Next Article in Journal
Chemical Properties and Microbial Analysis of Waterlogged Archaeological Wood from the Nanhai No. 1 Shipwreck
Previous Article in Journal
Spatial Variability and Optimal Number of Rain Gauges for Sampling Throughfall under Single Oak Trees during the Leafless Period
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 12
Issue 5
10.3390/f12050586
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review

by
Ishfaq Ahmad Khan
1,
Waseem Razzaq Khan
2,
Anwar Ali
3 and
Mohd Nazre
1,*
1
Department of Forest Science & Biodiversity, Faculty of Forestry and Environment, University Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
2
Institut Ekosains Borneo, Universiti Putra Malaysia Bintulu Kampus, Sarawak 97008, Malaysia
3
Pakistan Forest Institute Peshawar, Peshawar 25130, Pakistan
*
Author to whom correspondence should be addressed.
Forests 2021, 12(5), 586; https://doi.org/10.3390/f12050586
Submission received: 20 March 2021 / Revised: 14 April 2021 / Accepted: 15 April 2021 / Published: 8 May 2021
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Review Reports Versions Notes

Abstract

:
Climate change is acknowledged as a global threat to the environment and human well-being. Forest ecosystems are a significant factor in this regard as they act both as a sink and a source of carbon. Forest carbon evaluation has received more attention after the Paris Agreement. Pakistan has 5.1% forest cover of its total land area, which comprises nine forest types. This study covers the studies conducted on above-ground biomass and carbon stock in various forest types of Pakistan. Most of the studies on biomass and carbon stock estimation have been conducted during 2015–2020. The non-destructive method is mostly followed for carbon stock estimation, followed by remote sensing. The destructive method is used only for developing allometric equations and biomass expansion factors. The information available on the carbon stock and biomass of Pakistan forest types is fragmented and sporadic. Coniferous forests are more important in carbon sequestration and can play a vital role in mitigating climate change. Pakistan is a signatory of the Kyoto Protocol and still lacks regional and national level studies on biomass and carbon stock, which are necessary for reporting under the Kyoto Protocol. This study will help researchers and decision-makers to develop policies regarding Reducing Emissions from Deforestation and forest Degradation (REDD+), conservation, sustainable forest management and enhancement of forest carbon stocks

1. Introduction

The forest ecosystem is considered one of the most significant ecosystems in combating climate change. Climate change is acknowledged as a global threat to the environment and human well-being. Natural systems such as glaciers, coral reefs, boreal and tropical forests, and polar and alpine ecosystems are susceptible to climate change [1]. Forests are valued as a useful tool against global warming. They regulate the carbon cycle by sequestering carbon dioxide from the atmosphere via photosynthesis and store it in their biomass and soil organic matter. Therefore, it is imperative to evaluate the source and sink of carbon at local, sub-national and national levels to assess the prospective role of different carbon pools in decreasing atmospheric carbon dioxide and preventing global warming. Hence, it is essential to obtain reliable data on forest carbon stocks and greenhouse gas emissions. Article 2.1 of the Kyoto Protocol addressed global warming issues and urged the signatory nations to protect existing forests and enhance the carbon sequestration potential through afforestation, reforestation and sustainable forest management [2,3].
Forests store more carbon than the carbon present in the atmosphere [4], likely the amount of one trillion tons—double the atmospheric carbon [5]. Therefore, protecting these forests is essential to combat climate change and protect biodiversity. In the case of disturbances such as deforestation and land-use change from forest to agriculture, these forests become a source and emit carbon into the atmosphere.
Forests consist of five carbon pools: above-ground biomass, below-ground biomass, soil organic carbon, litter and deadwood. Most of the studies are conducted on above-ground biomass, followed by soil due to its carbon sequestration potential. Litter and deadwood are often ignored due to their low contribution to carbon sequestration. However, this can result in the underestimation of the study site’s carbon content.
Pakistan has a low forest cover of 5.1%, which translates into an area of 4.51 m ha from a total land area of 87.98 m [6,7]. Pakistan forest cover map is shown in Figure 1. Pakistan has diverse ecological zones, divided into various forest types based on altitude and species composition. These forests are mangrove forest, tropical thorn/scrub forest, tropical dry deciduous forest, sub-tropical broad-leaved evergreen forest, sub-tropical chirpine forest, moist temperate forest, dry temperate forest, sub-alpine forest and alpine scrub. Numerous studies on carbon stock estimation have been conducted in different parts of Pakistan [5,8]. Different techniques are used for carbon stock estimation across the globe: destructive, non-destructive and remote sensing. Non-destructive is the most common method used all over the world, while the destructive method is particularly used for developing an allometric equation. Remote sensing techniques, on the other hand, require field data for validation.
Reducing emissions from deforestation and forest degradation (REDD+) is an initiative to protect existing forests and enhance forest cover. Under the REDD+ framework, developing countries are encouraged to enhance their forest cover in return for carbon credits to prevent people from cutting trees and instead earn their livelihood from preserving the forests. As a benchmark to evaluate the country’s success in implementing REDD+ activities, the United Nations Framework Convention on Climate Change (UNFCCC) needs REDD+ countries to create a baseline of forest carbon stocks and emissions. Thus, an extensive forest inventory is needed in each forest type of Pakistan to report REDD+ related activities. To create a precise forest inventory, carbon stock estimation should be documented. In short, REDD+ deals with carbon stock estimation in terms of above-ground, below-ground and other carbon pools. This paper’s main objective is to highlight above-ground biomass, carbon stock and evaluation methods used in Pakistan’s forest ecosystem.

2. Carbon Stock Estimation Status in Pakistan

Since 1948, forest inventories (FIs) in Pakistan have been conducted for each designated forest located in a particular area. FIs are species-based compartment working circles [9]. The government of Pakistan developed a master plan for forestry development for 25 years (1993–2018) based on forest valuation at a national level using remote sensing (54 Landsat satellite images at a scale of 1:250,000) and fieldwork. Mountainous northern areas were not considered due to poor image quality. Sparse coniferous was not distinguishable from scrub forests, so about 4.7 m ha of a total of 7.04 m ha of northern areas remained unclassified [9]. Later in 2003–2004, the National Forest and Range Resource Assessment Study (NFRRAS) was conducted by the Ministry of Environment to assess the country’s total forest cover, and the study stated that the forest cover in 1997 and 2001 was 3.62 and 3.32 million ha, respectively [10]. Recently, Bukhari et al. [7] conducted a national level study and reported a total forest cover of 5.1%. The comparison of these three significant studies of forest cover is given in Table 1, while the output of these studies and other reports are given in Table 2.
Pakistan ratified the Kyoto Protocol and UNFCCC (United Nations Framework Convention on Climate Change) in 2005 [8]. Therefore, it is necessary to quantify carbon stocks in all forest types to report it under the Kyoto Protocol.
In Pakistan, provincial forest departments continuously carry out forest inventories to prepare the working plan and evaluate existing forest growing stocks. However, they have never undertaken a carbon stock estimation in these forests before 2015. In 2005, FAO prepared the Global Forest Resources Assessment (GFRA). For this purpose, FAO consulted with experts at ten regional and subregional workshops to have growing stock calculations of the country. This report contains the total carbon stock of the country. However, no scientific work has been carried out on actual biomass and carbon stock measurements in any forest in Pakistan, and the GFRA (2005) calculations are focused on remotely sensed data and may be prone to error [4].
Few research studies have been conducted on forest ecosystem biomass and carbon stock in the first decade (2000–2010) of the 21st century. Most research on biomass can be found in the second decade of the current century. These research studies on carbon stock assessment are broadly based on provincial territories, such as Khyber Pakhtunkhwa (KP), Sindh, Punjab, Balochistan, Azad Jammu and Kashmir (AJK) and Gilgit Baltistan (GB).

