Forest cover dynamics from Landsat time-series data over Yan’an city on the Loess Plateau during the Grain for Green Project

An unprecedented reforestation process happened in the Loess Plateau, China due to the ecological restoration project ‘Grain for Green Project’, which has affected regional carbon and water cycles as well as brought climate feedbacks. Accurately mapping the area and spatial distribution of emerged forests in the Loess Plateau over time is essential for forest management but a very challenging task. Here we investigated the changes of forests in the Loess Plateau after the forest reconstruction project. First, we used a pixel and rule-based algorithm to identify and map the annual forests from 2007 to 2017 in the Loess Plateau by integrating 30 m Landsat data and 25 m resolution PALSAR data in this study. Then, we carried out the accuracy assessment and comparison with several existing forest products. The overall accuracy (OA) and Kappa coefficient of the resultant map, were about 91% and 0.77 in 2010, higher than those of the other forest products (FROM-GLC, GlobeLand30, GLCF-VCF, JAXA, and OU-FDL) with OA ranging from 83.57% to 87.96% and Kappa coefficients from 0.52 to 0.68. Based on the annual forest maps, we found forest area in the Loess Plateau has increased by around 15,000 km2 from 2007 to 2017. This study clearly demonstrates the advantages of data fusion between PALSAR and Landsat images for monitoring forest cover dynamics in the Loess Plateau, and the resultant forest maps with lower uncertainty would contribute to the regional forest management.

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Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3064580 ◽

2021 ◽

Vol 14 ◽

pp. 3373-3385

Author(s):

Taixia Wu ◽

Yuting Zhao ◽

Shudong Wang ◽

Hongjun Su ◽

Yingying Yang ◽

...

Keyword(s):

Time Series ◽

Forest Cover ◽

Time Series Data ◽

Evergreen Forest ◽

Series Data ◽

Landsat 8 ◽

Sentinel 2

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Spatiotemporal Variation of Vegetation Coverage and its Response to Climate Change Before and After Implementation of Grain for Green Project in the Loess Plateau

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8898716 ◽

2019 ◽

Author(s):

Rui Sun ◽

Shaohui Chen ◽

Hongbo Su ◽

Guibin Hao

Keyword(s):

Climate Change ◽

Loess Plateau ◽

Spatiotemporal Variation ◽

Vegetation Coverage ◽

The Loess Plateau ◽

Grain For Green ◽

Before And After ◽

Grain For Green Project

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Spatio-temporal detection of converting from cropland to forest/grassland on the Loess Plateau using all available Landsat time-series images

10.5194/egusphere-egu21-1903 ◽

2021 ◽

Author(s):

Zhihui Wang ◽

Peiqing Xiao

Keyword(s):

Time Series ◽

Change Detection ◽

Loess Plateau ◽

Water Conservation ◽

Series Data ◽

Vegetation Types ◽

Continuous Change ◽

The Loess Plateau ◽

Ndvi Time Series ◽

Spatio Temporal

<p><strong>Conversion of cropland to forest/grassland has become a key ecological restoration measure on the Loess Plateau since 1999. Accurate mapping of the spatio-temporal dynamic information of conversion from cropland into forest/grassland is necessary for studying the effects of vegetation change on hydro-ecological process and soil and water conservation on the Loess Plateau, China. Currently, the accuracy of change detection of farmland and forest/grassland at 30-m scale in this area is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, an innovative method for continuous change detection of cropland and forest/grassland using all available Landsat time-series data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% &#177; 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation.</strong></p>

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Estimation of Actual Evapotranspiration in a Semiarid Region Based on GRACE Gravity Satellite Data—A Case Study in Loess Plateau

Remote Sensing ◽

10.3390/rs10122032 ◽

2018 ◽

Vol 10 (12) ◽

pp. 2032 ◽

Cited By ~ 2

Author(s):

Miao Sun ◽

Qin’ge Dong ◽

Mengyan Jiao ◽

Xining Zhao ◽

Xuerui Gao ◽

...

Keyword(s):

