI values were more varied at a lower elevation (<4000 m) than at a higher elevation (>4000 m). Considering the aspect, the greater vegetation growth was found in SE (132°, 148°), SW (182°, 186°), and NW (309.5°–337.5°), with mean NDVI values larger than 0.56. This indicated that vegetation was susceptible to better growth conditions in the lower elevation ranges and in shady areas. The vegetation NDVI in this study area was mostly uncorrelated with precipitation (R2 = 0.34), but was strongly correlated with LST (R2 = 0.99) and ET (R2 = 98). LST (≥18 °C) and ET (1286 mm/year−1) were found to provide optimal conditions for vegetation growth in the Virunga Volcanoes Massif. Empirically, the results concluded that elevation, aspect, LST, and ET are the main factors controlling the spatial distribution and vegetation growth in this area. This information is significantly helpful for biodiversity conservation and constitutes a valuable input to environmental and ecological research.
Elevation (ballistics)
Massif
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Цель исследования – изучить пространственное распределение значений вегетационного индекса Normalized Difference Vegetation Index (NDVI) и выявить слабоиспользуемые земли в сельском хозяйстве на территории города федерального значения Севастополь в 2017 году. Для расчетов вегетационного индекса Normalized Difference Vegetation Index (NDVI) были использованы космические снимки Sentinel-2 с минимальными показателями облачности за период с 16 февраля 2017 года по 29 октября 2017 года. Космические снимки были предварительно обработаны и прошли атмосферную коррекцию. Результаты исследования показывают, что на территории города федерального значения Севастополь в 2017 году средние значения вегетационного индекса Normalized Difference Vegetation Index (NDVI) колеблются достигают 0,61. Выявлены семь участков сельскохозяйственных земель со средними значениями вегетационного индекса Normalized Difference Vegetation Index (NDVI) менее 0,2, что свидетельствует о слабом развитии растительности и вовлечении в сельскохозяйственную деятельность.
Enhanced vegetation index
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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.
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Qinling Mountains is the north–south boundary of China’s geography; the vegetation changes are of great significance to the survival of wildlife and the protection of species habitats. Based on Landsat products in the Google Earth Engine (GEE) platform, Pearson’s correlation coefficient method, and classification and regression models, this study analyzed the changes in NDVI (Normalized Difference Vegetation Index) in the Qinling Mountains in the past 38 years and the sensitivity of its driving factors. Finally, residual analysis method and accumulate slope change rate are used to identify the impact of human activities and climate change on NDVI. The research results show the following: (1) The NDVI value in most areas of Qinling Mountains is at a medium-to-high level, and 99.76% of the areas correspond to an increasing trend of NDVI, and the significantly increased area accounts for more than 20%. (2) From 1981 to 2019, the NDVI of the Qinling Mountains increased from 0.63 to 0.78, showing an overall upward trend, and it increased significantly after 2006. (3) Sensitivity analysis results show that the western high-altitude area of Qinling Mountain area dominated by grassland is mainly affected by precipitation. The central and southeastern parts of the Qinling Mountains are significantly affected by temperature, and they are mainly distributed in areas dominated by forest. (4) The contribution rates of climate change and human activities to NDVI are 36.04% and 63.96%, respectively. Among them, the positive impact of human activities on the NDVI of the Qinling Mountains accounted for 99.85% of the area. The area with significant positive effect accounted for 36.49%. The significant negative effect area accounts for only 0.006%, mainly distributed in urban areas and coal mining areas.
