干旱区生态环境知识资源中心(Arid): Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management

Arid

DOI	10.3390/rs13010032
	Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management
	Adenle, Ademola A.; Speranza, Chinwe Ifejika
通讯作者	Adenle, AA (corresponding author), Univ Bern, Inst Geog, Hallerstr 12, CH-3012 Bern, Switzerland. ; Adenle, AA (corresponding author), Fed Univ Technol, Dept Geog, PMB 65, Minna, Niger State, Nigeria.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:1
英文摘要	The Nigerian Guinea Savannah is the most extensive ecoregion in Nigeria, a major food production area, and contains many biodiversity protection areas. However, there is limited understanding of the social-ecological features of its degraded lands and potential insights for sustainable land management and governance. To fill this gap, the self-organizing map method was applied to identify the archetypes of both proximal and underlying drivers of land degradation in this region. Using 12 freely available spatial datasets of drivers of land degradation-4 environmental; 3 socio-economic; and 5 land-use management practices, the identified archetypes were intersected with the Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived land-degradation status of the region, and the state administrative boundaries. Nine archetypes were identified. Archetypes are dominated by: (1) protected areas; (2) very high-density population; (3) moderately high information/knowledge access; (4) low literacy levels and moderate-high poverty levels; (5) rural remoteness; (6) remoteness from a major road; (7) very high livestock density; (8) moderate poverty level and nearly level terrain; and (9) very rugged terrain and remote from a major road. Four archetypes characterized by very high-density population, moderate-high information/knowledge access, and moderate-high poverty level, as well as remoteness from a major town, were associated with 61.3% large-area degradation; and the other five archetypes, covering 38.7% of the area, were responsible for small-area degradation. While different combinations of archetypes exist in all the states, the five states of Niger (40.5%), Oyo (29.6%), Kwara (24.4%), Nassarawa (18.6%), and Ekiti (17.6%), have the largest shares of the archetypes. To deal with these archetypical features, policies and practices that address increasing population in combination with poverty reduction; and that create awareness about land degradation and promote sustainable practices and various forms of land restoration, such as tree planting, are necessary for progressing towards land-degradation neutrality in the Nigerian Guinea Savannah.
英文关键词	archetypes self-organizing maps land degradation drivers savannah Nigeria
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000606063700001
WOS关键词	COVER CHANGE ; LANDSCAPE ; FOREST ; DESERTIFICATION ; VULNERABILITY ; RESTORATION ; DYNAMICS ; SYSTEM ; SAHEL ; DELTA
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/348127
作者单位	[Adenle, Ademola A.; Speranza, Chinwe Ifejika] Univ Bern, Inst Geog, Hallerstr 12, CH-3012 Bern, Switzerland; [Adenle, Ademola A.] Fed Univ Technol, Dept Geog, PMB 65, Minna, Niger State, Nigeria
推荐引用方式 GB/T 7714	Adenle, Ademola A.,Speranza, Chinwe Ifejika. Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management[J],2021,13(1).
APA	Adenle, Ademola A.,&Speranza, Chinwe Ifejika.(2021).Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management.REMOTE SENSING,13(1).
MLA	Adenle, Ademola A.,et al."Social-Ecological Archetypes of Land Degradation in the Nigerian Guinea Savannah: Insights for Sustainable Land Management".REMOTE SENSING 13.1(2021).