Next Article in Journal
Development of a Spectral Index for the Detection of Yellow-Flowering Vegetation
Previous Article in Journal
Basal Melt Patterns around the Deep Ice Core Drilling Site in the Dome A Region from Ice-Penetrating Radar Measurements
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 15
Issue 7
10.3390/rs15071727
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Vegetation Cover Variation in Dry Valleys of Southwest China: The Role of Precipitation

by
Qiankun Guo
1,
Ronghua Zhong
2,*,
Zhijie Shan
1 and
Xingwu Duan
2
1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100048, China
2
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(7), 1727; https://doi.org/10.3390/rs15071727
Submission received: 23 February 2023 / Revised: 10 March 2023 / Accepted: 16 March 2023 / Published: 23 March 2023
(This article belongs to the Section Ecological Remote Sensing)
Browse Figures
Review Reports Versions Notes

Abstract

:
Many ecological restoration projects have been carried out in Southwest China; however, changes in vegetation cover in the dry valleys of Southwest China have rarely been reported. With their unique characteristics of high temperatures and low humidity, these dry valleys have considerably lower vegetation cover than their neighboring areas, making them the main sediment sources of rivers in Southwest China. Thus, it is imperative to understand changes in vegetation cover in China’s dry valleys, as well as the effects of changes in precipitation, since water deficit is the dominant cause of obstructed plant growth. In this study, changes in fractional vegetation cover (FVC) in dry valleys in the period 2000 to 2020 were analyzed based on MODIS-NDVI data, and the effects of precipitation were also analyzed. Our results indicated that: (1) the long-term mean annual FVC values in the dry–hot valleys (DHVs), dry–warm valleys (DWVs), and dry–temperate valleys (DTVs) were 0.426, 0.504, and 0.446, respectively; (2) significant decreasing trends in FVC were mainly found in DHVs and DWVs that were distributed in the southwestern part of the dry valley region (DVR), which was mainly due to the decrease in precipitation; and (3) significant increasing trends were reported in DTVs of the Min River and the Baishui River, which was probably due to the increase in precipitation. By analyzing the temporal trends of FVC in dry valleys, this study highlighted the effects of precipitation on the dynamics of FVC and demonstrated that anthropogenic activities such as urbanization, land use changes, and hydro-power project construction may also have considerable effects on FVC in dry valleys. Overall, this study not only provides insights that might inform further detailed studies on the dynamics and mechanisms of vegetation cover, but could also provide valuable guidance for ecological restoration management in the dry valley region.

Graphical Abstract

1. Introduction

Vegetation plays a vital role in improving ecological systems [1], stabilizing soils, and minimizing erosion [2,3]. Since the 1990s, China has implemented a series of ecological protection and restoration programs, such as “Grain for Green”, “the Three-North Shelter Forest Project”, and “the Key Treatment Project of Soil and Water Conservation in the Upper and Middle Yangtze River” [4], which have resulted in substantial changes in FVC. In recent decades, numerous studies have focused on the dynamics of FVC and reported significant landscape greening across China [5,6,7,8,9,10,11] and its main regions, including the Loess Plateau [3,12,13,14,15], Northwest China [16,17,18,19], the Qinghai–Tibet Plateau [3,20,21], Northeast China [22,23], and Southwest China [24,25,26,27,28]. These studies have provided valuable information about the overall characteristics of the dynamics of FVC and the effects of the main influencing factors in their study regions [9,10,11,28,29,30,31].
However, these studies had some deficiencies. Firstly, most of them detected the overall dynamics of FVC either in an entire study region, such as the vast region of the Loess Plateau, Southwest China, or on a large scale, such as the entire Yangtze River basin; these methods overlooked internal variations in the dynamics of vegetation cover in the study regions, especially in topographically or climatically complicated regions. Southwest China is a world-famous region of substantial natural differentiation, with many high mountain areas and deep river valleys. Its mountainous region is mainly covered by abundant vegetation, while the characteristics of the valleys mainly include high temperatures, low humidity, predominantly bushveld vegetation, and a sparse distribution of trees [32,33,34]; these areas comprise the dry valley region (DVR). As an ecologically fragile region, the DVR is susceptible to soil erosion and thus is usually considered one of the most important sediment sources of many large rivers in China, such as the Changjiang River (the Yangtze River), the Lancangjiang River (the Mekong River in Southeast Asia), and the Nujiang River (the Salween River in Myanmar). Since vegetation plays a vital role in stabilizing soils and minimizing erosion [2,3], the dynamics of FVC in the DVR have recently attracted growing attention. Various studies conducted in recent decades have reported increasing trends in FVC in Southwest China [5,7,8,9,10,11,24]. The improvement in FVC is mainly attributed to implementation of ecological restoration projects in the mountainous region, which covers the majority of Southwest China. However, the dynamics of vegetation cover in the DVR have rarely been reported; this is probably because recent studies have mainly considered the entire region of Southwest China, and as dispersedly distributed valleys only account for a small proportion of Southwest China, it is difficult for them to attract public attention.
Studies on the effects of precipitation and anthropogenic activities on the dynamics of FVC are critical for ensuring effective soil and water management and ecological improvement. Some studies have focused on Southwest China, such as that of Tao et al. [26], who highlighted the effects of climate change, while Jiang et al. [24] and Yin et al. [28] emphasized the effects of human activities. However, such studies are largely lacking in the DVR. In dry valleys, the impacts of climate change (such as spatio-temporal changes in precipitation) and anthropogenic activities (such as urbanization and hydro-power project construction) are important factors that influence the dynamics of vegetation cover. Many studies agree that water plays a vital role in vegetation growth, that precipitation is the major driving force of greening in the DVR [15,35,36], and that a precipitation deficit is a major limitation of vegetation growth in the DVR [37,38]. Increases/decreases in precipitation may have profound influences on the dynamics of vegetation cover in the DVR, since moisture is usually scarce under the dry climate conditions of dry valleys. Currently, numerous studies have reported spatial and temporal variations in precipitation in Southwest China [39,40,41,42,43,44,45], while studies on precipitation dynamics and their effects on changes in FVC in the DVR are rare, making the effects of changes in precipitation on the dynamics of vegetation cover unclear.
On the other hand, many anthropogenic activities, such as urbanization, land use changes, and the construction of hydro-power projects, have taken place in the DVR [46]. Several studies have indicated that the high population density and intensive exploitation in the DVR could affect vegetation cover and lead to severe soil and water loss [47,48]. In this context, understanding the effects of changes in precipitation on the dynamics of FVC in the DVR could also provide important information for evaluating the effects of anthropogenic activities on FVC in the DVR.
Despite the numerous studies on the dynamics of FVC and its main influencing factors across Southwest China, few studies have focused on the ecologically fragile dry valleys that are dispersedly distributed in Southwest China, causing difficulties in the effective management of soil and water conservation projects in the entire region of Southwest China. Therefore, in this study, the dynamics of FVC in the DVR were analyzed for the main dry valleys across Southwest China, and the effects of changes in precipitation were also evaluated for these valleys. The objectives of this study were: (1) to determine the dynamics of FVC in the main dry valleys in the DVR from 2000 to 2020, and (2) to assess the effects of precipitation changes on the dynamics of FVC in these valleys. By reporting the dynamics of FVC in the ecologically fragile dry river valleys of Southwest China, this study could serve as an important scientific basis for comprehensively understanding the mechanisms behind the dynamics of FVC in the DVR; this is important for ensuring the effective management of soil and water conservation projects in the entire region of Southwest China.