Driving factors
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植被指数是反映地表植被覆盖状况的重要参数,分析气候因子与植被指数间的相互关系有助于揭示气候变化对植被的影响,然而当前研究有两种分析植被指数与气候因子关系的方法,分别为分析植被指数与生长季内和生长季间气候因子的关系,然而这两种法差异如何,何种方法更为合适需要进一步分析。利用2000年—2009年生长季的MODIS的归一化植被指数NDVI(Normalized Difference Vegetation Index)数据集和藏北那曲地区3个气象站逐月气象资料(月平均气温、≥0℃活动积温和月降水量),分析比较了生长季内和生长季间气候因子对植被生长影响的差异,并分析了两种方法的优劣。结果表明:(1)生长季内植被NDVI与同期气温和降水量均呈高度正相关,生长季内时滞时间尺度为1个月时,植被NDVI对月平均气温及降水响应均最为强烈。(2)生长季间NDVI与同期降水量相关性并不明显,气候因子的滞后效应在生长季间也较弱。(3)生长季内和生长季间植被NDVI与气候因子的关系所得出的结论有一定差异性,可能是因为两方面的原因:生长季内植被NDVI与水热因子的高相关性与中国季风季候造成的高温多雨出现在夏季有关,而生长季内高水热条件与高植被指数对应的多年重复必然造伪的高相关系数,但这种相关性不一定能真实反映植被与水热条件的关系,而生长季间水热等气候因子与植被指数年际变化相关性分析不存在水热与高植被指数同期问题,更能真实反映气候因子年际变化对植被的影响。
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A precise, simple, and rapid growth diagnosis method using normalized difference vegetation index (NDVI) obtained by unmanned aerial vehicle (UAV), which will help determine nitrogen (N) application rate to increase grain yield in numerous farmers' fields, is necessary for the development of a robust production system for rice (Oryza sativa L.). In the present study, we examined the relationship between UAV-NDVI and NDVI measured with the GreenSeeker handheld crop sensor (GS-NDVI), and between grain yield and UAV-NDVI or GS-NDVI at the reproductive stage in the plant communities at 4–1 week (wk) before heading in 2018 and 2019 and in 2020 and 2021, respectively. In the data of each measurement day in 2018 and 2019, the relationship between UAV-NDVI and GS-NDVI was strongly positive. However, in the pooled data of different measurement days, the relationship between UAV-NDVI and GS-NDVI was weakly positive. This was because GS-NDVI was more constant under various climatic conditions and across various time of day than UAV-NDVI at the reproductive stage. Furthermore, in the pooled data of different years in 2020 and 2021, GS-NDVI correlated more strongly with grain yield than UAV-NDVI between 3 and 1 wk before heading. To increase the efficiency of growth diagnosis and yield prediction in the numerous farmers' fields, UAV-NDVI could be used with correction by a few measurements of GS-NDVI determined on the same day.
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Elevation (ballistics)
Massif
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Using wavelet coherency analysis,this paper studies the relationships between normalized difference vegetation index( NDVI) and environmental factors at world heritage of Wuyi Mountain.This factors are elevation,slope,aspect,distance to the nearest resident,distance to the nearest road and distance to the nearest river in two transects based on data of TM remote sensing image,DEM,settlements,roads and rivers in 2009. The results show that the relationships between NDVI and environmental factors change as scale changes. At medium and large scale,NDVI is significantly correlated with elevation,aspect,slope. Thus elevation is the dominant controlling factors on the vegetation cover. There is positive correlation between NDVI and elevation below the altitude of 600 m,and above 600 m,the relationships between NDVI and elevation are positive in the windward side of the southeast monsoon and negative in the leeward side,but inversely above 1 200 m.
Elevation (ballistics)
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Elevation (ballistics)
Basal area
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The Hanjiang River Basin (HJRB) is an important source area for drinking water in Hubei Province, China, and the vegetation coverage there is important to the ecological system. Due to the spatial heterogeneity and synergistic effect of various factors, it is very difficult to identify the main factors affecting vegetation growth in the HJRB. With the normalized difference vegetation index (NDVI) data from 2001 to 2018 in the HJRB, the spatiotemporal patterns of NDVI and the influences of natural factors and human activities on NDVI were investigated and quantified based on the Mann-Kendall (M-K) test, partial correlation analysis, and Geographical Detector. The individual factors and their interactions and the range/type of factor attributes suitable for vegetation growth were also examined. NDVI in the HJRB increased from 2001 to 2018, and the variation rate was 0.0046 year−1. NDVI was increasing in 81.17% of the area (p < 0.05). Elevation and slope can effectively explain the vegetation distribution. The interactions of factors on NDVI were significant, and the interactions of the elevation and precipitation can maximize the impact among all factors. The range of available landforms is thought to be highly conducive to vegetation growth. The rates of the annual precipitation and annual mean temperature changed from 2001 to 2018, which were 3.665 mm/year and 0.017 °C/year, and the regions where NDVI positively correlated with them were over 85%. Contrary to the general trend, NDVI has obviously decreased in urban areas since 2010. The quantitative findings of this study can help us better understand the effects of various factors on vegetation growth and provide appropriate suggestions for vegetation protection and restoration in the HJRB.
Elevation (ballistics)
Landform
Driving factors
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