Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu &amp; Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory

Khan, Iftikhar Ahmad; Khan, Mobushir Riaz; Baig, Muhammad Hasan Ali; Hussain, Zaker; Hameed, Nasir; Khan, Junaid Aziz

doi:10.1371/journal.pone.0226341

Cited by 15 publications

(11 citation statements)

References 49 publications

(51 reference statements)

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“…The total forest area in the entire district was calculated as 63 km 2 using the GFW data set for 2019. The study by Khan et al [92] used Landsat TM, Landsat ETM+, and Landsat OLI satellite data sets to map temperate forests in Sudhnuti district (471 km 2 area), AJK. They extracted vegetation fractions (forest, non-forest area) using Linear Spectral Mixture Analysis (LSMA), a supervised image classification approach, for 1989, 1993, 1999, 2005, 2010, 2015, and 2018.…”

Section: Other Districtsmentioning

confidence: 99%

“…However, their study did not mention the achieved values. In the first study of 2020, Khan et al [92] used 140 validation points to map the forest and non-forest fractional maps. This study reported an overall accuracy of 96%, kappa value of 0.92, producer's accuracy of 97% for forest and 95% for the non-forest area, and user's accuracy of 96% for forest and 97% for the non-forest area.…”

Section: Accuracies Assessments and Validations For Forest Mappingmentioning

confidence: 99%

“…In this forest ecoregion, 11 studies were conducted [29,30,51,52,54,55,63,64,85,92,98]. Two studies from the Himalayan subtropical pine forests' ecoregion reported an increase in the forest area [29,92], and all other studies reported a decline in the overall forest area. Most studies used pixel-based image classification techniques, while only one study [63] used OBIA.…”

Section: Himalayan Subtropical Pine Forestsmentioning

confidence: 99%

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A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

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Section: Other Districtsmentioning

confidence: 99%

Section: Accuracies Assessments and Validations For Forest Mappingmentioning

confidence: 99%

Section: Himalayan Subtropical Pine Forestsmentioning

confidence: 99%

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A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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“…Thus, remote sensing techniques [1] are considered a powerful tool for analyzing land use. However, it is necessary to consider different techniques for analyzing satellite images from different historical and current periods of land use [8].…”

Section: Changes In Rural Areas Of the City Of Carazinho (Rs) Betweenmentioning

confidence: 99%

Changes in Rural Areas of the City of Carazinho (RS) between 2001 and 2020: A Temporal Analysis Using Landsat TM-7 and TM-8 Images

Neckel¹,

Maculan²,

Muller³

et al. 2020

JCEA

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The analysis of satellite images is of fundamental importance in relation to understanding the evolution in land use. The general objective of this study is to analyze the changes in characteristics of rural land use in the municipality of Carazinho (RS) from 2001 to 2020 through the analysis of images taken from the Landsat TM-7 and TM-8 satellites. The TM-7 and TM-8 satellite images were classified and supervised, thus generation thematic maps with the following groups: tillage, forest, water resources and exposed soil. The analysis of this study showed that in 2001 the tillage group had an area of 10,651.6 ha, the forest group had an area of 7,248.03 ha, the water resources group occupied an area of 1,444.96 ha, and the exposed soil group occupied an area of 6,798.75 ha. When using the images taken in 2020, the same area presented the following data: the tillage group with an area of 15,941.5 ha. (60.95%), the forest group with an area of 4,320.99 ha (16.52%), water resources group with an area of 141.02 ha (0.53%), and the exposed soil group with an area of 5,747.22 ha (21.97%). The results demonstrate changes in land use during the study period, mainly with regard to the increase of cultivation areas and, consequently, the suppression of vegetation.

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“…Forests cover approximately one-third of the earth's land surface area [1], and tropical and sub-tropical forests form a major component of the total area. Natural forests host diverse plant and animal species [2], caters to forage resources for insect pollinators [3], and help alleviate the effects of climate change by atmospheric carbon sequestration [4,5].…”

Section: Introductionmentioning

confidence: 99%

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Gyamfi-Ampadu

Gebreslasie

2021

Forests

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Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

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Assessment of forest cover and carbon stock changes in sub-tropical pine forest of Azad Jammu & Kashmir (AJK), Pakistan using multi-temporal Landsat satellite data and field inventory

Cited by 15 publications

References 49 publications

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Changes in Rural Areas of the City of Carazinho (RS) between 2001 and 2020: A Temporal Analysis Using Landsat TM-7 and TM-8 Images

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

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