scholarly journals Vegetation Change Analyses Considering Climate Variables and Anthropogenic Variables in the Three-River Headwaters Region

10.29007/qw2v ◽  
2018 ◽  
Author(s):  
Chen Chen ◽  
Tiejian Li ◽  
Jiaye Li ◽  
Wang Fu ◽  
Guangqian Wang
Keyword(s):  
Cloud Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climate Variables ◽  
Population Activity ◽  
Vegetation Growth ◽  
Vegetation Activity ◽  
Activity Temperature ◽  
Multiple Variables

In the terrestrial biosphere, vegetation plays vital roles in providing food and habitats for humankind and animals. In general, vegetation activity is influenced by both climate drivers and anthropogenic drivers, and studies have tried to disentangle contributions of these multiple variables from each other. However, it remains largely unclear how climatic and anthropogenic effects work together to impact on vegetation dynamics. In this study, we analyzed the vegetation change from 1995 to 2014 in the Three-River Headwaters Region (TRHR) using Normalized Difference Vegetation Index (NDVI). We applied partial correlation analyses to discriminate the contributions of climate variables and anthropogenic variables. The result indicates that the TRHR experiences a slightly greening trend from 1995 to 2014. The primary climatic driving factor is temperature for the southeast and south parts of the TRHR, precipitation in the west part, and a combination of precipitation, temperature and cloud cover for northeast part. The interaction between precipitation and cloud cover, precipitation and grazing activity, temperature and population activity, contribute to vegetation growth. The relationship between vegetation activity and the driving factors are evolving towards the direction which vegetation favors for the past two decades.

scholarly journals Vegetation Dynamic Changes and Their Response to Ecological Engineering in the Sanjiangyuan Region of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4035
Author(s):  
Xiaohui Zhai ◽  
Xiaolei Liang ◽  
Changzhen Yan ◽  
Xuegang Xing ◽  
Haowei Jia ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Positive Impact ◽  
Ecological Engineering ◽  
Scientific Basis ◽  
Vegetation Dynamic ◽  
Vegetation Growth ◽  
The Impact

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

scholarly journals Contrasting Changes in Vegetation Growth due to Different Climate Forcings over the Last Three Decades in the Selenga-Baikal Basin

2019 ◽  
Vol 11 (4) ◽  
pp. 426 ◽  
Author(s):  
Guan Wang ◽  
Ping Wang ◽  
Tian-Ye Wang ◽  
Yi-Chi Zhang ◽  
Jing-Jie Yu ◽  
...  
Keyword(s):  
Human Activities ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mean Annual Precipitation ◽  
Limiting Factor ◽  
Substantial Impact ◽  
Vegetation Growth ◽  
Baikal Basin ◽  
Global Rate

The Selenga-Baikal Basin, a transboundary river basin between Mongolia and Russia, warmed at nearly twice the global rate and experienced enhanced human activities in recent decades. To understand the vegetation response to climate change, the dynamic spatial-temporal characteristics of the vegetation and the relationships between the vegetation dynamics and climate variability in the Selenga-Baikal Basin were investigated using the Normalized Difference Vegetation Index (NDVI) and gridded temperature and precipitation data for the period of 1982 to 2015. Our results indicated that precipitation played a key role in vegetation growth across regions that presented multiyear mean annual precipitation lower than 350 mm, although its importance became less apparent over regions with precipitation exceeding 350 mm. Because of the overall temperature-limited conditions, temperature had a more substantial impact on vegetation growth than precipitation. Generally, an increasing trend was observed in the growth of forest vegetation, which is heavily dependent on temperature, whereas a decreasing trend was detected for grassland, for which the predominant growth-limiting factor is precipitation. Additionally, human activities, such as urbanization, mining, increased wildfires, illegal logging, and livestock overgrazing are important factors driving vegetation change.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Remote sensing assessment of glacial loss and vegetation change from 1989 to 2016: Pond Inlet, Nunavut

2021 ◽  
Author(s):  
Ruby R. Pennell
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Relevant Literature ◽  
Arctic Tundra ◽  
The Arctic ◽  
Landscape Changes ◽  
Storm Events ◽  
Time Period ◽  
Induced Changes