2. Materials and Methods

2.1. Characteristics of Dry Valleys

The complex topography of Southwest China has contributed substantially to the great variability in the redistribution of heat, rainfall, and water [49]. Its high mountains and deeply incised valleys create a strong foehn effect, making these valleys a striking geographical landscape with an arid climate, less rainfall, and higher temperatures and evaporation compared with their neighboring areas and placing them among the most fragile and degraded ecosystems in Southwest China [37,48]. There are a total of 20 dry valleys that are geographically distributed along some important rivers and their tributaries, such as the upper Yangtze (Jinsha), Lancang (Mekong), Nu (Salween), Yuan (Red), Shuiluo, Yalong, Dadu, Min, and Baishui Rivers [50]; these valleys cover four provinces: Sichuan, Yunnan, Tibet, and Gansu (Figure 1). According to the substantial variations in the climate, soil, vegetation, and agricultural conditions of these dry valleys, Zhang [37] classified the DVR into three sub-regions (the dry–hot valley (DHV), dry–warm valley (DWV), and dry–temperate valley (DTV)) based on the classification criteria listed in Table 1. These three sub-regions are geographically distributed from south to north [50]. The basic characteristics of these dry valleys are presented in Table 2, in which the precipitation and temperature data were downloaded from the national meteorological data center (http://data.cma.cn, accessed on 10 May 2021), and the elevation and slope data were extracted from an ALOS 12.5 m DEM (https://search.asf.alaska.edu/, accessed on 3 December 2020).
The total area of the DVR is about 33,400 km2, and the area proportions of the DHV, DWV, and DTV regions are 50.3%, 32.7%, and 16.7%, respectively, indicating that the DHVs have the largest area. The area-weighted average annual temperatures are 19.6, 15.0, and 9.9 ℃ for the DHVs, DWVs, and DTVs, respectively (Table 2), revealing an approximately 5 ℃ grade from north to south (Table 2). The area-weighted average elevation values are 1272.1 m, 1804.5 m, and 2658.8 m for the DHVs, DWVs, and DTVs, respectively, revealing a substantial increment from south to north. The area-weighted slope values are 22.4°, 25.8°, and 31.3°, for the DHVs, DWVs, and DTVs, respectively, indicating that the mean slopes become steeper from south to north. The area-weighted average annual precipitation values are 954.9, 924.7, and 779.3 mm for the DHVs, DWVs, and DTVs, respectively, revealing decreasing trends from south to north. It can be inferred that despite the higher annual precipitation in the DHVs than in both the DWVs and DTVs, the highest temperature in the DHVs could result in the highest evaporation, making them more fragile than dry–warm and dry–temperate valleys.
The land use data of all the dry valleys were interpreted based on Gaofen-1 and Gaofen-2 satellite remote-sensing images acquired in 2019. The Gaofen series are among the most important high-resolution satellites in the China Earth Observation System. For each valley, six main land use types, including arable land, orchard, forest, grassland, construction land, and water bodies, were interpreted, and the geographical distribution of these land use types are presented in Figure 2. The area proportions of each land use type in the DHVs, DWVs, and DTVs are listed in Table 3. The area proportions of arable land and orchard were the highest in the DHVs and lowest in the DTVs, probably due to the more suitable temperature and gentle slope in the DHVs compared with the DWVs and DTVs. Land uses with perennial vegetation cover (forest and grassland) accounted for the largest area proportion in all the dry valleys, ranging from 62.25% in the DHVs to 67.55% in the DWVs and 87.26% in the DTVs. It was found that the area proportions of forest and grassland in all the dry valleys were substantially higher than those of the other land uses, emphasizing the importance of understanding the dynamics of vegetation cover and the effects of the main influencing factors in these valleys.

2.2. Calculation of FVC

The dynamics of FVC in each dry valley were determined based on a difference analysis of the multi-phase satellite images, whereby the Normalized Difference Vegetation Index (NDVI), which is probably the most widely used product of the Moderate-Resolution Imaging Spectroradiometer (MODIS), was used to detect changes in FVC in the DVR. Based on linear spectral unmixing, a method for calculating FVC based on the NDVI product was proposed in [31]. Currently, the NDVI and FVC are widely used to study changes in vegetation cover and the effects of driving forces on the dynamics of vegetation cover [9].
The NDVI products were downloaded from the publicly available United States Geological Survey (https://ladsweb.modaps.eosdis.nasa.gov/search/order/1/MOD13Q1-6, accessed on 2 May 2021). FVC data from 2000 to 2020 were derived from the 250 m resolution MODIS product (MOD13Q1), with a time interval of 16 days. To estimate FVC, a dimidiate pixel model based on the NDVI was used. The following formula was used to calculate the FVC of each grid in a specific image:
F V C = ( N D V I − N D V I s o i l ) / ( N D V I v e g − N D V I s o i l )
N D V I = ( N I R − R E D ) / ( N I R + R E D )
where N I R is the near-infrared band and R E D is the red-light band (both variables were estimated based on Landsat-TM/ETM+ remote-sensing images); N D V I s o i l is the NDVI value of a bare soil area; and N D V I v e g is the NDVI value of vegetation coverage. In this study, a cumulative frequency of 5% was extracted from the NDVI cumulative frequency table for N D V I s o i l , and one of 95% was extracted for N D V I v e g .
The FVC from 2000 to 2020 with a 16-day time interval was estimated based on Equations (1) and (2), and the long-term geographical distribution of FVC for the 20 dry valleys was established; then, the dynamics of vegetation cover and the temporal trends of FVC were analyzed for all the dry valleys in the DVR. Moreover, in order to detect the geographical distribution of the dynamics of FVC in the dry valleys, the decadal average FVC values of each valley for the periods 2000–2010 and 2011–2020 were also calculated and compared.

2.3. Evaluation of Effects of Main Influencing Factors on FVC Dynamics

Precipitation deficit is considered the main cause of decreased vegetation in the DVR in Southwest China [25]; thus, it is important to detect the effects of precipitation on the dynamics of vegetation cover in dry valleys in the DVR. In this study, daily precipitation data from 2000 to 2020, measured by weather stations across Southwest China, were collected from the National Meteorological Data Centre (http://data.cma.cn, accessed on 10 May 2021). For each year, the geographical distribution of annual precipitation across Southwest China and its dry valleys was determined, and the annual precipitation for each year from 2000 to 2020 in each dry valley was analyzed. Then, the relationships between annual precipitation and annual FVC for each dry valley were tested using the Spearman correlation analysis method at a significance level of 0.05.

