Assessing the response of vegetation change to drought during 2009–2018 in Yunnan Province, China

Author(s):  
Yuanhe Yu ◽  
Yuzhen Shen ◽  
Jinliang Wang ◽  
Yuchun Wei ◽  
Lanping Nong ◽  
...  
Author(s):  
R. Sedricke Lapuz ◽  
Angelica Kristina Jaojoco ◽  
Sheryl Rose Cay Reyes ◽  
Jose Don Tungol De Alban ◽  
Kyle W. Tomlinson

Abstract Yunnan Province, southwest China, has a monsoonal climate suitable for a mix of fire-driven savannas and fire-averse forests as alternate stable states, and has vast areas with savanna physiognomy. Presently, savannas are only formally recognised in the dry valleys of the region, and a no-fire policy has been enforced nationwide since the 1980s. Misidentification of savannas as forests may have contributed to their low protection level and fire-suppression may be contributing to vegetation change towards forest states through woody encroachment. Here, we present an analysis of vegetation and land-use change in Yunnan for years 1986, 1996, 2006, and 2016 by classifying Landsat imagery using a hybrid of unsupervised and supervised classification. We assessed how much savanna area had changed over the three decades (area loss, fragmentation), and of this how much was due to direct human intervention versus vegetation transition. We also assessed how climate (mean annual temperature, aridity), landscape accessibility (slope, distance to roads), and fire had altered transition rates. Our classification yielded accuracy values of 77.89%, 82.16%, 94.93%, and 86.84% for our four maps, respectively. In 1986, savannas had the greatest area of any vegetation type in Yunnan at 40.30%, whereas forest cover was 30.78%. Savanna coverage declined across the decades mainly due to a drop in open parkland savannas, while forest cover remained stable. Savannas experienced greater fragmentation than forests. Savannas suffered direct loss of coverage to human uses and to woody encroachment. Savannas in more humid environments switched to denser vegetation at a higher rate. Fire slowed the rate of conversion away from savanna states and promoted conversion towards them. We identified remaining savannas in Yunnan that can be considered when drafting future protected areas. Our results can inform more inclusive policy-making that considers Yunnan’s forests and savannas as distinct vegetation types with different management needs.


2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Huaizhang Sun ◽  
Jiyan Wang ◽  
Junnan Xiong ◽  
Jinhu Bian ◽  
Huaan Jin ◽  
...  

The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.


2017 ◽  
Vol 25 (1) ◽  
pp. 43
Author(s):  
Qi Shuo ◽  
Yu Guo-hua ◽  
Lei Bo ◽  
Fan Yi ◽  
Zhang Deng-lin ◽  
...  
Keyword(s):  

2000 ◽  
pp. 26-31
Author(s):  
E. I. Parfenova ◽  
N. M. Chebakova

Global climate warming is expected to be a new factor influencing vegetation redistribution and productivity in the XXI century. In this paper possible vegetation change in Mountain Altai under global warming is evaluated. The attention is focused on forest vegetation being one of the most important natural resources for the regional economy. A bioclimatic model of correlation between vegetation and climate is used to predict vegetation change (Parfenova, Tchebakova 1998). In the model, a vegetation class — an altitudinal vegetation belt (mountain tundra, dark- coniferous subalpine open woodland, light-coniferous subgolets open woodland, dark-coniferous mountain taiga, light-coniferous mountain taiga, chern taiga, subtaiga and forest-steppe, mountain steppe) is predicted from a combination of July Temperature (JT) and Complex Moisture Index (CMI). Borders between vegetation classes are determined by certain values of these two climatic indices. Some bioclimatic regularities of vegetation distribution in Mountain Altai have been found: 1. Tundra is separated from taiga by the JT value of 8.5°C; 2. Dark- coniferous taiga is separated from light-coniferous taiga by the CMI value of 2.25; 3. Mountain steppe is separated from the forests by the CMI value of 4.0. 4. Within both dark-coniferous and light-coniferous taiga, vegetation classes are separated by the temperature factor. For the spatially model of vegetation distribution in Mountain Altai within the window 84 E — 90 E and 48 N — 52 N, the DEM (Digital Elevation Model) was used with a pixel of 1 km resolution. In a GIS Package IDRISI for Windows 2.0, climatic layers were developed based on DEM and multiple regressions relating climatic indices to physiography (elevation and latitude). Coupling the map of climatic indices with the authors' bioclimatic model resulted into a vegetation map for the region of interest. Visual comparison of the modelled vegetation map with the observed geobotanical map (Kuminova, 1960; Ogureeva, 1980) showed a good similarity between them. The new climatic indices map was developed under the climate change scenario with summer temperature increase 2°C and annual precipitation increase 20% (Menzhulin, 1998). For most mountains under such climate change scenario vegetation belts would rise 300—400 m on average. Under current climate, the dark-coniferous and light-coniferous mountain taiga forests dominate throughout Mountain Altai. The chern forests are the most productive and floristically rich and are also widely distributed. Under climate warming, light-coniferous mountain taiga may be expected to transform into subtaiga and forest-steppe and dark-coniferous taiga may be expected to transform partly into chern taiga. Other consequences of warming may happen such as the increase of forest productivity within the territories with sufficient rainfall and the increase of forest fire occurrence over territories with insufficient rainfall.


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