The climate change phenomenon occurring across the globe is having an increasingly alarming effect on Canada’s Arctic. Warming temperatures can have wide spanning impacts ranging from more rain and storm events, to increasing runoff, thawing permafrost, sea ice decline, melting glaciers, ecosystem disruption, and more. The purpose of this MRP was to assess the climate-induced landscape changes, including glacial loss and vegetation change, in Pond Inlet, Nunavut. A time series analysis was performed using the intervals 1989-1997, 1997-2005, and 2005-2016. The two methods for monitoring change were 1) the Normalized Difference Snow Index (NDSI) to detect glacial change, and 2) the Normalized Difference Vegetation Index (NDVI) to detect vegetation change, both utilizing threshold and masking techniques to increase accuracy. It was found that the percent of glacial loss and vegetation change in Pond Inlet had consistently increased throughout each time period. The area of glacial loss grew through each period to a maximum of 376 km2 of glacial loss in the last decade. Similarly, the area of the Arctic tundra that experienced vegetation change increased in each time period to a maximum of 660 km2 in the last decade. This vegetation change was characterized by overall increasing values of NDVI, revealing that many sections of the Arctic tundra in Pond Inlet were increasing in biomass. However, case study analysis revealed pixel clustering around the lower vegetation class thresholds used to classify change, indicating that shifts between these vegetation classes were likely exaggerated. Shifts between the higher vegetation classes were significant, and were what contributed to the most change in the last decade. The observations of higher glacial melt and increases in biomass are occurring in parallel with the increasing temperatures in Pond Inlet. Relevant literature in the Arctic agrees with the findings of this MRP that there are significant trends of glacial loss and vegetation greening and many studies attribute this directly to climate warming. The results of this study provide the necessary background with regards to landscape changes which could be used in future field studies investigating the climate induced changes in Pond Inlet. This study also demonstrates that significant landscape modifications have occurred in the recent decades and there is a strong need for continued research and monitoring of climate induced changes.

scholarly journals Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 162 ◽  
Author(s):  
Xiang Li ◽  
Yuying Lin
Keyword(s):  
High Voltage ◽  
Transmission Lines ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Power Transmission ◽  
Mesh Size ◽  
Forest Landscape ◽  
Power Transmission Lines ◽  
Vegetation Growth ◽  
Landscape Types

The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are uncertain. Taking one of the areas with the highest forest coverage in China as an example, the upper reaches of the Minjiang River in Fujian Province, we quantitatively analyzed the effect of HVPTLs on forest landscape fragmentation and vegetation growth using Landsat imageries and forest inventory datasets. The results revealed that 0.9% of the forests became edge habitats assuming a 150 m depth-of-edge-influence by HVPTLs, and the forest plantations were the most exposed to HVPTLs among all the forest landscape types. Habitat fragmentation was the main consequence of HVPTL installation, which can be reduced by an increase in the patch density and a decrease in the mean patch area (MA), largest patch index (LPI), and effective mesh size (MESH). In all the landscape types, the forest plantation and the non-forest land were most affected by HVPTLs, with the LPI values decreasing by 44.1 and 20.8%, respectively. The values of MESH decreased by 44.2 and 32.2%, respectively. We found an obvious increasing trend in the values of the normalized difference vegetation index (NDVI) in 2016 and NDVI growth during the period of 2007 to 2016 with an increase in the distance from HVPTL. The turning points of stability were 60 to 90 meters for HVPTL corridors and 90 to 150 meters for HVPTL pylons, which indicates that the pylons have a much greater impact on NDVI and its growth than the lines. Our research provides valuable suggestions for vegetation protection, restoration, and wildfire management after the construction of HVPTLs.

scholarly journals Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

2020 ◽  
Vol 12 (10) ◽  
pp. 1546 ◽  
Author(s):  
Christopher Potter ◽  
Olivia Alexander
Keyword(s):  
Time Series ◽  
Coastal Plain ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Vegetation Growth ◽  
The Past ◽  
Moderate Resolution Imaging Spectroradiometer

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

scholarly journals A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015

2019 ◽  
Vol 19 (6) ◽  
pp. 1189-1213 ◽  
Author(s):  
Sergio M. Vicente-Serrano ◽  
Cesar Azorin-Molina ◽  
Marina Peña-Gallardo ◽  
Miquel Tomas-Burguera ◽  
Fernando Domínguez-Castro ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Forest Growth ◽  
Dominant Factor ◽  
Drought Severity ◽  
Vegetation Types ◽  
Vegetation Activity

