Spatiotemporal variations and a driving force analysis of arbor forest loss

ZHAO Qing, HUANG Fei, CHEN Xiaohui, LIN Yuying, QIU Rongzu, WU Zhilong, HU Xisheng

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (2) : 227-235.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (2) : 227-235. DOI: 10.12302/j.issn.1000-2006.202102005

Spatiotemporal variations and a driving force analysis of arbor forest loss

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Abstract

【Objective】As the main body of forest ecosystems, arbor forests play a decisive role regarding ecological functions such as climate regulation and water and soil conservation. The major objective of this study was to understand the spatiotemporal variation trend of arbor forest loss and to explore the driving factors responsible for arbor forest loss in China. 【Method】Based on the Sen+Mann-Kendall significance test and the standard deviation ellipse method, this study analyzed the spatio-temporal changes in tree loss from 2005 to 2018. The exploratory regression analysis was used to screen the main driving factors of tree loss (tree height > 5 m), and the spatiotemporal pattern of the driving factors contributing to tree loss was investigated using a geographically weighted regression model. 【Result】(1) The loss of arbor forest in China showed an increasing trend from 2005 to 2018, with an annual increase of 412.451 km2. (2) From 2005 to 2018, the migration path of arbor forest loss characterized by the gravity center of the forest changed irregularly, and the areas with serious arbor forest loss concentrated in the south. (3) Per capita gross domestic product was mainly negatively correlated with arbor forest loss; the positive correlation area of per capita disposable income of urban residents increased markedly, but the influence decreased; the urbanization rate showed a positive correlation with arbor forest loss, and the degree of influence decreased; road density showed a negative correlation with arbor forest loss, and its negative effect on the loss of arbor forest was not significant. 【Conclusion】In the context of the overall continuous improvement of forest resources in China, however, there are obvious regional differences regarding the loss of arbor forests. This study found that the loss of arbor forests in the northeast forest region and the Three Northern Shelterbelt Project implementation regions was small and showed a significant reducing trend, whereas in the Southeast forest region, the loss of arboreal forests in Hunan, Jiangxi, Guangdong, Guangxi, and other provinces was relatively large and still showed a trend of the significant increase.

Key words

loss of arbor forest / Sen+Mann-Kendall / standard deviation ellipse method(SDE) / geographically weighted regression(GWR) / spatiotemporal variation / driving force

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ZHAO Qing , HUANG Fei , CHEN Xiaohui , et al . Spatiotemporal variations and a driving force analysis of arbor forest loss[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(2): 227-235 https://doi.org/10.12302/j.issn.1000-2006.202102005

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
WANG X G, Comita Liza S, HAO Z Q, et al. Local-Scale drivers of tree survival in a temperate forest[J]. PLoS ONE, 2018, 7(2).DOI: 10.1371/journal.pone.0029469.
https://doi.org/10.1371/journal.pone.0029469
Cited in this article [1]
[2]
LIU Y L, FENG Y H, ZHAO Z, et al. Socioeconomic drivers of forest loss and fragmentation: a comparison between different land use planning schemes and policy implications[J]. Land Use Policy, 2016, 54:58-68. DOI: 10.1016/j.landusepol.2016.01.016.
https://doi.org/10.1016/j.landusepol.2016.01.016
https://linkinghub.elsevier.com/retrieve/pii/S0264837715301903
Cited in this article [2]
[3]
MILLER M D. The impacts of Atlanta’s urban sprawl on forest cover and fragmentation[J]. Appl Geogr, 2012, 34:171-179. DOI: 10.1016/j.apgeog.2011.11.010.
https://doi.org/10.1016/j.apgeog.2011.11.010
https://linkinghub.elsevier.com/retrieve/pii/S0143622811002335
Cited in this article [1]
[4]
NEWMAN M E, MCLAREN K P, WILSON B S. Assessing deforestation and fragmentation in a tropical moist forest over 68 years; the impact of roads and legal protection in the Cockpit Country, Jamaica[J]. For Ecol Manage, 2014, 315:138-152. DOI: 10.1016/j.foreco.2013.12.033.
https://doi.org/10.1016/j.foreco.2013.12.033
https://linkinghub.elsevier.com/retrieve/pii/S0378112713008360
Cited in this article [1]
[5]
LI M S, HUANG C Q, ZHU Z, et al. Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model[J]. For Ecol Manage, 2008, 257(6):1480-1488. DOI: 10.1016/j.foreco.2008.12.023.
