Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

Zhang, Zhiming; Coillie, Frieke Van; Ou, Xiaokun; Wulf, Robert R. De

doi:10.3390/rs6021026

Cited by 19 publications

(11 citation statements)

References 89 publications

(116 reference statements)

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“…To correct for geometric and radiation distortions, geometric correction and radiation correction were preprocessed, respectively [48]. The three images were classified using a combination of object-oriented interpretation, visual interpretation, and an artificial neural network feed-forward back-propagation algorithm [49,50], with the specific methods detailed in Zhang, et al [51]. Based on the first-level classification system of land use status [52] and field investigations in Weixi County, four land use categories were identified: agricultural land, forest, water, and other.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Zinda

Zhang

2019

Forests

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In China, the Returning Farmland to Forest Program (RFFP) has afforested large areas, transforming land and livelihoods. By impacting vegetation cover, it may also drive spatial pattern changes across landscapes. Most studies have focused on time series data as a means to determine the effectiveness of the program, but there is a paucity of community-level comparative studies. Twelve communities in Northwest Yunnan Province were selected to test whether the RFFP changed landscape patterns by testing the following hypotheses: with (or without) the RFFP, forest and shrubland fragmentations would decrease (or increase) and farmland fragmentation would increase (or decrease). Remote sensing images from 2000, 2010, and 2014 were used to compare the differences in landscape patterns. Survey data from 421 households were used to examine the socioeconomic and ecological factors that affect the differences in landscape fragmentation across communities. The results showed that landscape patterns and fragmentation metrics were not significantly different between communities with or without the RFFP, regardless of the class or landscape level. These communities showed consistent patterns of change in their fragmentation parameters between 2000 and 2014, with forest fragmentation decreasing and the fragmentation of farmland and the overall landscape increasing. The regression models suggest these changes were affected by the local natural conditions, socioeconomic patterns, policy implementation, and farmer livelihoods, with the proximity to market towns and elevation being significant factors. The RFFP alone did not directly drive the changes in landscape patterns for the considered region. For the new RFFP to effectively contribute to reducing fragmentation, managers of afforestation efforts should carefully consider livelihoods and biophysical factors that influence changes in landscape patterns.

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Section: Remote Sensing Datamentioning

confidence: 99%

Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Zinda

Zhang

2019

Forests

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“…In the CCA ordination diagram, the arrow represented soil physical and chemical factors and its length indicated the correlation between plants and the environmental factors [ 52 – 54 ]. It could be concluded from the diagram that both AK and AP were very important soil factors for SSBs.…”

Section: Resultsmentioning

confidence: 99%

Analysis on Soil Seed Bank Diversity Characteristics and Its Relation with Soil Physical and Chemical Properties after Substrate Addition

He¹,

Lv²,

Li³

et al. 2016

PLoS ONE

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AimsConsidered as an essential measure in the application of soil seed bank (SSB) projects, the mixing of substrate and surface soil can effectively improve soil condition. This research is aimed at exploring the diversity characteristics of SSBs and the relationships between SSBs and soil properties.MethodsCanonical correspondence analysis (CCA) was adopted to describe the ordination of SSBs on soil properties’ gradients; multiple linear regressions were adopted to analyze the relationship between average growth height and soil properties, density and soil properties.ResultsExperimental groups of mixed substrate (the mixture of organic and inorganic substrates) had high diversity indexes, especially the Shannon-Wiener Index compared with those of single substrate. Meanwhile, a higher number of species and increased density were also noted in those of mixed substrate. The best test group, No.16, had the highest diversity indexes with a Shannon-Wiener of 1.898, Simpson of 0.633 and Pielou of 0.717, and also showed the highest density of 14000 germinants /m2 and 21 species. In addition, an improvement of the soil’s chemical and physical properties was noted when the substrates were mixed. The mixed substrate of turfy soil and perlite could effectively enhance the soil moisture content, whilst a mixed substrate of rice husk carbon and vermiculite could improve the content of available potassium (AK) and phosphorus (AP) and strengthen soil fertility. The germinated plants also reflected obvious regularities of ordination on soil factor gradients. Three distinct cluster groups were presented, of which the first cluster was distributed in an area with a relatively higher content of AK and AP; the second cluster was distributed at places with relatively higher soil moisture content; and the third cluster of plants didn’t show any obvious relationship with soil physical and chemical properties. Through CCA analysis, AK and AP were considered the most important soil factors to influence the SSB, which was illustrated in regression analysis with a high correlation coefficient when dependent values were growth height and density respectively. The linear regression equations with: growth height = 142.728 − 1.514TC + 30.218AP − 5.083TN + 10.839AK + 1.234mc; density = −68.216 − 4.609TC + 9.566AP − 35.492TN + 19.779AK − 1.591mc.were established by multiple linear regression.Conclusions(1) The mixed substrates showed a greater advantage for SSBs than single substrates, both in improving the number of species, density and diversity indexes. (2) The germinated plants reflected an obvious preference to different soil conditions, which different mixed substrates could improve accordingly. (3) AK, AP were important soil factors to influence SSBs, especially in the growth of plants and density.

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“…Nine land cover classes were selected based on (i) adequacy in capturing major land cover types, (ii) feasibility of identification using Landsat, ALOS, and Gaofen-1 image data, and (iii) relevance to vegetation change. The image processing protocol used to prepare the vegetation maps has been described by Zhang et al (2014). To address the effects of RFFP on differences in land cover change patterns between communities with and without RFFP, the nine land cover classes were grouped into six categories: forest, shrub, agricultural land, and three non-vegetation classes (snow, water, shadow) ( Table 2).…”

Section: Remote Sensing Datamentioning

confidence: 99%

Forest transitions in Chinese villages: Explaining community-level variation under the returning forest to farmland program

Zhang

Zinda

2017

Land Use Policy

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China's Returning Farmland to Forest Program (RFFP) aimed to transform rural landscapes and livelihoods by compensating households for planting trees on retired farmland. The program has been attributed a key role in an apparent forest transition. Studies uncover great local variation in its impacts, but the mechanisms behind them have received little attention. We examine such heterogeneity in 12 communities in northwest Yunnan, assessing the hypothesis that the RFFP catalyzed a state-led forest transition by evaluating the contributions of RFFP implementation and other processes to land cover change. Our dataset combines socioeconomic data from household surveys, focus groups, and intensive interviews with remote sensing data for a linked, cross-scale analysis. Results show no significant relationship between RFFP implementation and community-level vegetation cover change. Between 2000 and 2010, high-elevation communities had larger vegetation gains, while from 2010 to 2014, low-elevation communities had larger gains. Regression analyses and interview data show off-farm labor, tree crop planting, and changing energy sources influenced the rate of community-level vegetation change. This pattern, combining tree crop cultivation with labor outflows, may represent a distinct "policy plantation" pathway of forest gain. Meanwhile, new, high-elevation cash crops may be constraining forest expansion. This analysis suggests limited additionality for the RFFP in this region and highlights how heterogeneous, intersecting land use processes bring uneven forest transitions.

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Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

Cited by 19 publications

References 89 publications

Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China?

Analysis on Soil Seed Bank Diversity Characteristics and Its Relation with Soil Physical and Chemical Properties after Substrate Addition

Forest transitions in Chinese villages: Explaining community-level variation under the returning forest to farmland program

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