3. Results

3.1. Average Annual FVC in Dry Valleys from 2000 to 2020

The average annual FVC values of 20 dry valleys from 2000 to 2020 are presented in Table 4, and their geographical distribution is presented in Figure 3. The average annual FVC ranged from 0.291 to 0.574, with an area-weighted average value of 0.465. The average annual FVC differed substantially among dry valleys, and the three grades can be classified according to the values of the different dry valleys: low grade (0.291 to 0.320), medium grade (0.366 to 0.461), and high grade (0.495 to 0.574).
The dry valleys with a low grade were mainly in the upper stream of the Lancang, Jinsha, and Nu Rivers, which is geographically located in the western part of the DTV area, which forms part of the widely known “Three Parallel Rivers Region”. The average annual FVC of these three dry valleys ranged from 0.291 to 0.320, with an area-weighted average value of 0.297 (Table 4; Figure 3). The low values of vegetation cover in this grade were mainly attributed to the combination of less precipitation, lower temperature, higher elevation, and steeper slopes compared with the other regions (Table 2).
The dry valleys with a medium grade were primarily distributed in the dry–hot valleys and some dry–warm valleys, including the dry–hot valleys of the Yuan, Luzhi, Jinsha, and Lancang Rivers, and the dry–warm valleys of the Yuan and Anning Rivers. The average annual FVC of these six valleys ranged from 0.366 to 0.461, with an area-weighted average value of 0.448 (Table 4; Figure 3). These values were higher than in the low-grade valleys, probably due to the higher annual precipitation, higher annual temperature, and gentler slopes in this grade. The annual FVC values of all six valleys were less than 0.5, which was probably due to the following two reasons: (1) the high evaporation (Table 2) in these valleys resulted in a lack of available water and thus restricted vegetation growth, and (2) anthropogenic activities, such as city and rural expansion, road construction, dam construction, and a tropical orchard plantation (Figure 2), were very intense in these valleys, contributing substantially to the low FVC values. All the dry–hot valleys and the dry–warm valley in the Yuan River in this grade are geographically adjacent to each other and share similar climates, topography, and land uses; thus, the FVC values in these five valleys were similar. However, despite being much further north compared with the other valleys in this grade, the dry–warm valley in the Anning River had similar FVC values to other valleys in this grade, which were considerably lower than those of the adjacent dry–warm valleys. The unique land use characteristic of the dry–warm Anning valley may explain this result. The area proportions of arable land (mainly paddy) and construction land in this flat, wide valley accounted for 53.2% and 18.2% of the total area, respectively, while forest and grassland only accounted for 15.1% and 6% (Figure 2; Table 3), respectively. Clearly, the lower area proportion of forest and grassland in the dry–warm Anning valley compared with other valleys (Figure 2; Table 3) contributed significantly to the low FVC value.
The dry valleys with a high grade were the dry–warm and dry–temperate valleys in the Yangtze River and its tributaries, together with the dry–warm valley in the Lancang River and the dry–hot valley in the Nu River. The average annual FVC of these 11 valleys ranged from 0.495 to 0.574, with an area-weighted average value of 0.526 (Table 4; Figure 3). The relatively high FVC values in these valleys were attributed to the following: (1) the warm and temperate climate conditions with less evaporation and low elevation (Table 2) are more suitable for vegetation growth than the conditions in valleys with low and medium grades, and (2) anthropogenic activities in these valleys are relatively less frequent due to their complicated topography and steep slopes (Table 2).
The area-weighted annual FVC averages in the DVR, DHVs, DWVs, and DTVs were 0.452, 0.426, 0.504, and 0.446 (Table 4; Figure 3), respectively, indicating that the average FVC value was highest in the dry–warm valleys and lowest in the dry–hot valleys. The lowest FVC occurring in the dry–hot valleys was due to the higher temperature, higher evaporation, and relatively intense anthropogenic activity, while the highest FVC occurring in the dry–warm valleys was attributed to their suitable climate conditions (mainly moderate temperature and precipitation) and reduced anthropogenic activities.

3.2. The Dynamics of Vegetation Cover in Dry Valleys

The temporal trends of the area-weighted annual FVC averages in the DVR, DHVs, DWVs, DTVs, and each dry valley from 2000 to 2020 were also analyzed and are presented in Table 5. Statistically significant decreasing trends were found in the DVR and DHVs at a significance level of 0.05, while insignificant trends were reported in the DWVs and DTVs. For dry–hot valleys, statistically significant decreasing trends were found in the Lancang, Yuan, and Luzhi Rivers at a significance level of 0.05, while insignificant decreasing trends were found in the Nu and Jinsha Rivers. The valleys with statistically significant trends were mainly located in the southern part of the DHV region, while those with insignificant trends were located in the north. For dry–warm valleys, significant decreasing trends at a significance level of 0.05 were found in the Yuan, Jinsha, and Anning Rivers, which are located in the southwestern part of the DWV region, except for the Anning River. A significant increasing trend at a significance level of 0.05 was found in the Lancang River, while insignificant trends occurred in the Min, Dadu, and Yalong Rivers, which are mainly located in the northeastern part of the DMV region. The dry–warm valley in the Anning River, despite being in the northeastern part of the DMV region, revealed significant decreasing trends in annual FVC due to the occurrence of rapid socioeconomic development, such as urbanization and infrastructure construction, in recent decades. For dry–temperate valleys, a significant decreasing trend at a significance level of 0.05 was reported in the Shuiluo River, which is located in the southwestern part of the DTV region, while significant increasing trends were reported in the Min and Baishui Rivers in the northeastern part of the DTV region.
The difference in FVC between the two decades (2000–2010 and 2011–2020) is presented in Figure 4. The difference in FVC ranged from −0.59 to 0.38, with a mean value of −0.04, indicating a slight reduction between the two periods. The area proportions of decreasing and increasing trends in the DVR were 79.48% and 20.52%, respectively, indicating that the average annual FVC decreased in most dry valleys. Statistically significant decreasing trends (Table 5) were mainly reported in the southwestern part of the DVR, while statistically significant increasing trends were found in the northeastern part, mainly in the Min and Baishui Rivers.

