干旱区生态环境知识资源中心(Arid): Implementation of a multi-layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

Arid

DOI	10.1002/ldr.4785
	Implementation of a multi-layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands
	Gupta, Sharad Kumar; Kim, Jungrack; Zucca, Claudio; Sharma, Arindam
通讯作者	Kim, J
来源期刊	LAND DEGRADATION & DEVELOPMENT
ISSN	1085-3278
EISSN	1099-145X
出版年	2023
卷号	34 期号:14 页码:4399-4414
英文摘要	Various earth observation (EO) data and related spatial technologies are employed to monitor land degradation (LD) and its impacts on the drylands. However, a comprehensive framework that integrates multiple data sources and metrics to generate a consistent output still needs to be explored. Furthermore, the lack of consistency and the limited reliabilities of existing approaches make it challenging to identify proper countermeasures (avoid, reduce, reverse LD) and support decisions to combat LD in line with the Land Degradation Neutrality (LDN) Framework. The Multi-Layered Land Degradation Tracer (ML-LDT) is a framework that addresses the above research gaps. It is based on a comprehensive framework that integrates data extraction, LD monitoring, and forecasting capacities and consists of three functional layers, that is, a base data processor, monitoring/modeling components, and a forecasting layer employing machine learning (ML) algorithms. ML-LDT has a state-of-art capacity to model wind erosion together with sand and dust storms (SDS), making it particularly suited to the arid lands most prone to these processes. It is implemented over cloud processors with open-source components based on industry standards. Therefore, the routines are easily adaptable to user requirements in LDN planning. We evaluated the ML-LDT framework in the Indian Thar Desert and Inner Mongolian drylands. The results showed the overall robustness of the procedures and that ML-based models provide good insights to forecast future LD/SDS patterns that can be used to simulate LDN scenarios and targets. We validated the forecasting results against LD maps and remote sensing observations.
英文关键词	desertification land degradation neutrality ML-LDT machine learning sand and dust storm soil erosion
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001005871200001
WOS关键词	DUST STORMS ; WIND EROSION ; CLIMATE ; DECORRELATION ; ECOSYSTEM ; RUSLE ; SAND ; SOIL
WOS类目	Environmental Sciences ; Soil Science
WOS研究方向	Environmental Sciences & Ecology ; Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397755
推荐引用方式 GB/T 7714	Gupta, Sharad Kumar,Kim, Jungrack,Zucca, Claudio,et al. Implementation of a multi-layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands[J],2023,34(14):4399-4414.
APA	Gupta, Sharad Kumar,Kim, Jungrack,Zucca, Claudio,&Sharma, Arindam.(2023).Implementation of a multi-layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands.LAND DEGRADATION & DEVELOPMENT,34(14),4399-4414.
MLA	Gupta, Sharad Kumar,et al."Implementation of a multi-layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands".LAND DEGRADATION & DEVELOPMENT 34.14(2023):4399-4414.