https://doi.org/10.1016/j.foreco.2008.12.023
https://linkinghub.elsevier.com/retrieve/pii/S0378112708009109
Cited in this article [1]
[6]
CARRANZA M L, HOYOS L, FRATE L, et al. Measuring forest fragmentation using multitemporal forest cover maps: forest loss and spatial pattern analysis in the Gran Chaco, central Argentina[J]. Landscape Urban Plann, 2015, 143:238-247. DOI: 10.1016/j.landurbplan.2015.08.006.
https://doi.org/10.1016/j.landurbplan.2015.08.006
https://linkinghub.elsevier.com/retrieve/pii/S0169204615001619
Cited in this article [1]
[7]
TANG J, BU K, YANG J C, et al. Multitemporal analysis of forest fragmentation in the upstream region of the Nenjiang River Basin, northeast China[J]. Ecol Indic, 2012, 23:597-607. DOI: 10.1016/j.ecolind.2012.05.012.
https://doi.org/10.1016/j.ecolind.2012.05.012
https://linkinghub.elsevier.com/retrieve/pii/S1470160X12002117
Cited in this article [1]
[8]
BREMER L L, FARLEY K A. Does plantation forestry restore biodiversity or create green deserts: a synthesis of the effects of land-use transitions on plant species richness[J]. Biodiversity Conserv, 2010, 19(14): 3893-3915. DOI: 10.1007/s10531-010-9936-4.
https://doi.org/10.1007/s10531-010-9936-4
http://link.springer.com/10.1007/s10531-010-9936-4
Cited in this article [1]
[9]
LINDENMAYER D B. Landscape change and the science of biodiversity conservation in tropical forests: a view from the temperate world[J]. Biol Conserv, 2009, 143(10):2405-2411. DOI: 10.1016/j.biocon.2009.12.015.
https://doi.org/10.1016/j.biocon.2009.12.015
https://linkinghub.elsevier.com/retrieve/pii/S0006320709005230
Cited in this article [1]
[10]
LÜ Y H, ZHANG L W, FENG X M, et al. Recent ecological transitions in China: greening, browning and influential factors[J]. Sci Rep, 2015, 51(1):587-592. DOI: 10.1038/srep08732.
https://doi.org/10.1038/srep08732
Cited in this article [1]
[11]
李凌超, 刘金龙, 程宝栋, 等. 中国劳动力转移对森林转型的影响[J]. 资源科学, 2018, 40(8):1526-1538.
LI L C, LIU J L, CHENG B D, et al. Influence of labor transfer on China’s forest transition[J]. Resour Sci, 2018, 40(8):1526-1538. DOI: 10.18402/resci.2018.08.04.
https://doi.org/10.18402/resci.2018.08.04
Cited in this article [1]
[12]
赵晓迪, 李凌超, 杨文涛, 等. 福建省县域森林转型社会经济影响因素分析[J]. 林业科学, 2019, 55(8):147-156.
ZHAO X D, LI L C, YANG W T, et al. Analysis of socio-economic influencing factors of forest transition in counties of Fujian Province[J]. Sci Silvae Sin, 2019, 55(8):147-156. DOI: 10.11707/j.1001-7488.20190816.
https://doi.org/10.11707/j.1001-7488.20190816
Cited in this article [1]
[13]
MORTON D C, DEFRIES R S, SHIMABUKURO Y E, et al. Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon[J]. Proc Natl Acad Sci USA, 2006, 103(39):14637-14641. DOI: www.pnas.org/cgi/doi/10.1073/pnas.0606377103.
https://doi.org/www.pnas.org/cgi/doi/10.1073/pnas.0606377103
https://pnas.org/doi/full/10.1073/pnas.0606377103
Cited in this article [1]
[14]
CARR D L, SUTER L, BARBIERI A. Population dynamics and tropical deforestation: state of the debate and conceptual challenges[J]. Popul Environ, 2006, 27(1):89-113. DOI: 10.1007/s11111-005-0014-x.
https://doi.org/10.1007/s11111-005-0014-x
http://link.springer.com/10.1007/s11111-005-0014-x
Cited in this article [1]
[15]
LIN Y Y, QIU R Z, YAO J X, et al. The effects of urbanization on China’s forest loss from 2000 to 2012: evidence from a panel analysis[J]. J Cleaner Prod,2019, 214:270-278. DOI: 10.1016/j.jclepro.2018.12.317.