3.3. Effects of Precipitation on the Dynamics of Vegetation Cover

The relationships between annual FVC and precipitation were analyzed to detect the effects of precipitation on the dynamics of FVC, and the results are presented in Table 5. The relationship between annual FVC and annual precipitation in the DHVs was significant at a significance level of 0.05, while their relationship in the DVR, DWVs, and DTVs was insignificant.
Specifically, statistically significant relationships between annual precipitation and FVC in the DHVs were found in the Yuan, Luzhi, and Lancang Rivers at a significance level of 0.05, indicating that for these valleys, changes in annual precipitation contributed substantially to the dynamics of annual FVC. This was probably because the decrease in annual precipitation in the DHVs reduced air moisture and increased evaporation, making these dry–hot valleys drier and hotter under the dry–hot climate conditions. This likely greatly obstructed vegetation growth and resulted in a statistically significant decrease in annual FVC in most dry–hot valleys.
For the dry–warm valleys, although significant temporal trends in FVC were found in the Yuan, Lancang, Jinsha, and Anning Rivers, a statistically significant relationship between annual precipitation and FVC was only reported in the Yuan River, implying that changes in annual precipitation near the rivers of all these valleys, except the Yuan River, were not the dominant factors affecting the dynamics of FVC. For the Yuan River, the changes in annual precipitation contributed substantially to changes in annual FVC, which was probably because the decrease in annual precipitation made this valley drier and hotter and thus obstructed vegetation growth; this is similar to the reason given for the dry–hot valleys in the Yuan and Luzhi River, since these valleys are geographically adjacent to each other and share similar climate conditions. It was noticed that for the Lancang, Jinsha, and Anning Rivers in the DWVs, changes in annual precipitation probably did not play a dominant role in the dynamics of FVC, while anthropogenic activities may have contributed substantially to the changes in FVC in these valleys.
For dry–temperate valleys, a statistically significant decreasing trend in annual FVC was reported in the Shuiluo River, while significant increasing trends were found in the Min and Baishui Rivers. Among these, the relationships between annual precipitation and annual FVC were statistically significant in the Min and Baishui Rivers, indicating that changes in precipitation contributed substantially to the increase in annual FVC. However, the relationship between annual precipitation and annual FVC in the Shuiluo River was statistically insignificant, indicating that precipitation was not the primary factor affecting the dynamics of FVC in the Shuiluo River; this implied that anthropogenic activities in the dry–temperate valley in the Shuiluo River may have also contributed substantially to the reduction in vegetation cover.
It can be concluded that for valleys with statistically significant temporal trends in FVC, the dynamics of FVC in the Yuan, Luzhi, and Lancang Rivers in the DHVs, the Yuan River of the DMVs, and in the Min and Baishui Rivers of the DTVs were significantly influenced by the changes in annual precipitation, while anthropogenic activities may have contributed substantially to the dynamics of FVC in the Lancang and Jinsha Rivers of the DMVs and the Shuiluo River of the DTVs. Geographically, annual FVC increased significantly with annual precipitation in the dry–temperate valleys in the Min and Baishui Rivers, which were in the northeastern part of the DVR, while annual FVC decreased significantly with annual precipitation in the dry–hot valleys in the Yuan, Luzhi, and Lancang Rivers and the dry–warm valley in the Yuan River, which are mainly located in the southwestern part of the DVR. However, although significant changes in FVC were reported in the dry–warm valleys in the Jinsha, Anning, and Lancang Rivers and the dry–temperate valleys in the Shuiluo River, the relationships between the dynamics of FVC and the changes in annual precipitation in these valleys were not significant, implying that anthropogenic activities in these areas may have substantially influenced the significant changes in FVC in these valleys.

4. Discussion

As indicated, numerous studies have focused on the dynamics of FVC in Southwest China. Such studies are valuable for elucidating the overall spatio-temporal trends in FVC across Southwest China; however, these studies may not have fully considered the substantial internal differences in vegetation cover, which are mainly caused by the complex topology and climate of Southwest China. Some authors have noticed this problem and have tried to identify regional differences in FVC in Southwest China, such as Tao et al. [26], who indicated that vegetation greening was stronger in plateaus with high elevation than in mountains with lower elevation; additionally, Jiang et al. [24] indicated that the NDVI in Southwest China increased significantly below an elevation of 3400 m after the 1980s but decreased above 3400 m in the 2000s. However, simply relying on elevation is insufficient to comprehensively understand the dynamics of vegetation cover in the topographically and climatically complicated region of Southwest China. In this study, considering its great natural variability, Southwest China was divided into a mountain region and a valley region to characterize its internal differences. This study provided the dynamics of vegetation cover in the dry valley region of Southwest China and indicated that the area-weighted average annual FVC in the DVR was 0.465, which was substantially lower than the value of 0.72–0.77 in Southwest China reported by Ma et al. [30]; meanwhile, this study showed a statistically significant decreasing trend in the DVR, which was also quite different to the increasing trend in FVC and the NDVI across Southwest China. This study highlighted the differences in the dynamics of vegetation cover between the DVR and the entire region of Southwest China, indicating that attention should be paid to FVC changes in dry valleys in the future; this is important for providing a comprehensive understanding of the dynamics of FVC in Southwest China, and it could also provide useful guidance for the deployment of ecological restoration and environmental management measures in the DVR.
This study highlighted the effects of annual precipitation on the dynamics of FVC in the DVR. In this study, significant temporal trends in annual FVC from 2000 to 2020 were reported in 10 dry valleys; significant relationships between annual FVC and annual precipitation were found in 6 of the 10 dry valleys, indicating that precipitation has contributed greatly to the changes in FVC in dry valleys. We also noticed that annual precipitation did not play an important role in the remaining four valleys, implying that anthropogenic activities contribute substantially to changes in annual FVC in some dry valleys. Of these four valleys, the annual FVC from 2000 to 2020 increased significantly in the dry–warm valley in the Lancang River, which was probably caused by the large-scale ecological restoration projects implemented by the government. However, the annual FVC decreased significantly in the remaining three valleys (the dry–warm valleys in the Anning River and Jinsha River, and the dry–temperate valley in the Shuiluo River). The dry–warm valley in the Anning River is the capital of the Liangshan Autonomous Prefecture, Sichuan Province, and has thus experienced rapid socioeconomic development in recent decades, such as urbanization and infrastructure construction; this has led to the conversion of perennial vegetation cover to constructed land, resulting in a significant decrease in FVC. The significant decreases in FVC in the DWV in the Jinsha River and the DTV in the Shuiluo River were probably due to anthropogenic activities such as city and rural expansion, infrastructure construction, and the construction of hydro-power projects. Southwest China is the most important hydroelectric energy base in China [46], and a cascade of hydro-power projects has been constructed, are under construction, or have been planned for almost all the rivers in the DVR (Figure 5; Table 6). Many studies have mentioned that disturbances caused by the construction of hydro-power projects can greatly affect vegetation cover and lead to severe soil and water loss [47,48,51]; thus, understanding the effects of the construction of hydro-power projects on the dynamics of annual FVC in dry valleys is of great importance, because many large-scale hydro-power projects have been planned and will soon be constructed in these dry valleys. For example, many planned large-scale hydro-power projects will soon be constructed in the Yuan River and in the upper parts of the Jinsha, Shuiluo, Yalong, and Dadu Rivers (Figure 5). By analyzing the dynamics of FVC in dry valleys, this study determined the possible effects of the construction of hydro-power projects; thus, more quantitative evaluations of the effects of the construction of hydro-power projects on the dynamics of vegetation cover in the DVR should be conducted to help achieve the optimal deployment and management of ecological restoration measures during and after the construction of hydro-power projects.
Many studies have indicated that changes in vegetation cover may cause substantial changes in flow discharge and sediment load in rivers, such as the “Grain for Green” project launched in the Loess Plateau, which greatly improved vegetation cover and contributed substantially to the dramatic reduction in runoff and sediment in the Yellow River and its tributaries [3,11,15,29,38,51,52,53,54,55]. Compared with the abundant research conducted on the Loess Plateau, studies on the effects of the dynamics of vegetation cover on changes in runoff and sediment in Southwest China are much more scarce. Furthermore, the dynamics of vegetation cover in dry valleys are different to those in mountainous regions; thus, the combined effects of changes in vegetation cover in both the mountains and the valleys on the dynamics of runoff and sediment are still unclear. Therefore, more studies on the combined effects of the dynamics of vegetation cover on changes in runoff and sediment should be conducted on important rivers in both the mountainous and dry valley regions of Southwest China.