Abstract. Drought is a major driver of vegetation activity in Spain, with significant impacts on crop yield, forest growth, and the occurrence of forest fires. Nonetheless, the sensitivity of vegetation to drought conditions differs largely amongst vegetation types and climates. We used a high-resolution (1.1 km) spatial dataset of the normalized difference vegetation index (NDVI) for the whole of Spain spanning the period from 1981 to 2015, combined with a dataset of the standardized precipitation evapotranspiration index (SPEI) to assess the sensitivity of vegetation types to drought across Spain. Specifically, this study explores the drought timescales at which vegetation activity shows its highest response to drought severity at different moments of the year. Results demonstrate that – over large areas of Spain – vegetation activity is controlled largely by the interannual variability of drought. More than 90 % of the land areas exhibited statistically significant positive correlations between the NDVI and the SPEI during dry summers (JJA). Nevertheless, there are some considerable spatio-temporal variations, which can be linked to differences in land cover and aridity conditions. In comparison to other climatic regions across Spain, results indicate that vegetation types located in arid regions showed the strongest response to drought. Importantly, this study stresses that the timescale at which drought is assessed is a dominant factor in understanding the different responses of vegetation activity to drought.

scholarly journals Detecting Patterns of Vegetation Gradual Changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework

2019 ◽  
Vol 11 (21) ◽  
pp. 2475 ◽  
Author(s):  
Ju Wang ◽  
Yaowen Xie ◽  
Xiaoyun Wang ◽  
Jingru Dong ◽  
Qiang Bie
Keyword(s):  
River Basin ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Logistic Function ◽  
Linear Pattern ◽  
Shiyang River Basin ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Consistency ◽  
Almost All

A lot of timeseries satellite products have been well documented in exploring changes in ecosystems. However, algorithms allowing for measuring the directions, magnitudes, and timing of vegetation change, evaluating the major driving factors, and eventually predicting the future trends are still insufficient. A novel framework focusing on addressing this problem was proposed in this study according to the temporal trajectory of Normalized Difference Vegetation Index (NDVI) timeseries of Moderate Resolution Imaging Spectroradiometer (MODIS). It divided the inter-annual changes in vegetation into four patterns: linear, exponential, logarithmic, and logistic. All the three non-linear patterns were differentiated automatically by fitting a logistic function with prolonged NDVI timeseries. Finally, features of vegetation changes including where, when and how, were evaluated by the parameters in the logistic function. Our results showed that 87.39% of vegetation covered areas (maximum mean growing season NDVI in the 17 years not less than 0.2) in the Shiyng River basin experienced significant changes during 2001–2017. The linear pattern, exponential pattern, logarithmic pattern, and logistic pattern accounted for 36.53%, 20.16%, 15.42%, and 15.27%, respectively. Increasing trends were dominant in all the patterns. The spatial distribution in both the patterns and the transition years at which vegetation gains/losses began or ended is of high consistency. The main years of transition for the exponential increasing pattern, the logarithmic increasing pattern, and the logarithmic increasing pattern were 2008–2011, 2003–2004, and 2009–2010, respectively. The period of 2006–2008 was the foremost period that NDVIs started to decline in Liangzhou Oasis and Minqin Oasis where almost all the decreasing patterns were concentrated. Potential disturbances of vegetation gradual changes in the basin are refer to as urbanization, expansion or reduction of agricultural oases, as well as measures in ecological projects, such as greenhouses building, afforestation, grazing prohibition, etc.

scholarly journals An Analysis of Vegetation Change Trends and Their Causes in Inner Mongolia, China from 1982 to 2006

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Baolin Li ◽  
Wanli Yu ◽  
Juan Wang
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Anthropogenic Factors ◽  
Simple Linear Regression ◽  
Ordos Plateau ◽  
Hetao Plain ◽  
Avhrr Data ◽  
The Relationship ◽  
Very High

This paper presents the vegetation change trends and their causes in the Inner Mongolian Autonomous Region, China from 1982 to 2006. We used National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to determine the vegetation change trends based on regression model by fitting simple linear regression through the time series of the integrated Normalized Difference Vegetation Index (NDVI) in the growing season for each pixel and calculating the slopes. We also explored the relationship between vegetation change trends and climatic and anthropogenic factors. This paper indicated that a large portion of the study area (17%) had experienced a significant vegetation increase at the 0.05 level from 1982 to 2006. The significant vegetation increase showed no positive link with precipitation and was mainly caused by human activities. In or to the south of Horqin Sandy Land, in the Hetao Plain, and at the northern foothills of the YinshanMountain, the significant NDVI increase trends were mainly caused by the increase of the millet yield per unit of cropland. In the east of Ordos Plateau, the significant NDVI increase trends were mainly determined by the fencing and planting of grasses and trees on grassland.

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