Vegetation dynamics and its relationship with climatic factors in the Changbai Mountain Natural Reserve

Hou, Guangjin; Zhang, Hongyan; Wang, Yeqiao

doi:10.1007/s11629-011-2206-4

Cited by 25 publications

(17 citation statements)

References 27 publications

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“…Precipitation and temperature are recognized as the two major climatic factors determines the vegetation biophysical processes [1]. To quantify how climatic factors affect vegetation growth at larger scale, the Normalized Difference Vegetation Index (NDVI), derived from infrared and near-infrared spectral band [2], has been widely applied as a proxy of vegetation growth [3]. The climate-vegetation-runoff correlation has been discussed based on the NDVI at the semi-arid watershed, Luanhe River Basin, China [4], where the vegetative activities are strongly affected by seasonal precipitation and temperature [5,6].…”

Section: Introductionmentioning

confidence: 99%

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Zhang

et al. 2020

Forests

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Hydrothermal and climatic conditions determine vegetation productivity and its dynamic changes. However, the legacy effect and the causal relationships between these climatic variables and vegetation growth are still unclear, especially in the dry regions. Based on multi-statistical methods, including bivariate correlation analysis and composite Granger causality tests, we investigated the correlation, causality, and lag length between temperature/precipitation and the vegetation growth (Normalized Difference Vegetation Index, NDVI) in three typical sub-watersheds in the Luanhe River Basin, China. The results show that: (1) Precipitation and temperature are the Granger causes of NDVI variation in the study catchment; (2) temperature and precipitation are not strictly positively correlated with NDVI during growing seasons along with the whole sequence, and excessive warmth and precipitation inhibits vegetative growth; (3) the lag length of vegetation growth in response to temperature/precipitation was shorter in agriculture areas (~2 months) than the forest-dominant area, which have indicated 3-4 months lag length; and (4) anthropogenic disturbance did not result in notable negative effects on vegetation growth at the Luanhe River Basin. Our study further suggests that use of these multi-statistical methods could be a valuable approach for comprehensively understanding the correlation between vegetation growth and climatic variations. We have also provided an avenue to bridge the gaps between stationary and non-stationary sequence, as well as to eliminate pseudo regression problems. These findings provide critical information for developing cost-efficient policies and land use management applications for forest conservation in arid and semi-arid area.

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Section: Introductionmentioning

confidence: 99%

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Zhang

et al. 2020

Forests

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“…However, detailed analysis of the monthly NDVI allows deeper understanding of interannual variation. Previous studies have also discussed vegetation dynamics at different altitudes [14,37,41] and in different ecosystems [35,37,41,42]. Different vegetative regions should also be taken into consideration as the effects of local climate may also cause differences in NDVI trends.…”

Section: Introductionmentioning

confidence: 99%

Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011

et al. 2014

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Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover) in China and their relations with climatic factors during 1982-2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data acquired from the National (1) annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2) of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35°N); (3) changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p < 0.001) at a region scale; (4) at the country scale, changes in NDVI was significantly and positively correlated with annual air temperature (r = 0.52, p < 0.01) and not associated with annual precipitation (p > 0.1); (5) of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative), and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative). The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6) monthly NDVI was significantly correlated with the preceding month's temperature and precipitation in western, central and northern China. The effects of a climate lag of more than two months in southern China may be caused mainly by the abundance of precipitation. These findings suggest that continuing efforts to monitor vegetation changes (in situ and satellite observations) over time and at broad scales are greatly needed, and are critical for the management of ecosystems and adapting to global climatic changes. It is likewise difficult to predict well future vegetation growth without linking these observations to mechanistic terrestrial ecosystem processes models that integrate all the satellite and in situ observations.

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“…Sarkar and Kafatos (2004) studied on the Indian subcontinent for several years to study the variability of vegetation and found the dominance of local climate anomaly in determining the vegetation. Several kinds of datasets, e.g., NOAA-AVHRR, SPOT-VGT, MODIS and GIMMS have been used (NOAA-AVHRR, SPOT-VGT, MODIS and GIMMS) to study the variability of vegetation at the local, regional and global scale (de Jong, de Bruin, de Wit, Schaepman, & Dent, 2011;Detsch, Otte, Appelhans, Hemp, & Nauss, 2016;Dubovyk, Landmann, Erasmus, Tewes, & Schellberg, 2015;Fensholt & Proud, 2012;Guo et al, 2015;Hou, Zhang, & Wang, 2011;Jeong, HO, GIM, & Brown, 2011;Lanorte, Lasaponara, Lovallo, & Telesca, 2014;Lu, Kuenzer, Wang, Guo, & Li, 2015;Martínez & Gilabert, 2009;Schucknecht, Erasmi, Niemeyer, & Matschullat, 2013;Sobrino & Julien, 2011;Teferi, Uhlenbrook, & Bewket, 2015;Zhao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India

Malik

Pal

Das

et al. 2019

Geology, Ecology, and Landscapes

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Intra-annual dynamics of vegetation status is very important to understand the spatial ecological environment. In this study, pixel wise temporal variation of vegetation status has been assessed using normalized differential vegetation index (NDVI) from Landsat 8 OLI data of 2014. Month-wise vegetation statuses were employed to understand the status of vegetation dynamics for the Sali River watershed. We have found that vegetation status (VS) of this study area varied with respect to space and time. We have identified two dominant natures of NDVI fluctuation, one with a single peak coming out of Sal forest and another with multiple peaks which is from paddy fields. NDVI curve of natural vegetation follows a rainfall pattern and depicts a single peak during the rainy season with the moderate standard deviation (S.D.) and coefficient of variation (C.V.). On the other hand, the area associated with population pressure and agricultural fields shows multiple peaks as well as a high degree of S.D. and C. V. of NDVI during the months of April, August and December.

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Vegetation dynamics and its relationship with climatic factors in the Changbai Mountain Natural Reserve

Cited by 25 publications

References 27 publications

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Response of Vegetation to Changes in Temperature and Precipitation at a Semi-Arid Area of Northern China Based on Multi-Statistical Methods

Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011

Intra-annual variations of vegetation status in a sub-tropical deciduous forest-dominated area using geospatial approach: a case study of Sali watershed, Bankura, West Bengal, India

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