5. Conclusions

This study explored the dynamics of fractional vegetation cover in the dry valley region of Southwest China from 2000 to 2020. We found that the long-term mean annual FVC for the dry valley region, the dry–hot valleys, the dry–warm valleys, and the dry–temperate valleys were 0.452, 0.426, 0.504, and 0.446, respectively. Significant decreasing trends in FVC were mainly found in the dry–hot and dry–warm valleys, which are located in the southwestern part of the DVR, and relationships between FVC and precipitation were found in these valleys. Meanwhile, significant increasing trends were found in the dry–temperate valleys of the Min River and Baishui River. Changes in precipitation had a significant relationship with the dynamics of FVC in many valleys, among which decreases in FVC in the Yuan, Luzhi, and Lancang Rivers in the DHV region and the Yuan River in the DMV region were significantly attributed to the decrease in annual precipitation. Meanwhile, the increases in FVC in the Min and Baishui Rivers in the DTV region were significantly affected by increases in annual precipitation. These results suggest that anthropogenic activities may have contributed substantially to the dynamics of FVC in some dry valleys. We recommend further in-depth studies to quantify the effects of anthropogenic activities on the dynamics of annual FVC in dry valleys.

Author Contributions

Methodology, investigation, formal analysis, and writing (original draft), Q.G.; writing (review and editing), Z.S.; writing (review and editing), X.D.; conceptualization, writing (original draft), supervision, and project administration, R.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (No. 42007068), the Less Developed Regions of the National Natural Science Foundation of China (No. 42067019), and the National Natural Science Foundation of China—Yunnan Joint Fund (No. U2102209 and U2002209).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Curtis, L. Remote sensing systems for monitoring crops and vegetation. Prog. Phys. Geogr. 1978, 2, 55–79. [Google Scholar] [CrossRef]
  2. Fu, B. Soil erosion and its control in the Loess Plateau of China. Soil Use Manag. 1989, 5, 76–82. [Google Scholar] [CrossRef]
  3. Wang, S.; Fu, B.J.; Piao, S.L.; Lü, Y.H.; Ciais, P.; Feng, X.M.; Wang, Y.F. Reduced sediment transport in the Yellow River due to anthropogenic changes. Nat. Geosci. 2016, 9, 38–41. [Google Scholar] [CrossRef]
  4. Liu, J.; Li, S.; Ouyang, Z.; Tam, C.; Chen, X. Ecological and socioeconomic effects of China’s policies for ecosystem services. Proc. Natl. Acad. Sci. USA 2008, 105, 9477–9482. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Chen, C.; Park, T.; Wang, X.; Piao, S.; Xu, B.; Chaturvedi, R.K.; Fuchs, R.; Brovkin, V.; Ciais, P.; Fensholt, R.; et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2019, 2, 122–129. [Google Scholar] [CrossRef] [PubMed]
  6. Lü, Y.; Zhang, L.; Feng, X.; Zeng, Y.; Fu, B.; Yao, X.; Li, J.; Wu, B. Recent ecological transitions in China: Greening, browning and influential factors. Sci. Rep. 2015, 5, 8732. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Piao, S.; Yin, G.; Tan, J.; Cheng, L.; Huang, M.; Li, Y.; Liu, R.; Mao, J.; Myneni, R.B.; Peng, S.; et al. Detection and attribution of vegetation greening trend in China over the last 30 years. Glob. Chang. Biol. 2015, 21, 1601–1609. [Google Scholar] [CrossRef]
  8. Wang, H.; Chen, A.; Wang, Q.; He, B. Drought dynamics and impacts on vegetation in China from 1982 to 2011. Ecol. Eng. 2015, 75, 303–307. [Google Scholar] [CrossRef]
  9. Xu, G.; Zhang, J.; Li, P.; Li, Z.; Lu, K.; Wang, X.; Wang, F.; Cheng, Y.; Wang, B. Vegetation restoration projects and their influence on runoff and sediment in China. Ecol. Indic. 2018, 95, 233–241. [Google Scholar] [CrossRef]
  10. Yu, L.; Liu, Y.; Liu, T.; Yan, F. Impact of recent vegetation greening on temperature and precipitation over China. Agric. For. Meteorol. 2020, 295, 108197. [Google Scholar] [CrossRef]
  11. Yu, L.; Xue, Y.; Diallo, I. Vegetation greening in China and its effect on summer regional climate. Sci. Bull. 2021, 66, 13–17. [Google Scholar] [CrossRef] [PubMed]
  12. Chen, Y.; Wang, K.; Lin, Y.; Shi, W.; Song, Y.; He, X. Balancing green and grain trade. Nat. Geosci. 2015, 8, 739–741. [Google Scholar] [CrossRef]
  13. Feng, X.; Fu, B.; Piao, S.; Wang, S.; Ciais, P.; Zeng, Z.; Lü, Y.; Zeng, Y.; Li, Y.; Jiang, X.; et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 2016, 6, 1019–1029. [Google Scholar] [CrossRef]
  14. Fu, B.; Wang, S.; Liu, Y.; Liu, J.; Liang, W.; Miao, C. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci. 2017, 45, 223–243. [Google Scholar] [CrossRef]
  15. Kou, P.; Xu, Q.; Jin, Z.; Yunus, A.P.; Luo, X.; Liu, M. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau. Sci. Total. Environ. 2021, 778, 146065. [Google Scholar] [CrossRef]
  16. Du, J.; Shu, J.; Yin, J.; Yuan, X.; Jiaerheng, A.; Xiong, S.; He, P.; Liu, W. Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China. Int. J. Appl. Earth Obs. Geoinf. 2015, 38, 216–228. [Google Scholar] [CrossRef]
  17. Fang, S.; Yan, J.; Che, M.; Zhu, Y.; Liu, Z.; Pei, H.; Zhang, H.; Xu, G.; Lin, X. Climate change and the ecological responses in Xinjiang, China: Model simulations and data analyses. Quat. Int. 2013, 311, 108–116. [Google Scholar] [CrossRef]
