TY - JOUR
T1 - Leveraging machine learning and remote sensing to monitor long-term spatial-temporal wetland changes
T2 - Towards a national RAMSAR inventory in Pakistan
AU - Shafi, Ansa
AU - Chen, Shengbo
AU - Waleed, Mirza
AU - Sajjad, Muhammad
N1 - Funding information:
The authors are thankful to all the mentioned institutes for the provisioning of several datasets to conduct this valuable study. This research is funded by the National Key Research and Development Program of China (No. 2020YFA0714103). Sajjad M. is funded by the HKBU Research Grant Committee (Start-up Grant-Tier 1, 162764) of the Hong Kong Baptist University, Hong Kong SAR. The authors declare no conflict of interest.
Publisher copyright:
© 2022 Elsevier Ltd.
PY - 2023/2
Y1 - 2023/2
N2 - In Pakistan, wetlands are of primary focus as they withstand the effects of floods, recharge groundwater, and provide several services in the context of economic, cultural, and climate mitigation aspects. However, the lack of field data and huge monitoring costs hinder their sustainable management in Pakistan. In connection with this, the current study leverages Google Earth Engine (GEE), earth observation data, and machine learning-based Random Forest (RF) algorithm to evaluate spatial-temporal heterogeneities in wetlands in Pakistan between 1990 and 2020. Additionally, the first high-resolution long-term inventory of wetlands in Pakistan is presented to provide a baseline. Our results ascertain an increase in wetlands areas over the last 30 years. The swamps’ area increased from 1391.19 km2 in 1990 to 8510.43 km2 in 2020 (2.62% annual change rate). Similarly, the marshes area increased between 1990 and 2020 with a ∼1.04% annual change rate. Conversely, the water area decreased from 8371.97 km2 in 1990 to 7818.34 km2 in 2020. The increase in wetlands could be associated with good conservation and planting practices in Pakistan. While these results provide important insights to implement conservation practices in the context of wetland sustainability, the resultant data is essential to the national wetlands inventory database for future evaluations.
AB - In Pakistan, wetlands are of primary focus as they withstand the effects of floods, recharge groundwater, and provide several services in the context of economic, cultural, and climate mitigation aspects. However, the lack of field data and huge monitoring costs hinder their sustainable management in Pakistan. In connection with this, the current study leverages Google Earth Engine (GEE), earth observation data, and machine learning-based Random Forest (RF) algorithm to evaluate spatial-temporal heterogeneities in wetlands in Pakistan between 1990 and 2020. Additionally, the first high-resolution long-term inventory of wetlands in Pakistan is presented to provide a baseline. Our results ascertain an increase in wetlands areas over the last 30 years. The swamps’ area increased from 1391.19 km2 in 1990 to 8510.43 km2 in 2020 (2.62% annual change rate). Similarly, the marshes area increased between 1990 and 2020 with a ∼1.04% annual change rate. Conversely, the water area decreased from 8371.97 km2 in 1990 to 7818.34 km2 in 2020. The increase in wetlands could be associated with good conservation and planting practices in Pakistan. While these results provide important insights to implement conservation practices in the context of wetland sustainability, the resultant data is essential to the national wetlands inventory database for future evaluations.
KW - Google earth engine
KW - Landsat
KW - Remote sensing
KW - Wetland sustainability
KW - Wetlands
UR - http://www.scopus.com/inward/record.url?scp=85145196667&partnerID=8YFLogxK
U2 - 10.1016/j.apgeog.2022.102868
DO - 10.1016/j.apgeog.2022.102868
M3 - Journal article
SN - 0143-6228
VL - 151
JO - Applied Geography
JF - Applied Geography
M1 - 102868
ER -