https://doi.org/10.1016/j.jclepro.2018.12.317
https://linkinghub.elsevier.com/retrieve/pii/S0959652618340484
Cited in this article [3]
[16]
GONG C F, YU S X, JOESTING H, et al. Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images[J]. Landscape Urban Plann, 2013, 117:57-65. DOI: 10.1016/j.landurbplan.2013.04.009.
https://doi.org/10.1016/j.landurbplan.2013.04.009
https://linkinghub.elsevier.com/retrieve/pii/S0169204613000789
Cited in this article [1]
[17]
HOEK J V D, OZDOGAN M, BURNICKI A, et al. Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of southwest China[J]. Appl Geogr, 2014, 47:177-189. DOI: 10.1016/j.apgeog.2013.12.010.
https://doi.org/10.1016/j.apgeog.2013.12.010
https://linkinghub.elsevier.com/retrieve/pii/S0143622813002956
Cited in this article [1]
[18]
吴恒, 朱丽艳, 王海亮, 等. 乔木树种分布格局和林分空间结构分析[J]. 林业资源管理, 2020(1): 54-61.
WU H, ZHU L Y, WANG H L, et al. Analyses on arbor tree species distribution pattern and stand spatial structure[J]. For Resour Manage, 2020(1): 54-61. DOI: 10.13466/j.cnki.lyzygl.2020.01.008.
https://doi.org/10.13466/j.cnki.lyzygl.2020.01.008
Cited in this article [2]
[19]
HANSEN M C, POTAPOV P V, MOORE R, et al. High-Resolution Global maps of 21st-century forest cover change[J]. Sciince, 2013, 342(6160):850-853. DOI: 10.1126/science.1244693.
https://doi.org/10.1126/science.1244693
Cited in this article [2]
[20]
BRYAN B A, GAO L, YE Y Q, et al. China’s response to a national land-system sustainability emergency[J]. Nat (Int Wkly J Sci), 2018, 559(7713):193-204. DOI: 10.1038/s41586-018-0280-2.
https://doi.org/10.1038/s41586-018-0280-2
Cited in this article [2]
[21]
中国林业科学研究院森林生态环境与保护研究所. 林业资源分类与代码森林类型: GB/T 14721-2010[S]. 北京: 中国标准出版社, 2010:88.
Cited in this article [1]
[22]
王昊, 吕植, 顾垒, 等. 基于Global Forest Watch观察2000-2013年间中国森林变化[J]. 生物多样性, 2015, 23(5):575-582.
https://doi.org/10.17520/biods.2015122
Abstract
了解森林的变化, 是评价生物多样性变化趋势和制订有效保护行动的基础, 也是公共政策及广大公众普遍关心的议题, 然而国内一直都没有可供空间分析的森林分布及变化的公开数据。Hansen等于2013年在Global Forest Watch网站上发布了基于Landsat数据的30 m分辨率的全球森林变化数据集, 第一次提供给研究者一套高分辨率的公开数据。我们解读了2000-2013年的数据, 并与已有的国家森林清查、ChinaCover、GlobeLand30等数据集进行比较。结果表明, Global Forest Watch数据质量可靠, 其与GlobeLand30数据集的森林分布有较高的空间重合度, 后者森林类别的生产者精度为87%, 用户精度为89%。在大于20%的树木覆盖度下, 估算出2000年森林面积为1,780,472 km<sup>2</sup>, 与同期国家森林清查数据1,749,092 km<sup>2</sup>相近, 解读结果表明2000-2010年间森林减少面积在37,551-42,031 km<sup>2</sup>之间, 而同期其他几套数据显示森林面积增量为2,370-433,810 km<sup>2</sup>。森林面积年减少量最大值出现在2008年, 以该年为拐点, 森林每年减少面积的变化趋势在此之前为显著增加, 在此之后为显著降低, 林地变化最活跃的区域在广西、广东、福建、江西和云南等南方省份。到2013年底, 占保护区总面积约60%以上的407个国家级保护区仅覆盖了森林面积的5.03%, 对森林保护的面积覆盖有限, 保护区对其内部的森林保护有成效, 区内森林面积净减1.39%, 低于全国平均水平(3.46%), 相当于少毁林1,856 km<sup>2</sup>, 但仍有1,200 km<sup>2</sup>的净减。
WANG H, LV Z,GUL, et al. Observations of China’s forest change(2000-2013) based on Global Forest Watch dataset[J]. Biodiversity Sci, 2015, 23(5):575-582. DOI: 10.17520/biods.2015122.
https://doi.org/10.17520/biods.2015122
http://www.biodiversity-science.net/EN/10.17520/biods.2015122
Cited in this article [1]
[23]
袁丽华, 蒋卫国, 申文明, 等. 2000-2010年黄河流域植被覆盖的时空变化[J]. 生态学报, 2013, 33(24):7798-7806.
