Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China

Huang, Sen-wang; Jiming, Kong

doi:10.3390/ijgi5090158

Cited by 54 publications

(44 citation statements)

References 59 publications

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“…Also, another researcher founded that the yearly aggregation of time series for trend analysis reduced by the temporal resolution and time series length. Therefore, the time series scale is analytical in identifying the significance of the trend in the non-parametrical statistical test Huang et al 2016). The land degradation map produced by the Image processing and Geographic information system.…”

Section: Methodsmentioning

confidence: 99%

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Nasanbat¹,

Sharav²,

Sanjaa³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This study examines MODIS NDVI satellite imagery time series can be used to determine hotspot of land degradation area in whole Mongolia. The trend statistical analysis of Mann-Kendall was applied to a 16-year MODIS NDVI satellite imagery record, based on 16-day composited temporal data (from May to September) for growing seasons and from 2000 to 2016. We performed to frequency analysis that resulting NDVI residual trend pattern would enable successful determined of negative and positive changes in photo synthetically health vegetation. Our result showed that negative and positive values and generated a map of significant trends. Also, we examined long-term of meteorological parameters for the same period. The result showed positive and negative NDVI trends concurred with land cover types change representing an improve or a degrade in vegetation, respectively. Also, integrated the climate parameters which were precipitation and air temperature changes in the same time period seem to have had an affecting on huge NDVI trend area. The time series trend analysis approach applied successfully determined hotspot of an improvement and a degraded area due to land degradation and desertification.

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Section: Methodsmentioning

confidence: 99%

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Nasanbat¹,

Sharav²,

Sanjaa³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Over this 11‐year period, the correlation coefficient for annual above‐ground total NPP was 0.765. As NDVI is strongly correlated with NPP (Huang & Kong, ; Vlek, Le, & Tamene, ), it represents a useful tool with which to couple climate and vegetation performance at large spatial and temporal scales (Pettorelli et al, ). Although NDVI can serve as an indicator of NPP to measure temporal changes in vegetation and as a proxy for land degradation, it is important to note that it does not tell us anything about the kind of degradation or regeneration processes (Bai, Dent, Olsson, & Schaepman, ).…”

Section: Introductionmentioning

confidence: 99%

“…The longest continuous record of NDVI data comes from the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellite series 7, 9, 11, 14, 16, and 17, starting in July 1981 (Pettorelli et al, ). Recent studies in diverse regions of the world in which the GIMMS NDVI time series data from the AVHRR instrument has been used to detect changes in photosynthetically active vegetation reveal diverse patterns of decline and increase in vegetation productivity (Erasmi, Schucknecht, Barbosa, & Matschullat, ; Huang & Kong, ; Ibrahim, Balzter, Kaduk, & Tucker, ; Vu, Le, & Vlek, ). In these studies, various methods were used to aggregate the NDVI data, including annual mean NDVI (Ibrahim et al, ; Vu et al, ), annual sum of NDVI (Erasmi et al, ), and seasonal sums of NDVI (Huang & Kong, ).…”

Section: Introductionmentioning

confidence: 99%

Establishing a land degradation neutrality national baseline through trend analysis of GIMMS NDVI Time‐series

Gichenje

Godinho

2018

Land Degrad Dev

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The land degradation-neutrality (LDN) national baseline for Kenya in 2015 was established in terms of the three LDN indicators (land cover, land productivity, and carbon stocks), and using trends in GIMMS NDVI and land cover datasets over the 24-year period from 1992 to 2015. Human-induced land degradation was separated from degradation driven by climate factors using soil moisture data and the residual trend method. On the basis of Kendall's tau of the NDVI residuals computed using annual mean data of the NDVI and soil moisture relationship, the country has experienced persistent negative trends (browning) over 21.6% of the country, and persistent positive trends (greening) in 8.9% of the country. The land cover change map for the period 1992-2015 showed that in 5.6% of the area there was a change from one land cover class to another. Pronounced changes in terms of land area were the increase in grasslands by 12,171 km 2 , the decrease of bare land by 9,877 km 2 , and the decrease in forests by 7,182 km 2 . Browning and greening trends account for 13% and 12%, respectively, of the land cover change areas. By establishing the LDN national baseline, the LDN concept is now operational. As a first step, targeted field level assessments, alongside the collection of data for the computation of soil organic carbon stocks, should be undertaken in selected browning, greening, and land cover change sites. These field studies will provide decision makers with key information on how to plan for the implementation and monitoring of LDN interventions.

