气候变化领域集成服务门户(CCPortal): Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics

CCPortal

DOI	10.1002/ldr.3695
	Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics
	Akinyemi F.O.; Ghazaryan G.; Dubovyk O.
发表日期	2021
ISSN	10853278
起始页码	158
结束页码	172
卷号	32 期号:1
英文摘要	Achieving land degradation neutrality (LDN) has been proposed as a way to stem the loss of land resources globally. To date, LDN operationalization at the country level has remained a challenge both from a policy and science perspective. Using an approach incorporating cloud-based geospatial computing with machine learning, national level datasets of land cover, land productivity dynamics, and soil organic carbon stocks were developed. Using the example of Botswana, LDN and proportion of degraded land were assessed. Between 2000 and 2015, grassland lost approximately 17% of its original extent, the highest level of loss for any land category; land productivity decline was highest in artificial surface areas (11%), whereas 36% of croplands show early signs of decline. With the use of national metrics (NM), degraded areas were found to be 32.6% compared to 51.4% of the total land area when global default datasets (DD) were used. Estimates of degraded land computed with NM and DD were validated in Palapye, an agro-pastoral region in eastern Botswana, where Composite Land Degradation Index (CLDI) field-based data exists. Comparing land degradation (LD) in the three datasets (NM, DD, and CLDI), NM estimates were closest to the field data. The extra efforts put into developing national level data for LD assessment in this study is, thus, well-justified. Beyond demonstrating remote sensing viability for LD assessment, the study developed procedures for generating and validating national level datasets. Using these procedures, LD monitoring will be enhanced in Botswana and elsewhere since these remote sensing datasets can be updated using freely available satellite datasets. © 2020 John Wiley & Sons, Ltd.
英文关键词	Google Earth Engine; land cover; land degradation neutrality; land productivity dynamics; Mann–Kendall; soil organic carbon stocks
scopus关键词	Organic carbon; Productivity; Weathering; Artificial surfaces; Field-based data; Geospatial computing; Land degradation; Land productivities; Land resources; National level; Soil organic Carbon stocks; Remote sensing; comparative study; data set; environmental assessment; environmental monitoring; land cover; land degradation; organic carbon; policy approach; remote sensing; satellite data; soil organic matter; Botswana; Matthiola
来源期刊	Land Degradation and Development
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177132
作者单位	Geographies of Sustainability Department, Institute of Geography, University of Bern, Bern, Switzerland; Earth and Environmental Science, Botswana International University of Science and Technology, Palapye, Botswana; Centre for Remote Sensing of Land Surfaces, University of Bonn, Bonn, Germany
推荐引用方式 GB/T 7714	Akinyemi F.O.,Ghazaryan G.,Dubovyk O.. Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics[J],2021,32(1).
APA	Akinyemi F.O.,Ghazaryan G.,&Dubovyk O..(2021).Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics.Land Degradation and Development,32(1).
MLA	Akinyemi F.O.,et al."Assessing UN indicators of land degradation neutrality and proportion of degraded land for Botswana using remote sensing based national level metrics".Land Degradation and Development 32.1(2021).