YUAN L H, JIANG W G, SHEN W M, et al. The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010[J]. Acta Ecol Sin,2013, 33(24):7798-7806. DOI: 10.5846/stxb201305281212.
https://doi.org/10.5846/stxb201305281212
Cited in this article [1]
[24]
张莉, 陈芸芝, 汪小钦. 基于时序MODIS NDVI数据的长汀县植被趋势特征研究[J]. 福州大学学报(自然科学版), 2016, 44(5):661-667.
ZHANG L, CHEN Y Z, WANG X Q. Vegetation trends research of Changting County based on time series of MODIS NDVI[J]. J Fuzhou Univ(Nat Sci Ed), 2016, 44(5):661-667. DOI: 10.7631/issn.1000-2243.2016.05.0661.
https://doi.org/10.7631/issn.1000-2243.2016.05.0661
Cited in this article [1]
[25]
蔡博峰, 于嵘. 基于遥感的植被长时序趋势特征研究进展及评价[J]. 遥感学报, 2009, 13(6):1170-1186.
CAI B F, YU R. Advance and evaluation in the long time series vegetation trends research based on remote sensing[J]. J Remote Sens, 2009, 13(6):1170-1186. DOI: 10.11834/jrs.20090614.
https://doi.org/10.11834/jrs.20090614
Cited in this article [1]
[26]
邵霜霜, 师庆东. 基于FVC的新疆植被覆盖度时空变化[J]. 林业科学, 2015, 51(10):35-42.
SHAO S S, SHI Q D. Spatial and temporal change of vegetation cover in Xinjiang based on FVC[J]. Sci Silvae Sin, 2015, 51(10):35-42. DOI: 10.11707/j.1001-7488.20151005.
https://doi.org/10.11707/j.1001-7488.20151005
Cited in this article [1]
[27]
王佃来, 刘文萍, 黄心渊. 基于Sen+Mann-Kendall的北京植被变化趋势分析[J]. 计算机工程与应用, 2013, 49(5):13-17.
WANG D L, LIU W P, HUANG X Y. Trend analysis in vegetation cover in Beijing based on Sen+Mann-Kendall method[J]. Comput Eng Appl,2013, 49(5):13-17. DOI: 1002-8331 (2013) 05-0013-05.
https://doi.org/1002-8331 (2013) 05-0013-05
Cited in this article [1]
[28]
黄晓军, 祁明月, 李艳雨, 等. 关中地区PM2.5时空演化及人口暴露风险[J]. 环境科学, 2020, 41(12):5245-5255.
HUANG X J, QI M Y, LI Y L, et al. Spatio-temporal evolution and population exposure risk to PM2.5 in the Guanzhong area[J]. Environ Sci, 2020, 41(12):5245-5255. DOI: 10.13227/j.hjkx.202004189.
https://doi.org/10.13227/j.hjkx.202004189
https://pubs.acs.org/doi/10.1021/es070273v
Cited in this article [1]
[29]
YUAN J F, BIAN Z F, YAN Q W, et al. An approach to the temporal and spatial characteristics of vegetation in the growing season in western China[J]. Remote Sens, 2020, 12(6):945. DOI: 10.3390/rs12060945.
https://doi.org/10.3390/rs12060945
https://www.mdpi.com/2072-4292/12/6/945
Cited in this article [1]
[30]
康雄, 曹俊涛, 陈成, 等. 不同趋势法的宁夏长时序植被变化分析[J]. 测绘通报, 2020, 11(11):23-27.
KANG X, CAO J T, CHEN C, et al. Analysis of long-term vegetation change in Ningxia with different trend methods[J]. Bull Surv Mapp, 2020, 11(11):23-27. DOI: 10.13474/j.cnki.11-2246.2020.0348.
https://doi.org/10.13474/j.cnki.11-2246.2020.0348
Cited in this article [1]
[31]
LEFEVER D W. Measuring geographic concentration by means of the standard deviational ellipse[J]. Am J Sociol, 1926, 32(1):88-94. DOI: 10.1086/214027.
https://doi.org/10.1086/214027
https://www.journals.uchicago.edu/doi/10.1086/214027
Cited in this article [1]
[32]
WANG B, SHI W Z, MIAO Z L. Confidence analysis of standard deviational ellipse and its extension into higher dimensional euclidean space[J]. PLoS ONE, 2017, 10(3):e118537. DOI: 10.1371/journal.pone.0118537.