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“…This approach is one of the most reliable trend analysis methods that can be utilized to disentangle the effects of climate from human-accelerated land degradation [40], when assuming that the climatic factor is the only driver in NDVI change [21]. It is well known that changes in vegetation are strongly influenced by climatic factors and human activities such as ecological restoration practices (seasonal cropping patterns, tree plantation and reducing deforestation); of these, temperature and precipitation exert a major influence but if significant climatic effects can be removed from long-term trends in the NDVI, land degradation induced by human activities can be distinguished more effectively within a study area [41,42].…”

Section: The Restrend Analysismentioning

confidence: 99%

“…A range of previous studies have applied the RESTREND method to quantitatively determine how human activities have influenced vegetation growth [21,22,31,[40][41][42][43]. He et al (2015) [21] had cleared that this method is highly applicable in semi-arid and semi-humid regions, when assuming that both temperature and precipitation influence a change in the NDVI; thus, this method was selected to isolate the contributions of precipitation and temperature to vegetation change from those due to human influence.…”

Section: The Restrend Analysismentioning

confidence: 99%

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

et al. 2017

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Vegetation dynamics in relation to climatic changes and anthropogenic activities is critical for terrestrial ecosystem management. The objective of this study was to investigate spatiotemporal change of vegetation and their driving forces during growing seasons (between April and October and including the spring, summer and autumn) in the Tumen River Basin (TRB) using Normalized Difference Vegetation Index (NDVI) and climate data spanning from 2000 to 2015. A linear regression, Pearson correlation coefficients and the residual trend (RESTREND) was applied for this study. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years; those in growing season (spring, summer, and autumn) were characterized by the increase in rates by 0.0012/year, 0.0022/year, 0.0011/year, and 0.0019/year, respectively. Forested regions are characterized by the largest increase (0.0021/year) in NDVI compared with other vegetation types across the entire study area. The trends in NDVI across the study area were influenced by both climatic variations and human disturbances. The human activities such as reforestation and agricultural practices are the primary driver, greater than climatic factors, during growing season, including summer and autumn. Temperature and precipitation has had a significant influence on NDVI in a limited area (temp = 0.86%, p < 0.05 and precipitation = 1.93%, p < 0.05) during growing season. The significant role of precipitation on NDVI change throughout growing season and the summer is larger than that of temperature across the TRB, although the influence of the latter becomes most significant during the spring and autumn. The RESTREND method shows that human activity during the growing season, including the spring, summer, and autumn, have led to enhancements in NDVI across more than 70% of the TRB over the last 16 years, with the most significant improvements seen in forested land and farmland. At the same time, a significant reduction in residual (i.e., degraded areas) NDVI values for different growing seasons had characterized farmland and urban land at low altitudes. This study provides important background information regarding the influence of human activities on land degradation and provides a scientific foundation for the development of ecological restoration policies within the TRB. We found that the RESTREND method can be used to detect human drivers of vegetation in the regions with semi-humid and humid monsoon, where the significant correlation between NDVI and climatic factors exists.

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Assessing Land Degradation Dynamics and Distinguishing Human-Induced Changes from Climate Factors in the Three-North Shelter Forest Region of China

Cited by 54 publications

References 59 publications

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Frequency Analysis of Modis Ndvi Time Series for Determining Hotspot of Land Degradation in Mongolia

Establishing a land degradation neutrality national baseline through trend analysis of GIMMS NDVI Time‐series

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

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