Soil Water ◽

Loess Plateau ◽

Water Consumption ◽

Satellite Data ◽

Potential Evapotranspiration ◽

Sustainable Growth ◽

Actual Evapotranspiration ◽

The Loess Plateau ◽

Grain For Green ◽

Grain For Green Project

Jointly influenced by natural factors and artificial protection measures in recent years, the vegetation coverage of the Loess Plateau has significantly increased. However, extensive vegetation recovery can result in massive water consumption and a severe soil water deficit, which poses a great threat to the sustainable development of the regional ecological system. Maintaining the balance between precipitation and water consumption is an important foundation of ecological security in the Loess Plateau. Based on this, the present study used the GRACE (Gravity Recovery and Climate Experiment) gravity satellite data to simulate the annual actual water consumption from 2003 to 2014 and to analyze the temporal and spatial evolution of the regional precipitation and the actual evapotranspiration (AET). This study also applied the newly developed rainwater utilization potential index (IRUP) to quantify the sustainability of the water balance in the Loess Plateau. The spatial-temporal patterns of precipitation, potential evapotranspiration, and AET from 2003 to 2014 in the Loess Plateau were all analyzed in this study. Based on the results, the annual average precipitation (AAP) and AET in the entire Loess Plateau had significant increasing trends. The analysis of the spatial distribution reveals that the AET was decreasing from the southeast to the northwest in the Loess Plateau. However, the average values of potential evapotranspiration did not obviously change. Based on the estimated AET result, it was determined that the average IRUP had an increasing trend. The increase in the IRUP is due to an increased rate of precipitation that is statistically higher than that of the AET. Consequently, the Loess Plateau experienced a wetting trend during the period of 2003–2014, especially after the Grain for Green project was implemented. The results in this paper were proven by using three different depths of ERA-Interim (a global atmospheric reanalysis product created by the European Centre for Medium-Range Weather Forecasts) soil water content data from the same period and the observed runoff data from 18 different hydrological sites. Consequently, it seems that the vegetation could maintain a sustainable growth with the implementation of the Grain for Green Project.

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The Grain for Green Project induced land cover change in the Loess Plateau: A case study with Ansai County, Shanxi Province, China

Ecological Indicators ◽

10.1016/j.ecolind.2012.03.021 ◽

2012 ◽

Vol 23 ◽

pp. 88-94 ◽

Cited By ~ 112

Author(s):

Decheng Zhou ◽

Shuqing Zhao ◽

Chao Zhu

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Loess Plateau ◽

Shanxi Province ◽

The Loess Plateau ◽

Grain For Green ◽

Grain For Green Project

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Feasibility Study of C- and L-band SAR Time Series Data in Tracking Indonesian Plantation and Natural Forest Cover Changes

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2015.2400439 ◽

2015 ◽

Vol 8 (7) ◽

pp. 3692-3699 ◽

Cited By ~ 12

Author(s):

Xichao Dong ◽

Shaun Quegan ◽

Uryu Yumiko ◽

Cheng Hu ◽

Tao Zeng

Keyword(s):

Time Series ◽

Feasibility Study ◽

Forest Cover ◽

Time Series Data ◽

Natural Forest ◽

Series Data ◽

Forest Cover Changes ◽

L Band

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Evolution Characteristics and Simulation Prediction of Forest and Grass Landscape Fragmentation Based on the “Grain for Green” Projects on the Loess Plateau, P.R. China

10.21203/rs.3.rs-138854/v1 ◽

2021 ◽

Author(s):

Li Gu ◽

Zhiwen Gong ◽

Yuankun Bu

Keyword(s):

Climate Change ◽

Land Use ◽

Loess Plateau ◽

Global Climate Change ◽

Global Climate ◽

Landscape Fragmentation ◽

The Loess Plateau ◽

Grain For Green ◽

Grain For Green Project ◽

The Impact

Abstract Forest fragmentation is one of the major environmental issues that the international community is generally concerned about under the background of global climate change. Studying the impact and the interaction mechanism of land use change processes on landscape fragmentation is important to gaining a comprehensive understanding of the ecosystem response to human activities and global climate change. Based on the implementation background for the “Grain for Green” Project, we selected the Loess Plateau as the research area and used the coupled future land use simulation (FLUS) model and landscape fragmentation model to explore the temporal and spatial changes in forest and grass landscape fragmentation. The results showed that (1) Woodland, grassland, and cropland are the main landscape types, accounting for about 90% of the total area. In addition, the area of cropland initially increased and then decreased, while the area of woodland and grassland exhibited the opposite trend Oover the last 35 years. In particular, the period from 2000 to 2015 was a forest and grass restoration stage, and the average annual rate of forest and grass restoration reached 0.56%. (2) The FLUS model was used to predict the land use on the Loess Plateau in 2030. The kappa coefficient was 0.85, and the figure of merit coefficient (FOM) was 0.11 for a 1% random sampling, which are within a reasonable range, and the simulation results are also consistent with the objective change in the current social and economic development. (3) The fragmentation of woodland and grassland were dominated by edge type and core type. The core type had a concentrated distribution and an absolute advantage, accounting for more than 75% of the total area. It is predicted that the landscape fragmentation will gradually slowdown in 2030 under different intensities of the “Grain for Green” project. The dynamics of landscape fragmentation based on land use changes are conducive to the reasonable planning and objective evaluation of woodland and grassland spatial allocation and quality improvement, and provide an important basis for the formulation of ecological protection and land management policies.

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