https://doi.org/10.1371/journal.pone.0118537
Cited in this article [1]
[33]
SHI Y S, MATSUNAGA T, YAMAGUCHI Y, et al. Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in south and southeast Asia from 1999 to 2014[J]. Sci Total Environ,2018, 615:177-186. DOI: 10.1016/j.scitotenv.2017.09.241.
https://doi.org/10.1016/j.scitotenv.2017.09.241
https://linkinghub.elsevier.com/retrieve/pii/S0048969717325925
Cited in this article [1]
[34]
夏晓圣, 汪军红, 宋伟东, 等. 2000-2019年中国PM2.5时空演化特征[J]. 环境科学, 2020, 41(11):4832-4843.
XIA X S, WANG J H, SONG W D, et al. Spatio-temporal evolution of PM2.5 concentration during 2000-2019 in China[J]. Environ Sci, 2020, 41(11):4832-4843. DOI: 10.13227/j.hjkx.202004108.
https://doi.org/10.13227/j.hjkx.202004108
https://pubs.acs.org/doi/10.1021/es0709627
Cited in this article [1]
[35]
蔡汉, 朱权, 罗云建, 等. 快速城镇化地区耕地景观生态安全格局演变特征及其驱动机制[J]. 南京林业大学学报(自然科学版), 2020, 44(5):181-188.
CAI H, ZHU Q, LUO Y J, et al. Evolution characteristics and driving mechanisms of cultivated landscape eco-security patterns in rapid urbanization areas[J]. J Nanjing For Univ (Nat Sci Ed), 2020, 44(5):181-188. DOI: 10.3969/j.issn.1000-2006.201907047.
https://doi.org/10.3969/j.issn.1000-2006.201907047
Cited in this article [1]
[36]
薛陈利, 张会琼, 邹滔, 等. 中老铁路经济廊带生态质量及其与人类活动的关系[J]. 应用生态学报, 2021, 32(2):638-648.
XUE C L, ZHANG H Q, ZOU T, et al. Ecological quality and its relationships with human activities in China-Laos Railway Economic Belt[J]. Chin J Appl Ecol, 2021, 32(2):638-648. DOI: 10.13287/j.1001-9332.202102.011.
https://doi.org/10.13287/j.1001-9332.202102.011
Cited in this article [1]
[37]
隋雪艳, 吴巍, 周生路, 等. 都市新区住宅地价空间异质性驱动因素研究--基于空间扩展模型和GWR模型的对比[J]. 地理科学, 2015, 35(6):683-689.
https://doi.org/10.13249/j.cnki.sgs.2015.06.683
Abstract
以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。
SUI X Y, WU W, ZHOU S L, et al. Drive Pattern on the spatial heterogeneity of residential land price in urban district: a comparison of spatial expansion method and GWR model[J]. Sci Geogr Sin, 2015, 35(6):683-689. DOI: 10.13249/j.cnki.sgs.2015.06.003.
https://doi.org/10.13249/j.cnki.sgs.2015.06.003
Cited in this article [1]
[38]
白立敏, 冯兴华, 孙瑞丰, 等. 生境质量对城镇化的时空响应--以长春市为例[J]. 应用生态学报, 2020, 31(4):1267-1277.
BAI L M, FENG X H, SUN R F, et al. Spatial and temporal responses of habitat quality to urbanization: a case study of Changchun City, Jilin Province, China[J]. Chin J Appl Ecol, 2020, 31(4):1267-1277. DOI: 10.13287/j.1001-9332.202004.012.
https://doi.org/10.13287/j.1001-9332.202004.012
Cited in this article [1]
[39]
薛瑞晖, 于晓平, 李东群, 等. 基于地理加权回归模型探究环境异质性对秦岭大熊猫空间利用的影响[J]. 生态学报, 2020, 40(8):2647-2654.
XUE E H, YU X P, LI D Q, et al. Using geographically weighted regression to explore the effects of environmental heterogeneity on the space use by giant pandas in Qinling Mountains[J]. Acta Ecol Sin, 2020, 40(8):2647-2654. DOI: 10.5846/stxb201903120469.
https://doi.org/10.5846/stxb201903120469
Cited in this article [1]
[40]
YACKULIC C B, FAGAN M, JAIN M, et al. Biophysical and socioeconomic factors associated with forest transitions at multiple spatial and temporal scales[J]. Ecol Soc, 2011, 16(3):15. DOI: 10.5751/ES-04275-160315.
https://doi.org/10.5751/ES-04275-160315
Cited in this article [1]
[41]
侯孟阳, 姚顺波. 森林资源与经济增长的EKC关系检验--基于省际面板数据的实证研究[J]. 林业科学, 2019, 55(12):113-122.