2.1. Khyber Pakhtunkhwa (KP)

Khyber Pakhtunkhwa, located in the north-west of Pakistan, has the highest forest cover area compared to other provinces. It has about one-third of the country’s forest and forty percent of its natural forest [5]. According to Bukhari et al. [7], the forest area of the entire province is 1.504 million ha, while Ali [11] has estimated the forest cover as 1.133 million ha. This discrepancy is mainly due to how the forest was defined in both studies. More than half of the country’s coniferous forests exist in this province, making it quite relevant for carbon sequestration.
Furthermore, the KP government has made substantial progress in planning for REDD+. They have developed (i) a sub-national strategy for REDD+, (ii) land-use maps, (iii) allometric models for biomass and carbon evaluation and (iv) a comprehensive forest carbon inventory at provincial level. Moreover, the Billion Tree Afforestation Project (BTAP) has recently been completed, which will further increase this territory’s carbon stock. KP has heterogeneous climatic conditions, thus supporting various types of forests, such as subalpine forests, dry temperate forests, moist temperate forests, oak forests, subtropical pine forests, subtropical broad-leaved forests and dry tropical thorn forests [12].
Various studies related to land-use change [13], deforestation, forest degradation [14], and environment–vegetation relationships in coniferous forests [15] were conducted in the Khyber Pakhtunkhwa province. These studies were limited to their specific objectives, not considering biomass and carbon stock estimation. It was only recently when a comprehensive study on biomass and carbon stock was conducted at provincial level [5].
Ali et al. [5] have estimated the carbon stock at the subnational level, covering all forest types of the province. Data were collected through a stratified cluster sampling method from 449 sample plots, and the total carbon stock quantified was 144.71 million tons with an average of 127.66 ± 9.32 tonha−1. Above-ground carbon stock was estimated as 68.15 million tons (48%), while below-ground biomass was 10% and litter was 1% of the total carbon stock. They further stated that dry temperate forests contain the most carbon (99.41 tonha−1), followed by moist temperate (85.04 tonha−1). Subalpine and oak forests have aboveground carbon stock of 34.27 and 34.58 tonha−1 respectively. Similarly, subtropical pine forests have aboveground carbon stock of 24.77 tonha−1, whereas subtropical broad-leaved and dry tropical thorne forests have aboveground carbon stocks of 4.52 and 4.48 tonha−1.This was the first ever attempt of carbon stock at the provincial level. Before this, most studies only focused on carbon stock estimation for a particular area, species or forest type.
Pakistan Forest Institute has developed a forest reference emission level/forest reference level for Khyber Pakhtunkhwa province, which can be used as a baseline to calculate the reduction in emissions under the REDD+ program [11]. According to the defined reference level, the total annual carbon sequestration is near 6 million tons, where the annual accumulation of above-ground and below-ground carbon is 4.627 and 1.341 million tons, respectively.
Olea ferruginea forest structure was studied using the Point Centered Quarter method in lower Dir and it was found that the study sites’ density and basal area ranged from 56–1089 trees/ha and 6.62–37.90 m2/ha, respectively [16]. Quercus forests of Chitral were assessed and the total tree density and basal area were estimated, ranging from 166.42–351.55 per ha and 12.11–30.13 m2/ha, respectively [17]. The study also reported a low tree density per ha and low basal area m2/ha at a lower height. The basal area of Monotheca buxifolia and other associated tree species was studied, and the results revealed that the basal area ranged from 8.40 to 33.51 m2/ha [18]. Regeneration status was also investigated, where 34% of species showed good regeneration status while 50% of species showed low status and 16% with zero regenerating status. These studies focused on tree density and basal area. The biomass of tree components was determined in 30 trees by selecting ten trees from each diameter class (up to 10 cm, 11–20, and above 20 cm) following the non-destructive method [19]. Total above-ground biomass for an individual tree was calculated as 20.59, 58.041 and 197.214 kg/tree for diameter classes of up to 10 cm, 11–20 cm, and above 20 cm, respectively, while the total biomass for an individual tree of diameter up to 10 cm, 11–20 cm and above 20 cm was 24.71, 69.649, and 236 kg, respectively. This study further reported that the mean biomass expansion factor was measured as 1.44 t.m−3 for all diameter classes. A coniferous forest of Dir Kohistan had 129.49 Mg.ha−1 as the mean carbon stock, while the total carbon content of the forest was 8.06 Tg [20]. A bare area of 5523.7 ha was also reported, which can be potentially used for plantations and increasing carbon stock. A pine forest of district Shangla was studied using the Point Centered Quarter method, according to which it was concluded that the Pinus wallichiana dominated the study area and has higher density with 409 trees per ha and 132.1 m2/ha basal area, while Abies pindrow was reported as 384 trees/ha with a basal area of 145.3 m2/ha [21]. The study also reported that the forest is facing a serious threat from over exploitation. A study was carried out to evaluate biomass expansion factors for an indigenous species (Picea smithiana) using a destructive method in Kumrat valley [22]. They have found out the mean above-ground biomass and mean total biomass as 2104.055 ± 264.814 and 2539.87 ± 341.80 kg, respectively. Their report further stated that stem contributed 66.28% in total biomass, followed by root, branches, foliage and twigs as 17.05, 11.28, 2.62 and 2.193%, respectively. They also estimated root-to-shoot ratio as 0.206 ± 0.011 and the mean biomass expansion factor as 0.686 ± 0.0027 t m3. A study was conducted to calculate the growing stock of Karaker forest. Average tree density, basal area and volume of the study area were 18 trees per ha, 1.3 m2/ha, and 15.19 m3/ha, respectively [23]. They also predicted the growing stock for the next ten years while keeping in mind the demand of the local people and reported a reduction in tree density, basal area and standing volume. Their study was limited to volume and did not estimate the area’s biomass and carbon stock. Munawar et al. [24] studied the absorption and emission capacity of District Dir forest with remote sensing and reported that the site contains a net carbon storage worth a million US dollars. This study further reported that the forest area increased with time and has a potential for REDD+ implementation. The carbon stock of the oak scrub forest of the Sheringal valley was calculated using field inventory data. The study area has a calculated mean carbon stock of 25.80 ± 0.47 t/ha [25]. Khan et al. [26] studied the Olea ferruginea forests of the Malakand division and reported that the tree density ranged from 153 to 2602 plants per ha, where Olea ferruginea has a relative density of 51–87%. The study site’s basal area varied from 19.55–2353 m2 ha−1 where Olea have a basal area of 48–93%. Ahmad and Moazzam [27] evaluated carbon stocks in three different land uses (forest, agriculture and rangeland) in Kumrat valley. They further divided the forest land based on the pure and mixed species. The study reported carbon storage in the forested land as 349.84 ± 30.79 t/ha, while rangeland and agriculture land stored 50 ± 6.5 and 28.62 ± 13.85 t/ha or carbon, respectively.
Total annual carbon sequestration and biomass in the coniferous forest of Dir Kohistan were estimated [28] as 1.65 t/ha/yr and 3.31 t/ha/yr, respectively. Khan et al. [29] examined the pine communities of Indus Kohistan and reported the tree density and basal area of seven different species. Above-ground biomass, using GIS and remote sensing, in coniferous forests of Murree and Abbottabad was estimated as 235 and 261 Mega g/ha, respectively [30].
Muslim graveyards are considered the least disturbed micro ecosystem because of cultural and religious rights. Ali et al. [31] carried out their study in Muslim graveyards of the Malakand division and found that the basal area ranged from 20–2523 m2/ha. Similarly, Ali et al. [6] studied the structure of temperate forests of the Kaghan valley using a systematic random sampling method with a grid of 700 × 700 m. The study reported that most of the trees were young and probably 17% were mature. Poor regeneration status was reported. According to Ahmad et al. [32], carbon stocks varied between agricultural land and dense forest as 30 to 538 MgC/ha, respectively. The results revealed the carbon storage as follows: Cedrus deodara, Pinus wallichiana, mix coniferous and Abies pindrow with an average of 538.14 ± 100.58 MgC/ha, open forest, agricultural land and rangeland with an average of 183.04 ± 79.6, 29.80 ± 4.3, and 35.63 ± 3.98 MgC/ha, respectively. This study further mentioned that the tree contains the highest carbon stock in forests. Cedrus deodara forest stored a greater amount of carbon than other forest types. Interestingly, Abies pindrow (AP) forest has mature trees but contained low carbon compared to Pinus wallichiana (PW) forest.
The biomass and carbon content of the Siran forest division were quantified through remote sensing [33]. The highest above-ground and below-ground biomass were recorded as 246 t/ha and 64 t/ha, respectively, while the lowest above-ground and below-ground biomass were 55 and 14 t/ha, respectively. Likewise, the uppermost above-ground and below-ground carbon stock were calculated as 116 and 30 t/ha, whereas the lowermost above-ground and below-ground carbon stock were 26 and 6.7 t/ha, respectively. The total carbon stock of the subtropical scrub forest of Khanpur Range, Haripur in six forest types was 43,570.9 t [34]. Ahmad et al. [35] studied the carbon sequestration potential of Cedrus deodara forest at three different altitudes. The study reported that the forest stored more carbon at high elevation due to less external influence, as compared to the carbon stored at lower elevation due to excessive human intervention. The tree biomass and carbon stock of Pinus roxburghii and Eucalyptus camaldulensis were examined in the district of Buner [36]. Eucalyptus plantations are mainly for commercial use, and it is harvested at the age of 4 or 5 years depending on the site conditions. These plantations help reduce the pressure on natural forests. Forest carbon stock in the Balakot region was calculated as 243.79 t/ha with an average tree biomass of 207.41 t/ha. Simultaneously, the soil carbon was estimated as 36.38 t/ha [37]. Another study was carried out by Ali et al. [38] to estimate the biomass and carbon stock of subtropical broad-leaved forests of the Malakand division. The study showed the mean biomass as 100.1 ± 22.6 t/ha and mean carbon stock as 49.54 ± 11.3 t/ha. The total average carbon stock in different carbon pools was estimated as 568.63 t/ha [39]. Similarly, Ahmad et al. [40] inspected different types of forests of Kumrat valley and reported its potential for carbon sequestration with field inventory and remote sensing. They stated that the forest vegetation covered 51.13% of the area of the valley. The mean total carbon stock of the area was reported as 393.70 ± 270.09 MgC/ha.
In this context, Shah et al. [41] have conducted a study on agroforestry to highlight its carbon stock potential. They used a stratified random sampling with a plot size of 100 × 100 m2 for the linear plantations and 33 × 33 m2 for block plantations. Carbon stock data were taken in three different agroforestry systems: agrisilviculture system, silvopastoral system and agrisilvopastoral system. The result shows that the silvopastoral system’s carbon storage was the highest (76.33 t/ha), followed by the agrisilvopastoral system (68.05 t/ha), while the agrisilvicultural system stored the lowest carbon (11.61 t/ha). Agroforestry is an excellent approach for climate change mitigation and reducing pressure on natural forests. The above-ground biomass and carbon content of the moist temperate forest of district Battagram were calculated as 148.79 t/ha and 69.93 t/ha with a mean CO2 equivalent value of 322.5 t/ha [42]. An allometric model was developed for Pinus roxburghii in the subtropical pine forest of Hazara forest division using destructive sampling [43].