  18. Liang, S.; Yi, Q.; Liu, J. Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecol. Indic. 2015, 58, 64–76. [Google Scholar] [CrossRef]
  19. Shao, S.S.; Shi, Q.D. Spatial and temporal change of vegetation cover in Xinjiang based on FVC. Sci. Silvae Sin. 2015, 51, 35–42, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  20. Liu, Z.Y.; Zhang, J.; Chen, L. The latest change in the Qinghai-Tibetan Plateau vegetation index and its relationship with climate factors. Clim. Environ. Res. 2017, 22, 289–300, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  21. Peng, L.; Deng, W.; Liu, Y. Understanding the Role of Urbanization on Vegetation Dynamics in Mountainous Areas of Southwest China: Mechanism, Spatiotemporal Pattern, and Policy Implications. ISPRS Int. J. Geo-Int. 2021, 10, 590. [Google Scholar] [CrossRef]
  22. Chu, H.; Venevsky, S.; Wu, C.; Wang, M. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Sci. Total. Environ. 2019, 650, 2051–2062. [Google Scholar] [CrossRef] [PubMed]
  23. Zhou, S.; Zhang, W.; Wang, S.; Zhang, B.; Xu, Q. Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin. Remote Sens. 2021, 13, 684. [Google Scholar] [CrossRef]
  24. Jiang, S.; Chen, X.; Smettem, K.; Wang, T. Climate and land use influences on changing spatiotemporal patterns of mountain vegetation cover in southwest China. Ecol. Indic. 2021, 121, 107193. [Google Scholar] [CrossRef]
  25. Li, X.; Li, Y.; Chen, A.; Gao, M.; Slette, I.J.; Piao, S. The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China. Agric. For. Meteorol. 2019, 269, 239–248. [Google Scholar] [CrossRef]
  26. Tao, J.; Xu, T.; Dong, J.; Yu, X.; Jiang, Y.; Zhang, Y.; Huang, K.; Zhu, J.; Dong, J.; Xu, Y.; et al. Elevation-dependent effects of climate change on vegetation greenness in the high mountains of southwest China during 1982–2013. Int. J. Clim. 2018, 38, 2029–2038. [Google Scholar] [CrossRef]
  27. Zheng, Z.J.; Zeng, Y.; Zhao, Y.J.; Zhao, D.; Wu, B.F. Monitoring and dynamic analysis of fractional vegetation cover in southwestern China over the past 15 years based on MODIS data. Remote Sens. Land Resour. 2017, 29, 128–136, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  28. Yin, L.; Dai, E.; Zheng, D.; Wang, Y.; Ma, L.; Tong, M. What drives the vegetation dynamics in the Hengduan Mountain region, southwest China: Climate change or human activity? Ecol. Indic. 2020, 112, 106013. [Google Scholar] [CrossRef]
  29. Li, J.; Peng, S.; Li, Z. Detecting and attributing vegetation changes on China’s Loess Plateau. Agric. For. Meteorol. 2017, 247, 260–270. [Google Scholar] [CrossRef]
  30. Ma, H.-Y.; Zhang, L.-L.; Wei, X.-Q.; Shi, T.-T.; Chen, T.-X. Spatial and temporal variations of land use and vegetation cover in Southwest China from 2000 to 2015. Chin. J. Appl. Ecol. 2021, 32, 618–628, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  31. Wu, D.; Wu, H.; Zhao, X.; Zhou, T.; Tang, B.; Zhao, W.; Jia, K. Evaluation of Spatiotemporal Variations of Global Fractional Vegetation Cover Based on GIMMS NDVI Data from 1982 to 2011. Remote Sens. 2014, 6, 4217–4239. [Google Scholar] [CrossRef] [Green Version]
  32. Dong, Y.; Xiong, D.; Su, Z.; Li, J.; Yang, D.; Shi, L.; Liu, G. The distribution of and factors influencing the vegetation in a gully in the Dry-hot Valley of southwest China. Catena 2014, 116, 60–67. [Google Scholar] [CrossRef]
  33. Duan, X.; Liu, B.; Gu, Z.; Rong, L.; Feng, D. Quantifying soil erosion effects on soil productivity in the dry-hot valley, southwestern China. Environ. Earth Sci. 2016, 75, 1164. [Google Scholar] [CrossRef]
  34. Su, Z.; Xiong, D.; Dong, Y.; Li, J.; Yang, D.; Zhang, J.; He, G. Simulated headward erosion of bank gullies in the Dry-hot Valley Region of southwest China. Geomorphology 2014, 204, 532–541. [Google Scholar] [CrossRef]
  35. Cai, D.; Ge, Q.; Wang, X.; Liu, B.; Goudie, A.S.; Hu, S. Contributions of ecological programs to vegetation restoration in arid and semiarid China. Environ. Res. Lett. 2020, 15, 114046. [Google Scholar] [CrossRef]
  36. Shao, R.; Zhang, B.; Su, T.; Long, B.; Cheng, L.; Xue, Y.; Yang, W. Estimating the Increase in Regional Evaporative Water Consumption as a Result of Vegetation Restoration Over the Loess Plateau, China. J. Geophys. Res. Atmos. 2019, 124, 11783–11802. [Google Scholar] [CrossRef]
  37. Zhang, R.Z. The Dry Valley of the Hengduan Mountains Regions; Science Press: Beijing, China, 1992. (In Chinese) [Google Scholar]
  38. Li, G.; Sun, S.B.; Han, J.C.; Yan, J.W.; Liu, W.B.; Wei, Y.; Lu, N.; Sun, Y.Y. Impacts of Chinese Grain for Green program and climate change on vegetation in the Loess Plateau during 1982–2015. Sci. Total Environ. 2019, 660, 177–187. [Google Scholar] [CrossRef]
  39. Jiang, X.W.; Li, Y.Q. Spatio-temporal variability of winter temperature and precipitation in Southwest China. J. Geogr. Sci. 2011, 21, 250–262. [Google Scholar] [CrossRef]
  40. Zhen-Feng, M.; Jia, L.; Shun-Qian, Z.; Wen-Xiu, C.; Shu-Qun, Y. Observed Climate Changes in Southwest China during 1961–2010. Adv. Clim. Chang. Res. 2013, 4, 30–40. [Google Scholar] [CrossRef]
  41. Qin, N.; Chen, X.; Fu, G.; Zhai, J.; Xue, X. Precipitation and temperature trends for the Southwest China: 1960–2007. Hydrol. Process. 2010, 24, 3733–3744. [Google Scholar] [CrossRef]