HOU M Y, YAO S B. Testing of the EKC relationship between amount of forest resources and economic growth: an empirical study based on provincial panel data[J]. Sci Silvae Sin, 2019, 55(12):113-122. DOI: 10.11707/j.1001-7488.20191212.
https://doi.org/10.11707/j.1001-7488.20191212
Cited in this article [1]
[42]
曹荣青, 胡喜生, 吴承祯. 福建省2000-2012年森林丧失量时空分布动态研究[J]. 山东农业大学学报(自然科学版), 2019, 50(2):197-201.
CAO R Q, HU X S, WU C Z. Study on temporal and spatial distribution of forest loss in Fujian Province during 2000 to 2012[J]. J Shandong Agric Univ (Nat Sci Ed), 2019, 50(2):197-201. DOI: 10.3969/j.issn.1000-2324.2019.02.004.
https://doi.org/10.3969/j.issn.1000-2324.2019.02.004
Cited in this article [1]
[43]
侯秀英, 黄菲, 赵青, 等. 福建省森林丧失的时空格局演化及其驱动力机制[J]. 山地学报, 2020, 38(6):829-840.
HOU X Y, HUANG F, ZHAO Q, et al. Spatial-temporal pattern evolution and driving mechanism of forest loss in Fujian Province, China[J]. Mt Res, 2020, 38(6):829-840. DOI: 10.16089 /j.cnki.1008-2786.000559.
https://doi.org/10.16089 /j.cnki.1008-2786.000559
Cited in this article [1]
[44]
李奇, 朱建华, 范立红, 等. 西南地区乔木林碳储量及木材生产潜力预测[J]. 生态环境学报, 2018, 27(3):416-423.
LI Q, ZHU J H, FAN L H, et al. Prediction of forest carbon storage and timber yield potential in southwestern China[J]. Ecol and Environ Sci, 2018, 27(3):416-423. DOI: 10.16258/j.cnki.1674-5906.2018.03.003.
https://doi.org/10.16258/j.cnki.1674-5906.2018.03.003
Cited in this article [1]
[45]
刘豪, 徐冬梅. 山西省乔木林碳汇动态趋势研究[J]. 林业资源管理, 2019(6):49-54,68.
LIU H, XU D M. Study on the dynamic trend of carbon sinks in arbor forests in Shanxi Province[J]. For Resour Manage, 2019(6):49-54, 68. DOI: 10.13466/j.cnki.lyzygl.2019.06.010.
https://doi.org/10.13466/j.cnki.lyzygl.2019.06.010
Cited in this article [1]
[46]
冉建波, 陈兴伟. 土地利用空间格局的置换变化与代换变化分析[J]. 地球信息科学学报, 2015, 17(6):661-667.
https://doi.org/10.3724/SP.J.1047.2015.00661
Abstract
为揭示土地利用空间格局的动态变化,本文基于土地利用转移矩阵,从类型间的空间转移过程出发,构建了置换变化与代换变化的计算模型,并对其2种变化进行了分析。置换变化是2种类型之间以相同面积发生空间位置的置换过程,且各种类型的数量结构保持不变;代换变化则是所研究类型的数量结构保持不变,而与之转换的其他类型数量上的增加或减少;从而将单一类型的总变化进一步细分为置换变化、代换变化与净变化。以晋江流域1985年与2006年2期土地利用格局的变化为例进行分析,结果显示：在1985-2006年间,晋江流域的土地利用空间格局发生了显著变化;园地、建设用地、未利用地呈现扩张态势,草地与旱地呈现萎缩变化,水域变化不大;这6种地类的交换变化面积相对较少,以置换变化为主。水田与林地则主要表现为空间位置的交换变化,其中,水田呈现出与林地、旱地2种类型之间的置换变化,而林地则主要是园地-林地与林地-草地这2对类型间的代换变化。结果表明,通过置换变化与代换变化的分析,更细化了各种类型之间的动态变化过程。
RAN J B, CHEN X W. Displacement and substitution of the land-use spatial pattern[J]. J Geo-Inf Sci, 2015, 17(6):661-667. DOI: 10.3724/SP.J.1047.2015.00661.
https://doi.org/10.3724/SP.J.1047.2015.00661
Cited in this article [1]
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