A system dynamics model for billion trees tsunami afforestation project of Khyber Pakhtunkhwa in Pakistan: Model application to afforestation activities

Nazir, Naila; Farooq, Aqsa; Jan, Sajjad Ahmad; Ahmad, Aftab

doi:10.1007/s11629-018-5076-1

Cited by 15 publications

(4 citation statements)

References 48 publications

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“…As the study area's population increased, so did the extent of towns and agricultural lands, leading to overexploitation for domestic purposes. Despite the continuous decline in forest cover, however, Pakistan recently underwent a minor increase in forest area thanks to afforestation and regeneration developmental projects [7,44,58]. On the other hand, the socio-economic and environmental effects of changing forest cover are diverse and often mysterious.…”

Section: Introductionmentioning

confidence: 99%

Environmental Risk Assessment in the Hindu Kush Himalayan Mountains of Northern Pakistan: Palas Valley, Kohistan

et al. 2022

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Forest cover in the Hindu Kush Himalayan (HKH) mountains of northern Pakistan has changed dramatically due to community dynamics such as population growth, household dynamics, and intensive economic activity for people’s livelihoods. Demographic development is one of the major factors influencing forest cover change in a previously sparsely populated environment. An abrupt upsurge in population exerts adverse effects on the local natural resources, specifically forests. The present research shows an increase in population from 1980 to 2017, the development of human settlements, and a long-term decline in forest cover. This study was conducted in the Palas valley in the HKH mountains using GIS and remote sensing (RS) technology. Analysis of the changes between 1980, 2000, and 2017 was done using ArcGIS and the maximum likelihood algorithm for supervised classification of Landsat MSS TM ETM+ and Sentinel 2A satellite images. We used Euclidean distances and buffer analysis techniques to identify that most changes occurred within 1 to 3 km of the settlement’s proximity in each period. We also found changes in forest cover to be much greater near settlements than elsewhere in the study area. According to the findings of the study, population explosion and other socio-economic factors have imposed excessive pressure on vegetation cover, resulting in the loss of 17,076 ha of forests in the remote Palas valley.

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Section: Introductionmentioning

confidence: 99%

Environmental Risk Assessment in the Hindu Kush Himalayan Mountains of Northern Pakistan: Palas Valley, Kohistan

et al. 2022

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“…6 and 7). Nazir et al 48 presented a mathematical model which suggests that the BTAP project will increase the forest area of the KP province to 23.59 million hectares, which is a 3.29% improvement over the forest area. Although this mathematical model considers the complete area of the KP province, our study of 15 districts shows a similar trend (presented in Table 8).…”

Section: Discussionmentioning

confidence: 99%

AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

Zulfiqar

Ghaffar

Shahzad

et al. 2021

J. Appl. Rem. Sens.

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Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wild fires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand-crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In contrast to traditional methods, we present AI-ForestWatch, an end to end framework for forest estimation and change analysis. The proposed approach uses deep convolution neural network-based semantic segmentation to process multi-spectral spaceborne images to quantitatively monitor the forest cover change patterns by automatically extracting features from the dataset. Our analysis is completely data driven and has been performed using extended (with vegetation indices) Landsat-8 multi-spectral imagery from 2014 to 2020. As a case study, we estimated the forest area in 15 districts of Pakistan and generated forest change maps from 2014 to 2020, where major afforestation activity is carried out during this period. Our critical analysis shows an improvement of forest cover in 14 out of 15 districts. The AI-ForestWatch framework along with the associated dataset will be made public upon publication so that it can be adapted by other countries or regions. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…This extreme precipitation hazard, especially the intensity of extreme precipitation on the southern part of the KRB, will remain a challenge in future. The Provincial Government of KPK started a project called the "Billion Tree Tsunami Project" to mitigate the deforestation in the region [83][84][85], but studies are required to assess how useful this project will be to controlling the flood peaks or rapid generation of runoff caused by the deforestation and extensive urbanization. At the same time, challenges are available for concerned organizations, and opportunities are also available to mitigate the extreme precipitation-related hazard on the southern region.…”

Section: Recommendations Challenges and Opportunitiesmentioning

confidence: 99%

Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities

Mehmood

Jia

et al. 2021

Water

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Recent evidence of regional climate change impacts on hydrological cycle directed us to study the floods in a high elevated and rapidly urbanized river basin, the Kabul River basin (KRB), Pakistan, which is susceptible to frequent flooding. Therefore, we analyzed the changes in flood regime at various spatial and temporal scales and their possible causes, which is accomplished by using flood indicators, trend analysis, change point analysis, and hydrological modeling. The results showed that the northern and northwestern parts of the KRB were more exposed to flood hazard than the southern parts under long-term scenario (1961/64-2015). However, after the change points, the flood risk decreased in the northern and increased in the southern regions. This spatial shift increased the vulnerability of population to the flood hazard, because the majority of population resides in the southern region. The extreme precipitation has also increased, especially the maximum one-day rainfall and maximum five-day rainfall throughout the basin. Particularly, the major cause of the decrease in different flood indicators in the northern parts of the KRB is the corresponding decrease in the annual and monsoonal rainfall and corresponding positive mass balance of glaciers in the northern region after the occurrence of change point in flood regime. However, the major cause of the increase in flood hazard on the southern part of the KRB is associated with maximum five-day rainfall. A 68% variability of annual maximum flood for the Kabul River at Nowshera and an 84% variability of annual maximum flood for Bara River at Jhansi post are explained by maximum five-day rainfall. In addition, a considerable decrease in forests (–5.21%) and increase in the urban area (88.26%) from 1992–2015 also amplifies the risk of higher flood peaks. The results of hydrological modeling suggest that the six-hourly flood peak increased by 6.85% (1992–2010) and 4.81% (2010–2015) for the extreme flood of 2010 for the Kabul River at Nowshera. The flood peak per decade will increase by 8.6%, as compared to the flood peak under the land use scenario of 2010. Therefore, consideration of proper land use planning is crucial for sustainable flood management in the KRB.

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A system dynamics model for billion trees tsunami afforestation project of Khyber Pakhtunkhwa in Pakistan: Model application to afforestation activities

Cited by 15 publications

References 48 publications

Environmental Risk Assessment in the Hindu Kush Himalayan Mountains of Northern Pakistan: Palas Valley, Kohistan

Environmental Risk Assessment in the Hindu Kush Himalayan Mountains of Northern Pakistan: Palas Valley, Kohistan

AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities

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