2.2. Punjab

Punjab is the most populous province of Pakistan and has a low forest cover of about 4%. Scrub forest is the most dominant type with 2%, while riverine and broad leaf cover about 0.7% [7].
Various studies in Punjab, such as the status of natural tropical thorn forest [44], Lahore city land cover changes [45], and the forest cover change of Murree hill with remote sensing [46], were limited to changes in forest cover and did not include biomass and carbon stock estimations.
Nizami et al. [47] measured the carbon content of subtropical pine forest at two different sites; 126 + 2.94 t/ha in Ghoragali and 99 + 1.58 t/ha in the Lehterar forest ecosystem. The biomass of Olea ferruginea was estimated through destructive sampling [48]. They concluded that the stem contains the major portion of 49.01% of the total tree’s biomass. Likewise, Nizami [8] estimated the carbon stock of two different forest types: subtropical chirpine and sub-tropical broad-leaved forest. The study results revealed that both forest types store an average of 114.5 ± 2.26 t/ha of carbon, where tree biomass contains 92% of the carbon while soils holds only 8%. The carbon content and biodiversity of Karore reserve forest were assessed using field inventory [49]. Allometric relation for calculating above-ground and below-ground biomass at four various ages of Dalbergia sissoo using destructive sampling was examined [50]. The total biomass calculated varied from 144.83 to 456.37 kg/tree. The result shows that the stem contributes more (38%) while leaves contribute only 5%. Since the study was performed in monospecific and closed-canopy forests, the allometric relationships mentioned in this study may not be appropriate for mixed and open forests. The total wood volume of the Karore reserve forest was estimated at 2,261,621,047 cubic feet [51]. The carbon stock of Bela forest Jehlum was calculated and it is revealed that the total carbon stock (upper story vegetation, understory vegetation and soil) was 198.18 ± 18 t/ha [52].
Ali (2018) estimated aboveground carbon stock in scrub forests of Chakwal as 8.20 t/ha. Parerah reserve forests have aboveground carbon stock of 11.08 t/ha wheras Ara and Diljabba reserve forests have carbon stocks of 6.76 t/ha [53]. Olea ferruginea, Acacia modesta and Zizyphus were the dominant tree species of this tract. Similarly Ali (2019) has found aboveground carbon stock in subtropical pine forests of Kahuta, Rawalpindi Forest Division as 42.28 t/ha [54]. Mahmood et al. [55] also estimated the biomass of four different species using the destructive method. The estimated root biomass of Dalbergia sissoo was 2.0 t/ha [56]. Baig et al. [57] made the first attempt to study the above-ground biomass of Dalbergia sissoo (irrigated plantation) in Chichawatni using remote sensing. Above-ground carbon stock was estimated for citrus acreage using the non-destructive method [58]. The result shows that the study area’s total carbon stock was 1176 tons, with an average density of 40 plants per ha. Nawaz et al. [59] studied agroforestry potential and concluded that the area has an average sequestration rate of 2.05 to CO2/ha/yr with an average density of 31 trees/ha. Agroforestry is essential in this context, particularly for a country with low forest cover such as Pakistan. Carbon stocks of Eucalyptus camaldulensis in sandy marginal lands were evaluated as 1.80 megaton carbons [60]. The study area has more carbon sequestration potential in case of afforestation on 6.5 million ha saline and waterlogged marginal land. Arif et al. [61] have estimated the carbon stock of plantations in Chichawatni, Punjab. The study concluded that the mean carbon stock in upper-story, under-story and soil was calculated as 68.20 mg/ha, 1.8 mg/ha and 21.57 mg/ha, respectively. It was further highlighted that plantations in open spaces with proper and sustainable management practices can improve carbon stock potential. Carbon stock was measured in different tree species at the early age of plantation, and it was concluded that Populus deltoides stores more carbon in above-ground pools and soil [62]. The above-ground biomass in Murree and Abbottabad’s coniferous forests was estimated using GIS and remote sensing [30]. The estimated above-ground biomass for Murree and Abbottabad was 235 and 261 Mega g/ha, respectively. Agroforestry carbon stock was calculated at three different age classes, ranging from 0.89–26.58 Mg/ha [63]. They also reported that with increasing tree age, the soil organic carbon also increased. The urban forest structure of the old Christian Gora Cemetery of Lahore was studied by Pervaiz et al. [64]. They also used Normalized Difference Vegetation Index (NDVI) to monitor the decline in the tree cover. The carbon sequestration potential of three different aged (young, mature and over-mature) forests was investigated and the result revealed an increasing trend of live tree carbon stock from young to overmature forest of Pinus roxburghii, while there was a decreasing trend in soil organic carbon in the same stand [65]. A similar study was carried out by Amir et al. [66] and they also reported that the soil organic carbon decreases with an increase in stand age. Similarly, Yasin et al. [67] evaluated the carbon stock potential of agroforestry in three districts of Punjab using an allometric equation. The study revealed that the total carbon potential of trees was 950,470 Mg, while the estimated soil carbon was 22,743,008 Mg. The study also highlighted the possibility of increasing carbon sequestration by two-fold under managed plantation schemes.
Mannan et al. [68] examined the land-use land cover changes and estimated the carbon stock of three different forest types of the foothills of northern Pakistan’s Himalayan Mountains through remote sensing and GIS. They reported a decrease in forest cover while there was an increase in agricultural land. The calculated carbon stocks of moist temperate, subtropical chirpine, and sub-tropical broad-leaved forests were 313.94 ± 44.78 Mg C/ha, 221.34 ± 44.78 Mg C/ha and 131.77 ± 44.78 Mg C/ha, respectively. Carbon stock was estimated using two different allometric models in Olea ferruginea and Acacia modesta, where Olea ferruginea had the highest tree density and biomass [69]. It was observed that the model considering height and dbh as covariates performed better in estimating the biomass compared to the model that considered diameter at breast height only. Hammad et al. [70] investigated the carbon stock potential of different land uses and concluded that forest land stores more carbon than others. They further reported that the amount of carbon in soil decreases with increased depth. Akhtar et al. [71] have reported the above-ground biomass of the subtropical pine forest of Murree hill using different data inputs of remote sensing.
Islamabad, the capital city of Pakistan, is known as Islamabad Capital Territory (ICT). It has a forest cover of about 22.6% of its total land area. As most of the forest of Islamabad is mixed with Punjab, it is therefore included in Punjab province.
Changes in the carbon storage of Margilla Hill National Park were studied using remote sensing and GIS along with field data for 1990–2017 [72]. According to the results, the site stored 2131, 2076, 2034, and 1987 GgC for 1990, 2000, 2010, and 2017, respectively. This declining pattern was due to land-use change. Likewise, Mannan et al. [73] examined Margilla Hill National Park’s carbon content in terms of altitudinal gradient. The results revealed that the carbon content was high at a higher elevation and decreased at a lower elevation. The study area’s carbon stock for elevation 1 (ELV-1), ELV-2, ELV-3, and ELV-4 was 76.53, 157.46, 156.01, and 279.89 Mg C per ha, respectively.

2.3. Sindh

Sindh contains 6% forest cover of its total land area where two types of forests exist: mangroves (3.5%) and riverine (1.4%) [7]. Various floristic studies have been carried out in the province, but limited studies are available on carbon stock assessment.
Maguire et al. [74] have developed a yield model for Acacia nilotica plantations and reported that they could produce 40-ton dry weight/ha/year. They have followed a random sampling technique with a circular plot of 0.004 ha. They also used 0.002 ha and 0.1 ha plots for premature, thick and old, open/sparse plantations, respectively. The structure of the Avicennia marina species in mangrove forests was studied, and it was found that the basal area of the study site ranged from 0.22–4.09 with an average of 1.33 m2 per 0.1 ha [75].
Various studies were carried out to investigate the spatial and temporal changes in different forest types at different periods [76,77,78]. These studies have not covered the biomass and carbon stock aspect. Ajani and Shams [79] estimated the carbon content of two different species at the University of Karachi following non-destructive methods. They reported that Azadirachta indica and Conocarpus erectus store carbon at an average of 662.32 + 1144.81 kg and 192.70 + 322.60 kg, respectively.
Pakistan Forest Institute (2018) conducted a study to assess carbon stock in mangroves forests of Karachi, Sindh. Mean aboveground biomass carbon stock was estimated as 24.4 t/ha and belowground biomass was estimated as 10.22 t/ha. Soil organic carbon was found as 233.73 t/ha in this ecosystem. Carbon sequestration potential in biomass was determined as 9.10 t/ha/year [80]. Similarly Ali et al. (2018) reported aboveground carbon stock in riverine forests of Sukkur as 4.6 t/ha. They reported that there is a potential of 20 tCO2/ha/year in highly fertile landscape [81].
A study was conducted in Sandspit mangrove forest to estimate its carbon content in three different carbon pools. The study revealed that the total carbon content of the ecosystem was 183.99 ± 11.02 t/ha, where above-ground stored almost half of the total carbon while below-ground and sediment stored 21.19% and 28.07%, respectively [82]. The carbon potential of two species was calculated using the non-destructive method and it was found that Azadirachta indica stored more carbon than Conocarpus erectus [83]. Sindh province has consequential forest types, but unfortunately, no systematic study has yet been conducted to explore its role regarding climate change and ecosystem services.

2.4. Balochistan

Balochistan is the largest province regarding land area with less population. It has a low forest cover of 1.5% and 26.7% rangeland [7].
The structure of Juniperus excelsa in Balochistan was studied, and the results revealed that the tree density varied from 56–332 stems/ha while the basal area ranged from 8.8–152.2 m2/ha [84]. Earlier, Ahmed et al. [85] studied the vegetation structure of Pinus gerardiana forest and reported that the mean tree density was 266 trees/ha, while the average basal area was found to be 25.5 m2/ha. They have also estimated the average growth rate for this species as 0.08 cm/year. Another study was carried out to study the status of juniper forests of Balochistan by Ahmed et al. [86]. Their study revealed that the density and basal area varied from 112–469 trees/ha and 12.4–132 m2/ha, respectively.
Balochistan has a unique juniper forest ecosystem facing human intervention. Juniper forest can play a significant role in carbon sequestration in the case of sustainable management. According to the literature review, no study has been performed to estimate the carbon content of these forests. Different studies worldwide have stated that juniper forest stores a significant amount of carbon dioxide. Thus, it is necessary to assess these forests’ carbon stocks for reporting under the Kyoto Protocol.

2.5. Azad Jammu and Kashmir (AJK)

AJK is a region administered by Pakistan. It has a total land area of 1,329,729 hectares, out of which 566,740 hectares or 42.6% comprises forest cover [87].
Shaheen et al. [88] examined the total carbon stock of subtropical forest types of Kashmir, Pakistan. They reported an average carbon content of 186.27 t/ha. The Musk Deer National Park’s mean carbon stock was estimated through non-destructive techniques as 44.64 ± 12.44 Mg/ha [89]. The total average carbon content of three carbon pools (above-ground, below-ground, and soil) of alpine and sub-alpine forest ecosystems was estimated as 356.73 t/ha using destructive and non-destructive methods for herbs and trees, respectively [90]. Temporal changes in the forest cover and carbon stock of subtropical pine forest were assessed for 1989–2018 using remotely sensed data along with field inventory [91]. Total (above-ground and below-ground) biomass and carbon stock were estimated as 182 and 91 t/ha, respectively. The study also reported a decline of 11% in forest area during 1989–1999 due to legal logging of mature trees with the permission of the government of AJK, but a net increase of 561 ha in a forest area during 1999–2018 due to the ban on cutting trees and reforestation.

2.6. Gilgit Baltistan (GB)

Gilgit Baltistan, formerly known as Northern Areas, expands over an area of 72,971 km2 with 1582 km2 of forest cover [92].
Akbar et al. [93] studied the forest structure in Skardu and reported the basal area for Pinus wallichiana, Juniperus excelsa, and Betula utilis as 42.38, 14.63 and 13.12 m2/ha, respectively. The volume of the different tree species of Chilas forest was calculated, and the result shows that the average volumes of Kail, Fir, Deodar and Chalgoza were 1.92, 1.57, 0.46 and 0.291 m3/tree, respectively [94]. A study was carried out to examine the Chalghoza forest of District Diamer and the result revealed that the basal area ranged from 3.98–16.16 m2/ha [95]. An allometric equation is essential to estimate the biomass of a forest accurately. Ali et al. [96] developed an allometric equation for Cedrus deodara of dry temperate forest in northern areas by harvesting 32 trees of different diameter classes. The results revealed that the mean Biomass Expansion Factor of Cedrus deodara was 1.37 ± 0.039 for a tree with a diameter at breast height greater than 20 cm, while the basic wood density was found to be 0.46 g/cm3. They also reported that bole, branches and brushwood contributed to total dry biomass as 72.95, 10.43, and 16.62%, respectively. Forest inventory was performed in the forests of Gilgit Baltistan, and the total above-ground biomass was estimated using an allometric model as 12,887 tons [92].
Carbon stock was estimated as a regulatory ecosystem service while assessing the ecosystem services of oak forest [97]. The results revealed that the mean above-ground biomass of the study area was 72.46 t/ha, while the mean carbon stock was found to be 34.06 t/ha. An allometric equation for naturally occurring Cedrus deodara species was developed through destructive sampling for 60 trees at different sites [98].
Carbon stock studies conducted in Pakistan at different time intervals are shown in Figure 2. Figure 2 explains only above-ground biomass and carbon stock studies, excluding studies related to other carbon pools or basal area and volume. Studies regarding above-ground biomass and carbon stock are presented province wise with current status in the following Table 3. We generally overviewed the present status of each forest type following published materials.
The literature review was carried out during October 2020 to December 2020 using Google Scholar, Scopus, government reports, as well as Ministry of Climate Change reports to obtain information from current studies on forest types, biomass, carbon stock and basal area of various forest types of Pakistan.
Various keywords such as forest types of Pakistan, forest biomass, carbon stock estimation, allometric equation, carbon stock of agroforestry, plantations, forest inventory and forest structure were searched. To collect more information of the previous studies, forest type wise (e.g., biomass/carbon stock of moist temperate forest) and territory wise (e.g., forest types of Sindh) searches were also carried out. This study’s only objective is to highlight the current state of knowledge with the help of a literature review.

3. Methods Used for Carbon Stock Estimation

Accurate forest biomass assessment through forest inventory is essential for various applications such as temporal changes in biomass/carbon stock and global carbon cycle. Forest inventory includes tree enumeration, volume, species composition, size, age, longevity and potential growth. Monitoring and assessing biomass distribution in forests is a primary requirement for implementing REDD+. Accurate analysis of biophysical attributes of the plantation is essential for biomass calculation in forests [103].
Forest biomass can be estimated through ground survey and remote sensing. Field measurements can be divided into two groups: destructive and non-destructive sampling. The destructive method involves harvesting all trees and measuring different parts’ biomass [104]. Nonetheless, this method is considered useful only for a small area since it is time consuming and labor intensive [105,106]. However, it can be used for developing a biomass equation for large forest areas [107,108].
Non-destructive sampling methods involve various forest attribute measurements such as diameter at breast height (dbh) and height [109] and biomass/carbon stock assessment through allometric equations [3]. Allometric models are developed using forest inventory data [43,108,110].
Remote sensing and Geographic Information System (GIS) is a new method for forest biomass estimation. Various studies have been carried out for calculating forest biomass with the help of remote sensing [33,34,42]. Nonetheless, biomass calculation through field data is crucial for the calibration of remote sensing models and verifying results for remote sensing methodologies. Tree allometric equations of the same species and the same ecological conditions are more reliable and precise for forest biomass and carbon stock estimation [2]. Methods used for above-ground biomass estimation in Pakistan are given in Figure 3.
Allometric equations have been developed for some major species existing in Pakistan (Table 4).
Besides allometric models, there are tree volume allometric equations developed for some forest species which can be used to calculate forest stand volume. The details of location and species are given in Table 5.

4. Research Needs and Future Directions

At a national level, the first study was carried out in 1992, which has not been repeated to this day. The availability of data is a requirement for good management and monitoring of resources. Current data are less reliable, and therefore it is suggested that an extensive study should be conducted on a country level for each forest type, which can be compared to the previous study conducted back in 1992 at a national scale.
The past decade (2010–2020) can be considered as a good era for forest biomass estimation research. There are few studies available on allometric equations for biomass estimation. Allometric models have mostly been developed for some of the major species in KP and GB (coniferous and broad leaved), which is not the case for other provinces. For accurate and precise carbon stock estimation, it is imperative to develop allometric equations for all major species found in Pakistan’s various ecological conditions. It is essential to understand that allometric equations can only be applied for the same species with the same ecological conditions for accurate carbon stock estimation. An allometric equation of monospecific forests can yield biased results when used for mixed forests [111]. Many studies in monocultures have stated that stand density and resource availability affect allometry [112,113,114], which can vary significantly between pure stand and mixed forests [115].
Allometric equations have been developed for coniferous species, but they still need to be developed for each ecological zone. For example, if the allometric model is developed for Cedrus deodara for dry temperate forests, it is necessary to develop it for the same species in the moist temperate forest. Furthermore, it has been revealed with the help of the literature review that a regional study for forest reference emission level is only available for one province i.e., Khyber Pakhtunkhwa, while the rest of the provinces/territories have not yet attempted to fill this gap. To obtain a benefit from REDD+, a country’s forest reference emission level at a national or subnational level is necessary.
Mangroves are one of the most important forest ecosystems in Pakistan, but unfortunately, the biomass and carbon stock data of this forest are not available. Furthermore, the specific allometric equation is not yet developed. Studies show that the forest cover of mangroves has been increased during the past few years [116], but its biomass and carbon stock are yet to be evaluated. Riverine forest is another important forest ecosystem in Sindh and Punjab. While temporal and spatial changes have been monitored for riverine forests, the carbon stock has not been studied systematically. It is one of the most productive forests in Sindh, and it is vital to estimate the biomass and carbon stock of this ecosystem to report under REDD+.
Similarly, Balochistan province has a centuries-old juniper forest, but its carbon stock potential has not been assessed up till now. It is a slow-growing forest and considered one of the world’s largest and oldest forests globally. Punjab has about half of the area of agricultural land, which means that agroforestry contains a significant amount of carbon, but unfortunately, it has not been given that much importance in this context. It is similar to the case with Sindh, which has about one-third of the agricultural land area. Therefore, it is essential to highlight and promote agroforestry and its services. Pakistan is a signatory to the Kyoto Protocol and needs to evaluate the carbon stock of various forest ecosystems that exist in the country. For the assessment of carbon stocks and carbon change, the permanent sample plot technique should be followed in the future across Pakistan’s different forest types.

5. Conclusions

Forests are important in the context of climate change as they act as a sink and a source of carbon emissions. Tropical forests are more crucial in this regard as they have more potential to remove carbon from the atmosphere and have a role in regulating the surface temperature. Data available on above-ground biomass and carbon stock in Pakistan are less reliable and fragmented. The only scientific study on a national scale was conducted back in 1992. Therefore, there is an urgent need to update forest inventory on a national scale.
This research attempts to review all studies in Pakistan conducted on the above-ground biomass and carbon stock of forest types, plantations and agroforestry. The methods used for carbon stock estimation were categorized as destructive and non-destructive. The destructive method is not commonly used due to the strict environmental protection law but is only used in a small area to develop an allometric model or biomass expansion factor for accurate carbon stock estimation. In contrast, the non-destructive method is used by measuring different tree parameters, such as diameter at 1.37 m, height of the tree, and tree height, to calculate the above-ground biomass. Remote sensing is also used for carbon stock estimation, but it also needs field data for validation.
Coniferous forests seem to store more carbon compared to other forest types. Most of these forests are young and therefore have a high potential for carbon sequestration and climate change mitigation under REDD+ and other carbon trading schemes. Khyber Pakhtunkhwa province has more comprehensive data on carbon stocks and allometric models than other provinces of the country. Allometric equations were used for carbon stock estimation, but they may not give an accurate calculation in other areas due to the different ecological conditions. Therefore, it is necessary to develop an allometric model of notable species from various habitats and conditions.
This review can be used for generating a database that could be helpful for decision-makers primarily related to climate change mitigation and forest conservation.

Author Contributions

M.N. and W.R.K. originated the concept of the paper and critically revised the whole paper. A.A. provided the resources for this project and revised the final draft. I.A.K. performed the literature search and drafted the paper. All authors took part in writing the review and the final manuscript was also read and approved by them. All authors have read and agreed to the published version of the manuscript.

Funding

The authors are grateful to University Putra Malaysia (UPM) for providing financial support to conduct this study.

Data Availability Statement

As this is a review paper, the data availability is limited to that which is published in the source material.

Acknowledgments

Firstly, we would like to thank the University Putra Malaysia (UPM) for financial support of this study. Secondly, we would like to thank Anwar Ali and Waseem Razzaq Khan for their support, guidance and productive discussion. We would also like to extend our gratitude to Mohd Nazre for his support, guidance and cooperation during all stages of the development of the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. McCarthy, J.J.; Canziani, O.F.; Leary, N.A.; Dokken, D.J.; White, K.S. Climate Change 2001: Impacts, Adaptation, And Vulnerability; Cambridge University Press: Cambridge, UK, 2001. [Google Scholar]
  2. Salunkhe, O.; Khare, P.K.; Kumari, R.; Khan, M.L. A systematic review on the aboveground biomass and carbon stocks of Indian forest ecosystems. Ecol. Process. 2018, 7, 17. [Google Scholar] [CrossRef]
  3. Yavasli, D.D. Recent approaches in above ground biomass estimation methods To prak Üstü Biyokütle Belirlemede Güncel Yakla ş ı mlar Do ğ ukan Do ğ u YAVA Ş LI Öz The Importance of Biomass for the Carbon Cycle. Aegean Geogr. J. 2012, 21, 39–51. [Google Scholar]
  4. Nizami, S.M. Estimation of Carbon Stocks in Subtropical Managed and Unmanaged Forests of Pakistan. Ph.D. Thesis, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan, 2010. [Google Scholar]
  5. Ali, A.; Ashraf, M.I.; Gulzar, S.; Akmal, M. Estimation of forest carbon stocks in temperate and subtropical mountain systems of Pakistan: Implications for REDD + and climate change mitigation. Environ. Monit. Assess. 2020, 192, 198. [Google Scholar] [CrossRef] [PubMed]
  6. Ali, A.; Ayaz, M.; Muhammad, S. A study of stand structure of temperate forests of kaghan valley, mansehra, khyber pakhtunkhwa. Pak. J. For. 2017, 67, 1–15. [Google Scholar]
  7. Bukhari, S.S.B.; Haider, A.; Laeeq, M.T. Land Use Atlas of Pakistan; Pakistan Forest Institute: Peshawar, Pakistan, 2012.
  8. Nizami, S.M. The inventory of the carbon stocks in sub tropical forests of Pakistan for reporting under Kyoto Protocol. J. For. Res. 2012, 23, 377–384. [Google Scholar] [CrossRef]
  9. FAO. Brief on National Forest Inventory NFI-Pakistan; FAO: Rome, Italy, 2007. [Google Scholar]
  10. Murthy, M.; Wesselman, S.; Gilani, H. Multi-Scale Forest Biomass Assessment and Monitoring in the Hindu Kush Himalayan Region: A Geospatial Perspective; ICIMOD Publication: Kathmandu, Nepal, 2015. [Google Scholar]
  11. Ali, A. Forest Reference Emission Level of Khyber Pakhtunkhwa; Pakistan Forest Institute: Peshawar, Pakistan, 2017.
  12. Sheikh, M.I. Trees of Pakistan; Pakistan Forest Institute: Peshawar, Pakistan, 1993.
  13. Ullah, S.; Farooq, M.; Shafique, M.; Siyab, M.A.; Kareem, F.; Dees, M. Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan. J. Mt. Sci. 2016, 13, 1229–1237. [Google Scholar] [CrossRef]
  14. Zeb, A. Spatial and temporal trends of forest cover as a response to policy interventions in the district Chitral, Pakistan. Appl. Geogr. 2019, 102, 39–46. [Google Scholar] [CrossRef]
  15. Khan, A.; Ahmed, M.; Siddiqi, M.F.; Shah, M.; Calixto, E.S.; Khan, A.; Shah, P.; Iqbal, J.; Azeem, M. Vegetation-environment relationship in conifer dominating forests of the mountainous range of Indus Kohistan in northern Pakistan. J. Mt. Sci. 2020, 17, 1989–2000. [Google Scholar] [CrossRef]
  16. Ahmed, M.; Khan, N.; Wahab, M.; Hamza, S.; Siddiqui, M.F.; Nazim, K.; Khan, M.U. Vegetation structure of Olea ferruginea royle forests of lower dir district of Pakistan. Pak. J. Bot. 2009, 41, 2683–2695. [Google Scholar]
  17. Khan, N.; Wahab, M.; Nazim, K.; Ajaib, M. Phytosociology, structure and physiochemical analysis of soil in quercus baloot griff, forest district chitral, PAKISTAN. Pak. J. Bot. 2010, 42, 2429–2441. [Google Scholar]
  18. Khan, N.; Ahmed, M.; Shaukat, S.S.; Wahab, M.; Siddiqui, M.F. Structure, diversity, and regeneration potential of Monotheca buxifolia (Falc.) A. DC. dominated forests of Lower Dir District, Pakistan. Front. Agric. China 2011, 5, 106–121. [Google Scholar] [CrossRef]
  19. Alam, K.; Nizami, S.M. Assessing Biomass Expansion Factor of Birch Tree Betula utilis D. DON. Open J. For. 2014, 4, 181–190. [Google Scholar]
  20. Ahmad, A.; Mirza, S.N.; Nizami, S.M. Assessment of biomass and carbon stocks in coniferous forest of dir kohistan, KPK. Pak. J. Agric. Sci. 2014, 51, 335–340. [Google Scholar]
  21. Iqbal, J.; Ahmed, M.; Khan, A.; Siddiqui, M.F. Vegetation Description of Some Pine Forests of Shangla District of Khyber Pakhtunkhwa Pakistan. A Preliminary Study. FUUAST J. Biol. 2014, 4, 83–88. [Google Scholar]
  22. Ahmad, S.; Ahmad, A.; Nizami, S.M. Assessment of Biomass Expansion Factor of Picea Smithiana (Wall) Boiss. Int. J. Sci. Eng. Res. 2014, 5, 1232–1239. [Google Scholar]
  23. Khan, M.S.; Shah, W.; Hussain, A.; Naz, A.; Masaud, S. Predicting the present growing stock for the next ten years keeping in view the dependency of local community in karaker forest swat. J. Sci. Technol. Univ. Peshawar 2015, 39, 37–55. [Google Scholar]
  24. Munawar, S.; Fahim, M.; Atif, S. International Biodeterioration & Biodegradation Reducing emissions from deforestation and forest degradation implementation in northern Pakistan. Int. Biodeterior. Biodegrad. 2015, 102, 316–323. [Google Scholar] [CrossRef]
  25. Khan, A.; Ahmad, A.; Rahman, Z.; Uddin, S.; Qureshi, R.; Muhammad, J. The Assessment of Carbon Stocks in the Oak Scrub Forest of Sheringal Valley Dir Kohistan. Open J. For. 2015, 5, 510–517. [Google Scholar] [CrossRef] [Green Version]
  26. Khan, N.; Ali, F.; Ali, K.; Shaukat, S. Composition, Structure and Regeneration Dynamics of Olea ferruginea Royle Forests from Hindukush Range of Pakistan. J. Mt. Sci. 2015, 12, 647–658. [Google Scholar]
  27. Ahmad, A.; Moazzam, S. Carbon stocks of different land uses in the Kumrat valley, Hindu Kush Region of Pakistan. J. For. Res. 2015, 26, 57–64. [Google Scholar] [CrossRef]
  28. Ahmad, A.; Nizami, S.M.; Marwat, K.B.; Muhammad, J. Annual accumulation of carbon in the coniferous forest of Dir Kohistan: An inventorybased estimate. Pak. J. Bot. 2016, 47, 115–118. [Google Scholar]
  29. Khan, A.; Ahmed, M.; Siddiqui, M.F.; Iqbal, J.; Wahab, M. phytosociological analysis of pine forest at indus kohistan, kpk, Pakistan. Pak. J. Bot. 2016, 48, 575–580. [Google Scholar]
  30. Naeem, S.; Ghauri, B.; Shahzad, A.; Shaukat, S.S. Estimation of aboveground forest biomass using geospatial techniques in murree and abbottabad areas, pakistan. Int. J. Biol. Biotechnol. 2017, 14, 203–213. [Google Scholar]
  31. Ali, F.; Khan, N.; Ali, K.; Khan, I. Influence of environmental variables on the distribution of woody species in muslim graveyards of malakand division, hindukush range mountains of pakistan. Pak. J. Bot. 2017, 49, 2357–2366. [Google Scholar]
  32. Ahmad, A.; Liu, Q.; Nizami, S.M.; Mannan, A.; Saeed, S. Carbon emission from deforestation, forest degradation and wood harvest in the temperate region of Hindukush Himalaya, Pakistan between 1994 and 2016. Land Use Policy 2018, 78, 781–790. [Google Scholar] [CrossRef]
  33. Imran, A.B.; Ahmed, S. Potential of Landsat-8 spectral indices to estimate forest biomass. Int. J. Hum. Cap. Urban Manag. 2018, 3, 303–314. [Google Scholar] [CrossRef]
  34. Ali, A.; Ullah, S.; Bushra, S.; Ahmad, N.; Ali, A.; Khan, M.A. Quantifying forest carbon stocks by integrating satellite images and forest inventory data. Austrian J. For. Sci. 2018, 135, 93–117. [Google Scholar]
  35. Ahmad, A.; Amir, M.; Mannan, A.; Saeed, S.; Shah, S.; Ullah, S.; Uddin, R.; Liu, Q. The Carbon Sinks and Mitigation Potential of Deodar (Cedrus deodara) Forest Ecosystem at Different Altitude in Kumrat Valley, Pakistan. Open J. For. 2018, 8, 553–566. [Google Scholar] [CrossRef] [Green Version]
  36. Khan, I.A.; Ali, A.; Hussain, M.; Ullah, H.; Urrehman, A.; Ali, N.; Khattak, M.A. Tree biomass and carbon estimation of Pinus roxburghii and Eucalyptus camaldulensis. J. Biodivers. Environ. Sci. 2018, 13, 8–16. [Google Scholar]
  37. Shahzad, L.; Tahir, A.; Sharif, F.; Mukhtar, H. Assessing the impacts of changing climate on forest ecosystem services and livelihood of Balakot mountainous communities. Pak. J. Bot. 2019, 51, 51. [Google Scholar] [CrossRef]
  38. Ali, F.; Khan, N.; Ahmad, A.; Abdullah, A. Structure and biomass carbon of Olea ferruginea forests in the foot hills of Malakand division, Hindukush range mountains of Pakistan. Acta Ecol. Sin. 2019, 39, 261–266. [Google Scholar] [CrossRef]
  39. Uddin, R.; Ansari, L.; Ahmad, A.; Ullah, S.; Munir, M.; Sher, K. Soil analysis, species composition and carbon dynamics of abies pindrow forest of dir kohistan, Pakistan. Appl. Ecol. Environ. Res. 2019, 17, 8049–8058. [Google Scholar] [CrossRef]
  40. Ahmad, A.; Liu, Q.I.J.; Marwat, K.B.; Shah, S.; Amir, M.; Manan, A. Tree distribution pattern, growing stock characteristics and carbon mitigation potential of different forests ecosystems in kumrat, hindukush region of northern Pakistan. Pak. J. Bot. 2019, 51, 2185–2194. [Google Scholar] [CrossRef]
  41. Shah, S.; Saleem, A.; Gulzar, S.; Mehmood, A. Agroforestry practices and its contribution to combat climate change in subtropical region of Pakistan. Pure Appl. Biol. 2020, 9, 376–389. [Google Scholar] [CrossRef]
  42. Khan, K.; Iqbal, J.; Ali, A.; Khan, S.N. assessment of sentinel-2-derived vegetation indices for the estimation of above-ground biomass/carbon stock, temporal deforestation and carbon emissions estimation in the moist temperate forests of Pakistan. Appl. Ecol. Environ. Res. 2020, 18, 783–815. [Google Scholar] [CrossRef]
  43. Ali, A.; Ashraf, M.I.; Gulzar, S.; Akmal, M. Development of an Allometric Model for Biomass Estimation of Pinus roxburghii, Growing in Subtropical Pine Forests of Khyber Pakhtunkhwa, Pakistan. Sarhad J. Agric. 2020, 36, 236–244. [Google Scholar] [CrossRef] [Green Version]
  44. Khan, A.U. History of decline and present status of natural tropical thorn forest in Punjab. Biol. Conserv. 1994, 67, 205–210. [Google Scholar] [CrossRef]
  45. Ali, G.; Nitivattananon, V. Exercising multidisciplinary approach to assess interrelationship between energy use, carbon emission and land use change in a metropolitan city of Pakistan. Renew. Sustain. Energy Rev. 2012, 16, 775–786. [Google Scholar] [CrossRef]
  46. Khan, W.R.; Khokhar, M.F.; Munawar, S.; Panhwar, Q.A. Assessing the Context of Redd+ in Muree Hill Forest, Pakistan. Adv. Environ. Biol. 2015, 9, 15–20. [Google Scholar]
  47. Nizami, S.; Mirza, S.; Livesley, S.; Arndt, S.; Fox, J.C.; Khan, I.A.; Mahmood, T. Estimating carbon stocks in sub-tropical pine (Pinus roxburghii) forests of Pakistan. Pak. J. Agric. Sci. 2009, 46, 266–270. [Google Scholar]
  48. Abbas, M.; Nizami, S.M.; Saleem, A.; Gulzar, S.; Khan, I.A. Biomass expansion factors of Olea ferruginea (Royle) in sub tropical forests of Pakistan. Afr. J. Biotechnol. 2011, 10, 1586–1592. [Google Scholar] [CrossRef]
  49. Hashmi, M.M.; Habib, M.; Abbasi, U.A.; Tariq, R.S. Assessment of Carbon Stocks and Biodiversity in Karore Forest, Rawalpindi-North, Pakistan. Eur. Acad. Res. 2014, 2, 7490–7498. [Google Scholar]
  50. Gulzar, S.; Nizami, S.M.; Saleem, A.; Mirza, S.N.; Mahmood, T. Allometric relationships for predicting the biomass in dalbergia sissoo ROXB. plantation in Punjab, Pakistan. Pak. J. Agric. Sci. 2014, 51, 525–529. [Google Scholar]
  51. Hashmi, M.; Sumble, K. Estimation of Wood Volumes in Karore Forest, Pakistan. Eur. Acad. Res. 2015, 2, 13107–13112. [Google Scholar]
  52. Saeed, S.; Irfan Ashraf, M.; Ahmad, A.; Rahman, Z. The bela forest ecosystem of district jhelum, a potential carbon sink. Pak. J. Bot. 2016, 48, 121–129. [Google Scholar]
  53. Ali, A. Carbon Stock Assessment in Scrub Forest of Chakwal; Pakistan Forest Institute: Peshawar, Pakistan, 2018.
  54. Ali, A. Progress Report of Sustainable Forest Management Project; Pakistan Forest Institute: Peshawar, Pakistan, 2019.
  55. Mahmood, K.; Chughtai, M.I.; Awan, A.R.; Waheed, R.A. Biomass production of some salt tolerant tree species grown in different ecological zones of Pakistan. Pak. J. Bot. 2016, 48, 89–96. [Google Scholar]
  56. Tariq, H.; Moazzam, N.S.; Ansari, L.; Naeem, I.; Asif, T.; Raqeeb, A.; Kausar, R. Litter fall quantity, root density, biomass and decomposition rate in Dalbergia sissoo: A nutrient cycling perspective. J. Biodivers. Environ. Sci. 2017, 10, 38–49. [Google Scholar]
  57. Baig, S.; Qazi, W.A.; Akhtar, A.M.; Waqar, M.M.; Ammar, A.; Gilani, H.; Mehmood, S.A. Above Ground Biomass Estimation of Dalbergia sissoo Forest Plantation from Dual-Polarized ALOS-2 PALSAR Data. Can. J. Remote Sens. 2017, 43, 297–308. [Google Scholar] [CrossRef]
  58. Akram, B.; Abbas, F.; Ibrahim, M.; Nawaz, M.F.; Zahra, S.I.; Salik, M.R.; Hammad, H.M. Above-ground carbon pools of Citrus acreage in Pakistan. J. Anim. Plant Sci. 2017, 27, 1903–1908. [Google Scholar]
  59. Nawaz, M.F.; Yousaf, M.T.B.; Yasin, G.; Gul, S.; Ahmed, I.; Abdullah, M.; Rafay, M.; Tanvir, M.A.; Asif, M.; Afzal, S. Agroforestry status and its role to sequester atmospheric CO2 under semi-arid climatic conditions in Pakistan. Appl. Ecol. Environ. Res. 2017, 16, 645–661. [Google Scholar] [CrossRef]
  60. Nawaz, M.F.; Shah, S.A.A.; Gul, S.; Afzal, S.; Ahmad, I.; Ghaffar, A. Carbon sequestration and production of eucalyptus camaldulensis plantations on marginal sandy agricultural lands. Pak. J. Agric. Sci. 2017, 54, 335–342. [Google Scholar] [CrossRef]
  61. Arif, M.; Shahzad, M.K.; Elzaki, E.; Elzaki, A.; Hussain, A.; Zhang, B.; Yukun, C. Biomass and Carbon Stocks Estimation in Chichawatni Irrigated Plantation in Pakistan. Int. J. Agric. Biol. 2017, 19, 1339–1349. [Google Scholar] [CrossRef]
  62. Nawaz, M.F.; Mazhar, K.; Gul, S.; Ahmad, I.; Yasin, G.; Asif, M.; Tanvir, M. Comparing the Early Stage Carbon Sequestration Rates and Effects on Soil Physico-Chemical Properties after Two Years of Planting Agroforestry Trees. J. Basic Appl. Sci. 2017, 13, 527–533. [Google Scholar]
  63. Yasin, G.; Nawaz, M.F.; Siddiqui, M.T.; Niazi, N.K. Biomass, carbon stocks and CO2 sequestration in three different aged irrigated populus deltoides bartr. Ex marsh. bund planting agroforestry systems. Appl. Ecol. Environ. Res. 2018, 16, 6239–6252. [Google Scholar] [CrossRef]
  64. Pervaiz, S.; Shirazi, S.A.; Khan, F.Z.; Javid, K.; Mughal, T.A. Tree census of urban green space with special reference to Gora Cemetery of Lahore, Pakistan. Int. J. Biosci. 2018, 13, 431–439. [Google Scholar] [CrossRef]
  65. Amir, M.; Liu, X.; Ahmad, A.; Saeed, S.; Mannan, A.; Muneer, M.A. Patterns of Biomass and Carbon Allocation across Chronosequence of Chir Pine (Pinus roxburghii) Forest in Pakistan: Inventory-Based Estimate. Adv. Meterology 2018, 2018, 1–8. [Google Scholar] [CrossRef] [Green Version]
  66. Amir, M.; Saeed, S.; Liu, X.D.; Mannan, A.; Khan, A.; Li, Z.Z.; Muneer, M.A. Soil organic content of stands of different ages in a subtropical chir pine (Pinus Roxburghii) forest of Pakistan. Appl. Ecol. Environ. Res. 2019, 17, 11475–11487. [Google Scholar] [CrossRef]
  67. Yasin, G.; Nawaz, M.F.; Martin, T.A.; Niazi, N.K.; Gul, S.; Yousaf, M.T. Bin Evaluation of agroforestry carbon storage status and potential in irrigated plains of Pakistan. Forests 2019, 10, 640. [Google Scholar] [CrossRef] [Green Version]
  68. Mannan, A.; Liu, J.; Zhongke, F.; Khan, T.U.; Saeed, S.; Mukete, B.; ChaoYong, S.; Yongxiang, F.; Ahmad, A.; Amir, M.; et al. Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan. Glob. Ecol. Conserv. 2019, 17, e00535. [Google Scholar] [CrossRef]
  69. Ghafoor, G.Z.; Sharif, F.; Khan, A.U.; Shahzad, L.; Hayyat, M.U. Assessment of tree biomass carbon stock of a subtropical scrub forest, Soan Valley Pakistan. Appl. Ecol. Environ. Res. 2020, 18, 2231–2245. [Google Scholar] [CrossRef]
  70. Hammad, H.M.; Nauman, H.M.F.; Abbas, F.; Ahmad, A.; Bakht, H.F.; Saeed, S.; Shah, G.M.; Ahmad, A.; Cerda, A. Carbon sequestration potential and soil characteristics of various land use systems in arid region. J. Environ. Manag. 2020, 264, 110254. [Google Scholar] [CrossRef]
  71. Akhtar, A.M.; Qazi, W.A.; Ahmad, S.R.; Gilani, H.; Mahmood, S.A.; Rasool, A. Integration of high-resolution optical and SAR satellite remote sensing datasets for aboveground biomass estimation in subtropical pine forest, Pakistan. Environ. Monit. Assess. 2020, 192, 1–17. [Google Scholar] [CrossRef]
  72. Mannan, A.; Feng, Z.; Ahmad, A.; Liu, J.; Saeed, S.; Mukete, B. Carbon dynamic shifts with land use change in Margallah Hills National Park, Islamabad (PAKISTAN) from 1990 to 2017. Appl. Ecol. Environ. Res. 2018, 16, 3197–3214. [Google Scholar] [CrossRef]
  73. Mannan, A.; Zhongke, F.; Khan, T.U.; Khan, M.A.; Badshah, M.T. Variation in tree biomass and carbon stocks with respect to altitudinal gradient in the Himalayan forests of Northern Pakistan. J. Pure Appl. Agric. 2019, 4, 21–28. [Google Scholar]
  74. Maguire, D.A.; Schreuder, G.F.; Shaikh, M. A biomass/yield model for high-density Acacia nilotica plantations in Sind, Pakistan. For. Ecol. Manag. 1990, 37, 285–302. [Google Scholar] [CrossRef]
  75. Saifullah, S.M.; Shaukat, S.S.; Shams, S. Population structure and dispersion pattern in mangroves of Karachi, Pakistan. Aquat. Bot. 1994, 47, 329–340. [Google Scholar] [CrossRef]
  76. Siddiqui, M.N.; Jamil, Z.; Afsar, J. Monitoring changes in riverine forests of Sindh-Pakistan using remote sensing and GIS techniques. Adv. Sp. Res. 2004, 33, 333–337. [Google Scholar] [CrossRef]
  77. Siyal, A.A.; Siyal, A.G.; Mahar, R.B. Spatial and temporal dynamics of Pai forest vegetation in Pakistan assessed by RS and GIS. J. For. Res. 2016, 28, 593–603. [Google Scholar] [CrossRef]
  78. Qasim, H.; Luqman, M.; Khan, S. A study of forest land cover changes using satellite remote sensing in thatta district Pakistan. Sci. Int. 2016, 28, 2445–2450. [Google Scholar]
  79. Ajani, A.; Shams, Z.I. Comparative Status of Sequestered Carbon Stock of Azadirachta indica and Conocarpus erectus at the University of Karachi Campus, Pakistan. Int. J. Environ. 2016, 5, 89–97. [Google Scholar] [CrossRef] [Green Version]
  80. Annon. REDD+PES Design Document for Mangroves Ecosystem; Pakistan Forest Instittue and Ministry of Climate Change: Islamabad, Pakistan, 2018.
  81. Ali, A.; Khan, A.; Marwat, A.K. Estimation of carbon stock and sequestration potential in riverine forest of Sukkur. Pak. J. Forest 2018, 68, 23–32. [Google Scholar]
  82. Nazim, K.; Ahmed, M.; Khan, M.U.; Durrani, A.T.H.; Khokhar, A. Carbon Squestration Potential of Sandspit Backwater Mangrove and Sediment, Pakistan. FUUAST J. Biol. 2018, 8, 209–215. [Google Scholar]
  83. Qureshi, M.A.; Pirzada, A.M.; Qureshi, M.M.; Samoon, N.A.; Zuberi, M.H.; Khurshid, O. Comparative Study of Sequestered Carbon Stock of different species at Sindh Madressatul Islam University Karachi, Sindh, Pakistan. Sindh Univ. Res. J. 2019, 51, 91–96. [Google Scholar] [CrossRef]
  84. Ahmed, M.; Shaukat, S.S.; Buzdar, A.H. Population structure and dynamics of Juniperus excelsa in Balouchistan, Pakistan. J. Veg. Sci. 1990, 1, 271–276. [Google Scholar] [CrossRef]
  85. Ahmed, M.; Ashfaq, M.; Amjad, M.; Saeed, M. Vegetation structure and dynamics of Pinus gerardiana forests in Balouchistan, Pakistan. J. Veg. Sci. 1991, 2, 119–124. [Google Scholar] [CrossRef]
  86. Ahmed, M.; Siddiqui, M.F.; Ahmed, A.; Sarangzai, A.M.; Abbas, T.; Hussain, M.I. Present state and multivariate analysis of a few juniper forests of Baluchistan, Pakistan. Pak. J. Bot. 2015, 47, 135–140. [Google Scholar]
  87. Forestry, Wildlife & Fisheries Department. Available online: https://forest.ajk.gov.pk/ (accessed on 18 January 2021).
