Jianzhong Lu

Prof. Dr. Jianzhong Lu

Wuhan University, Wuhan, Hubei, China
orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 April 2022 in International Journal of Disaster Risk Reduction
Verified
Journal Article
Closed access
JWJinru Wu
Jinru Wu
XCXiaoling Chen
Xiaoling Chen
Jianzhong LuJianzhong Lu
Jianzhong Lu


29 March 2016 in Environmental Science and Pollution Research
Verified
Journal Article
Closed access
PDF file
HQHengda Qi
Hengda Qi
Jianzhong LuJianzhong Lu
Jianzhong Lu
XCXiaoling Chen
Xiaoling Chen
SSSabine Sauvage
Sabine Sauvage
Jose-Miguel Sanchez-PerezJose-Miguel Sanchez-Perez
Jose-Miguel Sanchez-Perez

1 August 2021 in Water Research
Verified
Journal Article
Closed access
Zhiqiang TanZhiqiang Tan
Zhiqiang Tan
Yunliang LiYunliang Li
Yunliang Li
QZQi Zhang
Qi Zhang
XLXinggen Liu
Xinggen Liu
YSYanyan Song
Yanyan Song
Jianzhong LuJianzhong Lu
Jianzhong Lu

11 February 2022 in Stochastic Environmental Research and Risk Assessment
Verified
Journal Article
Closed access
PDF file
AYAndaleeb Yaseen
Andaleeb Yaseen
Jianzhong LuJianzhong Lu
Jianzhong Lu
XCXiaoling Chen
Xiaoling Chen

21 February 2020 in Remote Sensing
Verified
Journal Article
Open access
PDF file
Asif SajjadAsif Sajjad
Asif Sajjad
Jianzhong LuJianzhong Lu
Jianzhong Lu
XCXiaoling Chen
Xiaoling Chen
CCChikondi Chisenga
Chikondi Chisenga
Nayyer SaleemNayyer Saleem
Nayyer Saleem
HHHammad Hassan
Hammad Hassan
In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.