  42. Shi, P.; Wu, M.; Qu, S.; Jiang, P.; Qiao, X.; Chen, X.; Zhou, M.; Zhang, Z. Spatial Distribution and Temporal Trends in Precipitation Concentration Indices for the Southwest China. Water Resour. Manag. 2015, 29, 3941–3955. [Google Scholar] [CrossRef]
  43. Wang, S.; Jiao, S.; Xin, H. Spatio-temporal characteristics of temperature and precipitation in Sichuan Province, Southwestern China. Quat. Int. 2013, 286, 103–115. [Google Scholar] [CrossRef]
  44. Yu, H.; Wang, L.; Yang, R.; Yang, M.; Gao, R. Temporal and spatial variation of precipitation in the Hengduan Mountains region in China and its relationship with elevation and latitude. Atmospheric Res. 2018, 213, 1–16. [Google Scholar] [CrossRef]
  45. Zhang, K.; Pan, S.; Cao, L.; Wang, Y.; Zhao, Y.; Zhang, W. Spatial distribution and temporal trends in precipitation extremes over the Hengduan Mountains region, China, from 1961 to 2012. Quat. Int. 2014, 349, 346–356. [Google Scholar] [CrossRef]
  46. Fu, B.; Wang, Y.; Xu, P.; Yan, K.; Li, M. Value of ecosystem hydropower service and its impact on the payment for ecosystem services. Sci. Total. Environ. 2014, 472, 338–346. [Google Scholar] [CrossRef]
  47. Chen, J.; Mei, Y.; Ben, Y.; Hu, T. Emergy-based sustainability evaluation of two hydropower projects on the Tibetan Plateau. Ecol. Eng. 2020, 150, 105838. [Google Scholar] [CrossRef]
  48. Yang, J.; El-Kassaby, Y.; Guan, W. Multiple Ecological Drivers Determining Vegetation Attributes across Scales in a Mountainous Dry Valley, Southwest China. Forests 2020, 11, 1140. [Google Scholar] [CrossRef]
  49. Tu, C.; He, T.; Lu, X.; Luo, Y.; Smith, P. Extent to which pH and topographic factors control soil organic carbon level in dry farming cropland soils of the mountainous region of Southwest China. Catena 2018, 163, 204–209. [Google Scholar] [CrossRef]
  50. Fan, J.R.; Yang, C.; Bao, W.K.; Liu, J.L.; Li, X. Distribution scope and district statistical analysis of dry valleys in Southwest China. Mt. Res. 2020, 38, 303–313, (In Chinese with English Abstract). [Google Scholar] [CrossRef]
  51. Zhang, P.; Cai, Y.; Yang, W.; Yi, Y.; Yang, Z.; Fu, Q. Contributions of climatic and anthropogenic drivers to vegetation dynamics indicated by NDVI in a large dam-reservoir-river system. J. Clean. Prod. 2020, 256, 120477. [Google Scholar] [CrossRef]
  52. Gao, P.; Mu, X.-M.; Wang, F.; Li, R. Changes in streamflow and sediment discharge and the response to human activities in the middle reaches of the Yellow River. Hydrol. Earth Syst. Sci. 2011, 15, 1–10. [Google Scholar] [CrossRef] [Green Version]
  53. Kong, D.; Miao, C.; Borthwick, A.G.L.; Lei, X.; Li, H. Spatiotemporal variations in vegetation cover on the Loess Plateau, China, between 1982 and 2013: Possible causes and potential impacts. Environ. Sci. Pollut. Res. 2018, 25, 13633–13644. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Zhao, A.; Zhang, A.; Lu, C.; Wang, D.; Wang, H.; Liu, H. Spatiotemporal variation of vegetation coverage before and after implementation of Grain for Green Program in Loess Plateau, China. Ecol. Eng. 2017, 104, 12–22. [Google Scholar] [CrossRef]
  55. Zhao, G.; Mu, X.; Jiao, J.; Gao, P.; Sun, W.; Li, E.; Wei, Y.; Huang, J. Assessing response of sediment load variation to climate change and human activities with six different approaches. Sci. Total. Environ. 2018, 639, 773–784. [Google Scholar] [CrossRef] [PubMed]
Remotesensing 15 01727 g001 550
Figure 1. Geographical distribution of dry valleys in Southwest China.
Figure 1. Geographical distribution of dry valleys in Southwest China.
Remotesensing 15 01727 g001
Remotesensing 15 01727 g002 550
Figure 2. Spatial distribution of land use types in dry valleys of Southwest China.
Figure 2. Spatial distribution of land use types in dry valleys of Southwest China.
Remotesensing 15 01727 g002
Remotesensing 15 01727 g003 550
Figure 3. Spatial distribution of long-term annual FVC in dry valleys of Southwest China.
Figure 3. Spatial distribution of long-term annual FVC in dry valleys of Southwest China.
Remotesensing 15 01727 g003
Remotesensing 15 01727 g004 550
Figure 4. Changes in FVC in dry valleys between the periods 2000–2010 and 2011–2020.
Figure 4. Changes in FVC in dry valleys between the periods 2000–2010 and 2011–2020.
Remotesensing 15 01727 g004
Remotesensing 15 01727 g005 550
Figure 5. Geographical distribution of hydro-power projects in dry valleys of Southwest China.
Figure 5. Geographical distribution of hydro-power projects in dry valleys of Southwest China.
Remotesensing 15 01727 g005
Table
Table 1. Classification criteria of dry valleys [37].
Table 1. Classification criteria of dry valleys [37].
IndexDry–HotDry–WarmDry–Temperate
Mean annual precipitation (mm)600–1000300–1100400–700
Mean temperature in coldest month (°C)>1212–55–0
Mean temperature in warmest month (°C)28–2424–2222–16
No. of days with daily mean temperature higher than 10 °C>350350–251250–151
Vegetation TypeSavanna, mesophyllous shrubSavanna, small-leaf deciduous shrubSmall leaf with thorns, deciduous shrub
Soil TypeXerothermicLateriteCinnamon
AgricultureTriple-croppingDouble-croppingDouble-cropping
Table
Table 2. Basic information of 20 main dry valleys in Southwest China.
Table 2. Basic information of 20 main dry valleys in Southwest China.
No.Climate1st River2nd River3rd RiverArea(km2)AAP (mm)STDP (mm)AAT (°C)STDT (°C)AE (m)STDE (m)AS (°)STDS (°)
1HotYuanMS-29101073.18127.4821.701.52841.97319.7423.0511.30
2HotYuanLuzhi-765963.34125.7220.651.471180.03279.0223.7010.48
