Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

He, Dong; Yi, Guihua; Zhang, Tingbin; Miao, Jianjun; Li, Jingji; Bie, Xiaojuan

doi:10.3390/rs10060961

Cited by 33 publications

(24 citation statements)

References 58 publications

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“…The area exhibited widespread overall increasing trends in both the EVI and LAI. This widespread greening has also been reported in previous studies, e.g., [ 15 , 29 , 37 , 50 , 51 ]. Changes in the EVI and LAI were mainly influenced by factors other than climate for more than half of the grasslands and rainfed croplands.…”

Section: Discussionsupporting

confidence: 88%

Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Ding

Huang

et al. 2020

Sensors

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Knowledge of the dynamics of dryland vegetation in recent years is essential for combating desertification. Here, we aimed to characterize nonlinear changes in dryland vegetation greenness over East Inner Mongolia, an ecotone of forest–grassland–cropland in northern China, with time series of Moderate-resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and GEOV2 leaf area index (LAI) values during 2000 to 2016. Changes in the growing season EVI and LAI were detected with the polynomial change fitting method. This method characterizes nonlinear changes in time series by polynomial fitting with the highest polynomial order of three, and simultaneously provides an estimation of monotonic trends over the time series by linear fitting. The relative contribution of climatic factors (precipitation and temperature) to changes in the EVI and LAI were analyzed using linear regression. In general, we observed similar patterns of change in the EVI and LAI. Nonlinear changes in the EVI were detected for about 21% of the region, and for the LAI, the percentage of nonlinear changes was about 16%. The major types of nonlinear changes include decrease–increase, decrease–increase–decrease, and increase–decrease–increase changes. For the overall monotonic trends, very small percentages of decrease (less than 1%) and widespread increases in the EVI and LAI were detected. Furthermore, large areas where the effects of climate variation on vegetation changes were not significant were observed for all major types of change in the grasslands and rainfed croplands. Changes with an increase–decrease–increase process had large percentages of non-significant effects of climate. The further analysis of increase–decrease–increase changes in different regions suggest that the increasing phases were likely to be mainly driven by human activities, and droughts induced the decreasing phase. In particular, some increase–decrease changes were observed around the large patch of bare areas. This may be an early signal of degradation, to which more attention needs to be paid to combat desertification.

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Section: Discussionsupporting

confidence: 88%

Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Ding

Huang

et al. 2020

Sensors

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“…For example, around May, August, and September when the ground vegetation reached their maximum, FY-3C had a relatively large RMSE and ubRMSE, and a small R. We can also find that the AMSR2 statistical parameters shared a parallel trend with FY-3C in Table 7. The FY-3C and AMSR2 (JAXA algorithm) soil moisture products were all retrieved using the X-band brightness temperature [52], which may partly explain their similar performance. The SMAP statistical parameters did not show a similar seasonal variation like FY-3C and AMSR2.…”

Section: Spatial Performance At Different Timesmentioning

confidence: 99%

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Zhu

Pearson

et al. 2019

Water

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Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (<0.3 kg/m2) than a high VWC condition (>0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.

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“…Hence, depending on land management decisions, the size and degree of vegetation degradation can be different. Second, the response of vegetation can differ depending on the vegetation type (e.g., coniferous or deciduous) and elevation gradient [27,28]. Some vegetation types are vulnerable to climate change while others can respond positively, such as those used for firewood.…”

Section: Introductionmentioning

confidence: 99%

“…This study aimed to identify the differences in impact for vegetation state between climate and human factors, as well as to reveal vulnerable vegetation types and required management actions considering elevation gradients. To measure changes in the health and growth of vegetation, the enhanced vegetation index (EVI), a satellite-based vegetation greenness index, was used, since it is one of the most important data indices in ecosystems research and reflects the growth and status of surface vegetation [28].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Critical Impact Path on Vegetation Growth under Climate Extremes and Human Influence

Kim

Song

Lee

2019

Forests

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Reduced vegetation growth ultimately induces degradation of the ecosystem and CO 2 sequestration. Multiple risks can affect vegetation, but climate change and human influence have been particularly known to be major risks for deteriorating the ecosystem. However, there is limited information illustrating comprehensive impact pathways that consider both climatic and human impacts on vegetation. To promote optimum decision-making, information is required to elucidate complex cause-and-effect pathways in order to determine how various impacts are related and which ones are more important. Hence, we identified impact pathways affecting enhanced vegetation index (EVI) regarding climate and human factors by revealing a causal network using the Bayesian network approach. Vulnerable vegetation types and the spatial range of impact were evaluated based on the identified network by analyzing temporal changes in annual average EVI, human-induced land conversion, and multiple climate extremes from 2002 to 2014 on Jeju Island, South Korea. The results indicated the high vulnerability of coniferous forests compared with mixed and deciduous forests were able to elucidate the major impact paths, including human-induced land conversion at lower elevation, length of frost, degree of heat, and general intensity of wetness (Pearson's r = 0.58). Existing policies in the study site have been insufficient to avoid the major paths influencing vegetation state. This study offers insights into comprehensive impact paths in order to support effective decision-making for nature conservation.

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Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

Cited by 33 publications

References 58 publications

Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Nonlinear Changes in Dryland Vegetation Greenness over East Inner Mongolia, China, in Recent Years from Satellite Time Series

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Understanding the Critical Impact Path on Vegetation Growth under Climate Extremes and Human Influence

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