Reforestation improves vegetation coverage and biomass, but not spatial structure, on semi-arid mine dumps

2022 ◽  
Vol 175 ◽  
pp. 106508
Author(s):  
Yongjun Yang ◽  
Jiajia Tang ◽  
Yiyan Zhang ◽  
Shaoliang Zhang ◽  
Yongli Zhou ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Vegetation Coverage ◽  
Mine Dumps ◽  
Semi Arid

scholarly journals Estimation of the Restored Forest Spatial Structure in Semi-Arid Mine Dumps Using Worldview-2 Imagery

Forests ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 695
Author(s):  
Xiaoxiao Zhu ◽  
Yongli Zhou ◽  
Yongjun Yang ◽  
Huping Hou ◽  
Shaoliang Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Spatial Structure ◽  
Regression Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Mine Dumps ◽  
Restoration Age ◽  
Bp Neural Network Model ◽  
Semi Arid

Forest monitoring is critical to the management and successful evaluation of ecological restoration in mined areas. However, in the past, available monitoring has mainly focused on traditional parameters and lacked estimation of the spatial structural parameters (SSPs) of forests. The SSPs are important indicators of forest health and resilience. The purpose of this study was to assess the feasibility of estimating the SSPs of restored forest in semi-arid mine dumps using Worldview-2 imagery. We used the random forest to extract the dominant feature factor subset; then, a regression model and mind evolutionary algorithm-back propagation (MEA-BP) neural network model were established to estimate the forest SSP. The results show that the textural features found using 3 × 3 window have a relatively high importance score in the random forest model. This indicates that the 3 × 3 texture factors have a relatively strong ability to explain the restored forest SSPs when compared with spectral factors. The optimal regression model has an R2 of 0.6174 and an MSRE of 0.1001. The optimal MEA-BP neural network model has an R2 of 0.6975 and an MSRE of 0.0906, which shows that the MEA-BP neural network has greater accuracy than the regression model. The estimation shows that the tree–shrub–grass mode with an average of 0.7351 has the highest SSP, irrespective of the restoration age. In addition, the SSP of each forest configuration type increases with the increase in restoration age except for the single grass configuration. The increase range of SSP across all modes was 0.0047–0.1471 after more than ten years of restoration. In conclusion, the spatial structure of a mixed forest mode is relatively complex. Application cases show that Worldview-2 imagery and the MEA-BP neural network method can support the effective evaluation of the spatial structure of restored forest in semi-arid mine dumps.

scholarly journals Spatiotemporal Variation of Vegetation Coverage and Its Response to Climate Factors and Human Activities in Arid and Semi-Arid Areas: Case Study of the Otindag Sandy Land in China

2020 ◽  
Vol 12 (12) ◽  
pp. 5214 ◽  
Author(s):  
Hao Wang ◽  
Fei Yao ◽  
Huasheng Zhu ◽  
Yuanyuan Zhao
Keyword(s):  
Human Factors ◽  
Human Activities ◽  
Vegetation Dynamics ◽  
Spatiotemporal Variation ◽  
Vegetation Coverage ◽  
Arid Areas ◽  
Climate Factors ◽  
Sandy Land ◽  
Otindag Sandy Land ◽  
Semi Arid

Vegetation coverage is a key variable in terrestrial ecosystem monitoring and climate change research and is closely related to soil erosion and land desertification. In this article, we aimed to resolve two key scientific issues: (1) quantifying the spatial-temporal vegetation dynamics in the Otindag Sandy Land (OSL); and (2) identifying the relative importance of climate factors and human activities in impacting vegetation dynamics. Based on correlation analysis, simple regression analysis, and the partial derivative formula method, we examined the spatiotemporal variation of vegetation coverage in the OSL, belonging to the arid and semiarid region of northern China, and their interaction with climate-human factors. The results showed that the vegetation coverage of the area showed a downward trend with a rate of −0.0006/a during 2001–2017, and gradually decreased from east to west. Precipitation was the main climate factor controlling the overall distribution pattern of vegetation coverage, while the human factors had a more severe impact on the vegetation coverage than the climate factors in such a short period, and the overall impact was negative. Among the human factors, population pressure, urbanization, industrialization, pastoral production activities, and residents’ lifestyles had a negative impact. However, ecological restoration polices alleviated the contradiction between human development and vegetation deterioration. The results of this article provide a scientific basis for restoring grassland systems in arid and semi-arid areas

Seed bank spatial structure in semi-arid environments: beyond the patch-bare area dichotomy

Plant Ecology ◽  
2007 ◽  
Vol 195 (2) ◽  
pp. 215-223 ◽  
Author(s):  
Idoia Caballero ◽  
José Miguel Olano ◽  
Adrián Escudero ◽  
Javier Loidi
Keyword(s):  
Spatial Structure ◽  
Seed Bank ◽  
Arid Environments ◽  
Bare Area ◽  
Semi Arid

scholarly journals Construction of an evapotranspiration model and analysis of spatiotemporal variation in Xilin River Basin, China

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256981
Author(s):  
Hongbo Yu ◽  
Congming Cao ◽  
Qiaofeng Zhang ◽  
Yuhai Bao
Keyword(s):  
River Basin ◽  
Arid Regions ◽  
Exchange Process ◽  
Growing Season ◽  
Spatial Variations ◽  
Ecological Environment ◽  
Vegetation Coverage ◽  
Estimation Model ◽  
Basin Scale ◽  
Semi Arid

Surface evapotranspiration is a water exchange process between the atmosphere, biosphere, and hydrosphere. Accurate evapotranspiration estimations in arid and semi-arid regions are important for monitoring droughts and protecting the ecological environment. The main objective of this study is to build an evapotranspiration estimation model suitable for an effective scientific and objective evaluation of water consumption in the arid and semi-arid regions of the Xilin River Basin based on comprehensive parameters, including meteorological parameters, vegetation coverage, and soil water content. In this study, the community evapotranspiration model was initially constructed using field data, which was then expanded for applicability to the Xilin River Basin based on Geographic Information System technology and spatial heterogeneity characteristics of remote sensing data; both models were significant at the 0.05 level. The monthly evapotranspiration values in July during 2000–2017 and those from April to September (growing season) during the dry, normal, and wet years were calculated using the model at the basin scale. The evapotranspiration showed a generally increasing trend, which was consistent with the fluctuation trend in precipitation in July during 2000–2017. The trend curve for evapotranspiration was gentle during the growing season in dry years, but steep during wet years. The evapotranspiration was the lowest in April, with negligible spatial variations throughout the Xilin River Basin. During May–July, the evapotranspiration was higher than that in other months, in the following order: upper reaches > middle reaches > lower reaches; this was consistent with the vegetation coverage. The evapotranspiration declined and spatial variations were not evident during August–September. The results of this study provide a reference for evapotranspiration model construction and a scientific basis for evaluating regional water resources and protecting the ecological environment.

The Spatial Structure of Variability in a Semi-arid, Fluvial Ecosystem

Ecosystems ◽  
2006 ◽  
Vol 9 (3) ◽  
pp. 386-397 ◽  
Author(s):  
David Bruce Lewis ◽  
John D. Schade ◽  
Anne K. Huth ◽  
Nancy B. Grimm
Keyword(s):  
Spatial Structure ◽  
Fluvial Ecosystem ◽  
Semi Arid

scholarly journals The importance of plant water use on evapotranspiration covers in semi-arid Australia

2012 ◽  
Vol 9 (10) ◽  
pp. 11911-11940 ◽  
Author(s):  
A. Schneider ◽  
S. Arnold ◽  
D. Doley ◽  
D. R. Mulligan ◽  
T. Baumgartl
Keyword(s):  
Plant Species ◽  
Mine Waste ◽  
Vapour Pressure Deficit ◽  
Soil Surface ◽  
Bare Soil ◽  
Vegetation Coverage ◽  
Plant Water Use ◽  
Unsaturated Hydraulic Conductivity ◽  
Cover Systems ◽  
Semi Arid

Abstract. We estimated the evapotranspiration (ET) for an area vegetated with characteristic semi-arid native Australian plant species on ET mine waste cover systems. These systems aim to minimise drainage into underlying hazardous wastes by maximising evaporation (E) from the soil surface and transpiration from vegetation. An open top chamber was used to measure diurnal and daily ET of two plant species – Senna artemisioides (silver cassia) and Sclerolaena birchii (galvanised burr) – after a simulated rainfall event, as well as E from bare soil. Both ET and E decreased with increasing time after initial watering. Different temporal patterns were observed for daily ET from the two plant species and E from bare soil, revealing Senna artemisioides as intensive and Sclerolaena birchii as extensive water exploiters. A strong positive linear relationship was identified between ET (and E), and the atmospheric water demand represented by the vapour pressure deficit. The relationship always was more pronounced in the morning than in the afternoon, indicating a diminishing water supply from the soil associated with a declining unsaturated hydraulic conductivity of the soil in the afternoon. The slopes of the regression lines were steepest for Senna artemisioides, reflecting its intensive water-exploiting characteristics. We used the derived estimates of ET and E to predict the effect of species composition on plot ET in relation to total vegetation coverage. Although both species proved suitable for an operational ET cover system, vegetation coverage should exceed at least 50% in order to markedly influence plot ET, a value which is likely to be unsustainable in semi-arid climates.

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