3WarmYuanMS-194970.61167.2119.230.601546.05173.0824.2010.75
4HotLancangMS-2201032.99159.5620.310.671335.76196.1826.6312.62
5WarmLancangMS-7541023.08141.2715.071.692030.56370.0229.4712.02
6TemperateLancangMS-516789.88125.2410.851.722618.32375.2133.3211.52
7HotNuMS-16501117.13161.5321.121.241015.59272.7321.9612.79
8TemperateNuMS-537774.42130.8511.162.222545.07441.2636.9313.13
9HotYangtzeMS-11,279898.52111.2018.702.111425.51361.6122.0713.10
10WarmYangtzeMS-1730874.8193.1714.512.182111.39416.2529.9313.11
11TemperateYangtzeMS-3164704.9294.298.971.972976.21390.2931.2312.19
12TemperateYangtzeShuiluo-877794.5781.0010.652.082740.03419.8231.8312.17
13WarmYangtzeMinMS39942.06129.9813.051.111582.34318.1034.1813.51
14TemperateYangtzeMinMS1869849.53115.339.562.202384.37464.3932.8812.63
15WarmYangtzeMinDadu2241010.8571.3014.101.331753.41370.2731.1514.15
16TemperateYangtzeMinDadu1707823.8695.7210.421.902624.84394.4233.2413.08
17WarmYangtzeYalongMS1674892.4891.1114.362.221546.05434.6329.1612.21
18TemperateYangtzeYalongMS1580838.9170.0510.761.722647.48405.3334.4913.20
19WarmYangtzeYalongAnning1062959.51107.0816.071.361617.48155.5610.218.56
20TemperateYangtzeJialingBaishui693660.02104.638.852.042074.02447.9831.5812.60
Table
Table 3. Areas and proportions of land use types in dry valleys of Southwest China.
Table 3. Areas and proportions of land use types in dry valleys of Southwest China.
Land UseArea (km2)Proportion (%)
HotWarmTemperateHotWarmTemperate
Arable land3524.831161.61631.0521.0020.476.08
Orchard1222.5775.64240.117.281.332.31
Forest7953.733177.807251.5247.3855.9969.91
Grassland2496.14655.951799.7914.8711.5617.35
Construction sites842.25300.06227.725.025.292.19
Water bodies747.12304.15224.524.455.362.16
All16,786.645675.2110,374.71100.00100.00100.00
Table
Table 4. The long-term average annual FVC of 20 dry valleys in Southwest China.
Table 4. The long-term average annual FVC of 20 dry valleys in Southwest China.
No.River—ClimateFVCGradeNo.River—ClimateFVCGrade
1Yuan—Hot0.429medium11Jinsha—Temperate0.297low
2Yuan—Warm0.461medium12Shuiluo—Temperate0.560high
3Lvzhi—Hot0.366medium13Min—Warm0.508high
4Lancang—Hot0.455medium14Min—Temperate0.500high
5Lancang—Warm0.498high15Dadu—Warm0.520high
6Lancang—Temperate0.291low16Dadu—Temperate0.546high
7Nu—Hot0.551high17Yalong—Warm0.556high
8Nu—Temperate0.320low18Yalong—Temperate0.574high
9Jinsha—Hot0.429medium19Anning—Warm0.449medium
10Jinsha—Warm0.495high20Baishui—Temperate0.513high
Table
Table 5. Annual FVC dynamics and relationships with precipitation in dry valleys of Southwest China.
Table 5. Annual FVC dynamics and relationships with precipitation in dry valleys of Southwest China.
No.River—ClimateTemporal Trends of FVCRelationship between Precipitation and FVC
Slope (×10−4)InterceptSlope (×10−4)Intercept
/Dry−3.5 *1.15 *18.20.44
/Dry–Hot−9.1 *2.26 *65.2 *0.64 *
/Dry–Warm−5.01.50−39.50.54
/Dry–Temperate4.8−0.5386.10.38
1Yuan—Hot−22.4 *4.92 *1.1 *0.31 *
2Luzhi—Hot−44.2 *9.26 *1.7 *0.20 *
3Yuan—Warm−32.3 *6.94 *1.3 *0.33 *
4Lancang—Hot−55.3 *11.57 *1.7 *0.28 *
5Lancang—Warm24.6 *4.45 *13.20.51
6Lancang—Temperate−12.62.8376.2 *0.23 *
7Nu—Hot−1.30.8139.60.51
8Nu—Temperate−3.816.7196.00.25
9Jinsha—Hot−2.10.8554.2 *0.38 *
10Jinsha—Warm−15.9 *3.70 *−14.80.51
11Jinsha—Temperate−1.70.6454.10.26
12Shuiluo—Temperate−27.7 *6.14 *−49.90.60
13Min—Warm−20.34.58322.10.51
14Min—Temperate35.1 *−6.55 *1.6 *0.36 *
15Dadu—Warm−12.83.10−873.20.53
16Dadu—Temperate13.8−2.231.5 *0.43 *
17Yalong—Warm7.5−0.96−46.30.60
18Yalong—Temperate−8.22.21−30.90.60
19Anning—Warm−20.3 *4.53 *−74.50.52
20Baishui—Temperate42.3 *−7.99 *2.1 *0.37 *
Note: * mean difference is significant at 0.05 level.
Table
Table 6. Information regarding hydro-power projects in dry valleys of Southwest China.
Table 6. Information regarding hydro-power projects in dry valleys of Southwest China.
No.River—ClimateProjectConstruction PeriodNo.River—ClimateProjectConstruction Period
1Yuan—HotDawan2012–201514Min—TemperateTianlonghu2001–2004
Madushan2007–2011Jinlongtan2003–2006
4Lancang—HotXiaowan2002–2012Jiyu2003–2007
10Jinsha—WarmLiyuan2008–2016Jiangsheba1998–2005
Ahai2008–2014Futang2001–2004
Jin’anqiao2003–2011Maoergai2009–2013
Longkaikou2007–2014Seergu2009–2013
5Lancang—WarmWunonglong2015–2019Liuping2004–2009
Lidi2014–2019Baixi2004–2008
Huangdeng2008–2018Shiziping2004–2010
Dahuaqiao2010–201920Baishui—TemperateDuonuo2009–2012
Miaowei2014–2020Yawa2014–2017
12Shuiluo—TemperateGudi2013–2018Lingjiang2014–2017
Xinzang2014–2020HeiheTang2004–2006
Bowa2015–2020Baishuihe2014–2018
Ninglang2008–2012Shuanghe2008–2009
Saduo2010–2014Qinglong2007–2011
Shiji2009–2014
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Guo, Q.; Zhong, R.; Shan, Z.; Duan, X. Vegetation Cover Variation in Dry Valleys of Southwest China: The Role of Precipitation. Remote Sens. 2023, 15, 1727. https://doi.org/10.3390/rs15071727

AMA Style

Guo Q, Zhong R, Shan Z, Duan X. Vegetation Cover Variation in Dry Valleys of Southwest China: The Role of Precipitation. Remote Sensing. 2023; 15(7):1727. https://doi.org/10.3390/rs15071727

Chicago/Turabian Style

Guo, Qiankun, Ronghua Zhong, Zhijie Shan, and Xingwu Duan. 2023. "Vegetation Cover Variation in Dry Valleys of Southwest China: The Role of Precipitation" Remote Sensing 15, no. 7: 1727. https://doi.org/10.3390/rs15071727

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop