干旱区生态环境知识资源中心(Arid): Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)

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DOI	10.1007/s13201-019-1028-3
	Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)
	Javid, Kanwal; Akram, M. Ameer Nawaz; Mumtaz, Maria; Siddiqui, Rumana
通讯作者	Javid, K (corresponding author), Univ Punjab, Dept Geog, Lahore, Pakistan.
来源期刊	APPLIED WATER SCIENCE
ISSN	2190-5487
EISSN	2190-5495
出版年	2019
卷号	9 期号:7
英文摘要	The entire world is collectively facing the problem of climate change. The deterioration of the earth's climate change is being noticed and felt most apparently in Southeast Asia and predominantly in Pakistan. Pakistan is a victim of climate change, due to which Pakistan faces several geographical, political, economic and even social problems. The harmful impacts of climate change in the form of smog and abnormal heat waves have claimed the life of many Pakistanis. Climate change has brought disastrous impact on the agrarian economy of Pakistan, which has plunged the country into awful straits. Climatic change is a slow and continuous process. It is needed that climatic changes in an area should be traced out in time to face upcoming climatic challenges. Present research work has traced out such changes and introduced a new climatic classification scheme for the climate of Pakistan, by using remote sensing (RS) and a new climatic compound index that is RSCCI gives a calculated value, which is used to describe the state and the changes in the climatic system of an area. RSCCI is the combination of different indices. On the basis of RSCCI, a climatic index, spatiotemporal investigation is conducted to measure aridity, humidity and semi-aridity all over Pakistan. In order to find out the extent of these climatic conditions, three MODIS dataset images of 250 m resolution were acquired. RS applications are used effectively to assess the changing climatic trends for the period of eighteen years in Pakistan from 2000 to 2018. On the basis of the above-mentioned results, a new climatic classification has been introduced with five major classes, i.e., drought, aridity, humidity, wetlands and cold drought. The area of five classes has been calculated by using RS tools and RSCCI for the years of 2000 and 2018. New climatic classification of Pakistan divides Pakistan into five regions which is based on RSCCI . There is an increase in arid region of 1.84% in Pakistan from RSCCI 2000 to RCSSI 2018. Similarly, there is also increase in an area of wetlands and humid regions of Pakistan, i.e., 1.9% and 9.72%, respectively, from RSCCI 2000 to RCSSI 2018. On the other hand, there is 0.78% reduction of area of cold drought region, 8.49% reduction in moderate drought and 4.19% reduction in an area of intense drought classes from RSCCI 2000 to RCSSI 2018, which is a positive change. The results show dramatic changes which advocate the need of a new climatic classification for Pakistan. This new climate classification of Pakistan is based on 18 years of data only. Dramatic climatic changes could be imagined and predicted within next 30 years in Pakistan.
英文关键词	RSCCI Climatic index Climate change MODIS Remote sensing
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000481760400001
WOS关键词	VEGETATION ; COEFFICIENTS ; PERFORMANCE ; PATTERNS
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369809
作者单位	[Javid, Kanwal; Akram, M. Ameer Nawaz; Mumtaz, Maria] Univ Punjab, Dept Geog, Lahore, Pakistan; [Siddiqui, Rumana] Queen Mary Coll, Dept Geog, Lahore, Pakistan
推荐引用方式 GB/T 7714	Javid, Kanwal,Akram, M. Ameer Nawaz,Mumtaz, Maria,et al. Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)[J],2019,9(7).
APA	Javid, Kanwal,Akram, M. Ameer Nawaz,Mumtaz, Maria,&Siddiqui, Rumana.(2019).Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018).APPLIED WATER SCIENCE,9(7).
MLA	Javid, Kanwal,et al."Modeling and mapping of climatic classification of Pakistan by using remote sensing climate compound index (2000 to 2018)".APPLIED WATER SCIENCE 9.7(2019).