  88. Shaheen, H.; Khan, R.W.A.; Hussain, K.; Ullah, T.S.; Nasir, M.; Mehmood, A. Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot. 2016, 48, 2351–2357. [Google Scholar]
  89. Qasim, M. Forest carbon stock assessment of the Musk Deer National Park, Azad Jammu andKashmir (AJK). J. Trop. For. Environ. 2017, 7, 121–127. [Google Scholar]
  90. Aziz, S.; Chughtai, F.M.; Shaheen, H.; Khan, R.W.A.; Dar, M.E. ul I. Biomass and soil carbon stocks assessment in western himalayan alpine and subalpine vegetation zones of Kashmir. Pak. J. Bot. 2019, 51, 973–978. [Google Scholar] [CrossRef] [Green Version]
  91. Khan, I.A.; Khan, M.R.; Baig, M.H.A.; Hussain, Z.; Hameed, N.; Khan, J.A. 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. PLoS ONE 2020, 15, e0226341. [Google Scholar] [CrossRef] [Green Version]
  92. Ismail, I.; Sohail, M.; Gilani, H.; Ali, A.; Hussain, K.; Hussain, K.; Karky, B.S.; Qamer, F.M.; Qazi, W.; Ning, W.; et al. Forest inventory and analysis in Gilgit-Baltistan: A contribution towards developing a forest inventory for all Pakistan. Int. J. Clim. Chang. Strateg. Manag. 2018, 10, 616–631. [Google Scholar] [CrossRef] [Green Version]
  93. Akbar, M.; Ahmed, M.; Afzal, M. Phytosociology and structure of some forests of Skardu District of Karakorum range of Pakistan. Am. J. Agric. Environ. Sci. 2010, 9, 576–583. [Google Scholar]
  94. Raqeeb, A.; Nizami, S.M.; Saleem, A.; Hanif, M. Characteristics and Growing Stocks Volume of Forest Stand in Dry Temperate Forest of Chilas Gilgit-Baltistan. Open J. For. 2014, 4, 231–238. [Google Scholar] [CrossRef]
  95. Khan, H.; Akbar, M.; Zaman, M.; Ali, A.; Hyder, S.; Khan, M.; Nafees, M.A.; Raza, G.; Begum, F.; Hussain, S.A.; et al. Diameter size class distributions of Pinus gerardiana Wall. ex D. Don from Gohar Abad Valley district Diamer, Gilgit-Baltistan, Pakistan. J. Biodivers. Environ. Sci. 2015, 6, 50–56. [Google Scholar]
  96. Ali, A.; Iftikhar, M.; Ahmad, S.; Muhammad, S.; Khan, A. Development of allometric equation for biomass estimation of Cedrus deodara in dry temperate forests of Northern Pakistan. J. Biodivers. Environ. Sci. 2016, 9, 43–50. [Google Scholar]
  97. Afzal, M.; Ali, A.; Binte-Imran, A.; Ahmad, N. Assessment of ecosystem services of oak forests, Tangir valley, district Diamer, Pakistan. J. Pure Appl. Agric. 2020, 5, 116–128. [Google Scholar]
  98. Raqeeb, A.; Nizami, S.M.; Saleem, A.; Ansari, L.; Gulzar, S.; Ali, B.; Saleem, M. Biomass Allometric Equations of Naturally Growing Cedrus deodara in Dry Temperate Forests Ecosystems. Pak. J. Agric. Res. 2020, 33, 498–507. [Google Scholar] [CrossRef]
  99. Ali, A. Biomass and Carbon Tables for Major Tree Species of Khyber Pakhtunkhwa; Pakistan Forest Institute: Peshawar, Pakistan, 2020.
  100. Forest, Wildlife & Fishries Department. Available online: https://fwf.punjab.gov.pk/ (accessed on 15 February 2021).
  101. SINDH FOREST. Available online: https://sindhforests.gov.pk/ (accessed on 15 February 2021).
  102. Resource Information—Forest and Wildlife. Available online: https://bfwd.gob.pk/resource-information/ (accessed on 15 February 2021).
  103. Avtar, R.; Suzuki, R.; Sawada, H. Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data. PLoS ONE 2014, 9, e86121. [Google Scholar] [CrossRef] [PubMed]
  104. Gibbs, H.K.; Brown, S.; Niles, J.O.; Foley, J.A. Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environ. Res. Lett. 2007, 2, 045023. [Google Scholar] [CrossRef]
  105. Kumar, L.; Mutanga, O. Remote Sensing of Above-Ground Biomass. Remote Sens. 2017, 9, 935. [Google Scholar] [CrossRef] [Green Version]
  106. Iqbal, K.; Hussain, A.; Negi, A.K. An overview of Biomass Estimation methods. Res. J. Soc. Sci. Manag. 2014, 4, 42–57. [Google Scholar]
  107. Segura, M.; Kanninen, M. Allometric Models for Tree Volume and Total Aboveground Biomass in a Tropical Humid Forest in Costa Rica. Biotropica 2005, 37, 2–8. [Google Scholar] [CrossRef]
  108. Návar, J. Allometric equations for tree species and carbon stocks for forests of northwestern Mexico. For. Ecol. Manag. 2009, 257, 427–434. [Google Scholar] [CrossRef]
  109. Montes, N.; Gauquelin, T.; Badri, W.; Bertaudie, V.; Zaoui, E.H. A non-destructive method for estimating above-ground forest biomass in threatened woodlands. For. Ecol. Manag. 2000, 130, 37–46. [Google Scholar] [CrossRef]
  110. Basuki, T.M.; Van Laake, P.E.; Skidmore, A.K.; Hussin, Y.A. Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests. For. Ecol. Manag. 2009, 257, 1684–1694. [Google Scholar] [CrossRef]
  111. Forrester, D.I.; Pretzsch, H. Tamm Review: On the strength of evidence when comparing ecosystem functions of mixtures with monocultures. For. Ecol. Manag. 2015, 356, 41–53. [Google Scholar] [CrossRef]
  112. Litton, C.M.; Raich, J.W.; Ryan, M.G. Carbon allocation in forest ecosystems. Glob. Chang. Biol. 2007, 13, 2089–2109. [Google Scholar] [CrossRef] [Green Version]
  113. Lines, E.R.; Zavala, M.A.; Purves, D.W.; Coomes, D.A. Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Glob. Ecol. Biogeogr. 2012, 21, 1017–1028. [Google Scholar] [CrossRef]
  114. Poorter, H.; Niklas, K.J.; Reich, P.B.; Oleksyn, J.; Poot, P.; Mommer, L. Biomass allocation to leaves, stems and roots: Meta-analyses of interspecific variation and environmental control. New Phytol. 2012, 193, 30–50. [Google Scholar] [CrossRef]
  115. Richards, A.E.; Forrester, D.I.; Bauhus, J.; Scherer-lorenzen, M. The influence of mixed tree plantations on the nutrition of individual species: A review. Tree Physiol. 2010, 30, 1192–1208. [Google Scholar] [CrossRef] [Green Version]
  116. Gilani, H.; Naz, H.I.; Arshad, M.; Nazim, K.; Akram, U.; Abrar, A.; Asif, M. Evaluating mangrove conservation and sustainability through spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan. Estuar. Coast. Shelf Sci. 2021, 249, 107128. [Google Scholar] [CrossRef]
Forests 12 00586 g001 550
Figure 1. Forest cover in Pakistan (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.-E.; Herold, M.; Fritz, S. Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2019: Globe 2020).
Figure 1. Forest cover in Pakistan (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.-E.; Herold, M.; Fritz, S. Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2019: Globe 2020).
Forests 12 00586 g001
Forests 12 00586 g002 550
Figure 2. Graphical bars show the estimated carbon stock studies in Pakistan. Kpk = Khyber Pakhtunkhwa, AJK = Azad Jammu and Kashmir, and GB = Gilgit Baltistan
Figure 2. Graphical bars show the estimated carbon stock studies in Pakistan. Kpk = Khyber Pakhtunkhwa, AJK = Azad Jammu and Kashmir, and GB = Gilgit Baltistan
Forests 12 00586 g002
Forests 12 00586 g003 550
Figure 3. Above-ground biomass estimation methods used in Pakistan.
Figure 3. Above-ground biomass estimation methods used in Pakistan.
Forests 12 00586 g003
Table
Table 1. Major national studies of forest cover in Pakistan.
Table 1. Major national studies of forest cover in Pakistan.
StudySatellite Data Used
(Resolution)		Year of AcquisitionMethod UsedAim of the Study
Forestry Sector Master Plan (FSMP)Landsat Thematic Mapper ™ (30 m)1989–1990Manual interpretation of hard copies at a scale of 1:250,000Assessment of forest cover at provincial level
National Forest and Range Resources Assessment Study (NFRRAS)Landsat Enhanced Thematic Mapper (ETM) (30 m)1997–2001Supervised classification, forest inventory plots were used to facilitate the assessmentForest cover assessment of the country
Land Cover Atlas of Pakistan (LCAP)SPOT-5
(2.5 m)		2007Visual interpretationPreparing an atlas for each district regarding natural resources, assessing forest change, and capacity building of professionals
The FSMP study was conducted by the Ministry of Environment, while NFRRAS and LCAP were conducted by Pakistan Forest Institute, Peshawar. Source: [10].
Table
Table 2. Values for Pakistan forest cover from different reports.
Table 2. Values for Pakistan forest cover from different reports.
StudyForest Area (Million ha)Remarks
1989–199019972000200120072010
FSMP3.59
NFRRAS 3.62 3.32
LCAP 4.34 Alpine pastures, farmland trees, and linear plantation were not included
Food and Agriculture Organization (FAO)2.5 2.1 1.7 (3.14)This study excluded other wooded land for 2010, which (forest and wooded land) was about 3.14 million ha
World Bank 1.9Year of forest cover assessment is not given
Procured from: [10].
Table
Table 3. Current status of each territory with respect to above-ground biomass (AGB) and carbon stock studies.
Table 3. Current status of each territory with respect to above-ground biomass (AGB) and carbon stock studies.
Province/TerritoryForest Types Exist in Each TerritoryCurrent Status of AGB and Carbon Stock Studies
Forest TypeSource
Khyber PakhtunkhwaSubalpine, Dry temperate, Moist temperate, Oak, Subtropical pine, Subtropical broad-leaved, and Dry tropical thorn forest.[5,12][5,11,19,20,22,25,27,28,30,32,33,34,35,36,37,38,39,40,41,42,99]
PunjabConiferous forest, Scrub forest, Irrigated plantation, Riverain forest.[100][8,30,47,48,49,50,52,53,54,55,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73]
SindhRiverine forest, Mangroves, Irrigated plantations[101][79,80,81,82,83]
BalochistanJuniper forest, Chilghoza pine forest, Olive forests, Kohistan forest, Mangrove forest, Invasive forest, Tropical desert thorn forest.[102]
Azad Jammu and KashmirDry subtropical scrub forest, Subtropical Pine forest, Moist temperate, Dry temperate, Sub-Alpine, and Alpine Scrub.[87][88,89,90,91]
Gilgit BaltistanAlpine pastures, Sub-alpine, Dry temperate, Oak[7,97][92,97]
Table
Table 4. Allometric equations of Pakistan forest ecosystem.
Table 4. Allometric equations of Pakistan forest ecosystem.
Forest TypeLocationSpeciesAllometric Equations
(AGB)		Source
Moist Temperate ForestKhyber PakhtunkhwaCedrus deodara0.0491(D2H)0.9167[5]
Moist Temperate ForestKhyber PakhtunkhwaPinus wallichiana0.0594(D2H)0.881[5]
Dry Temperate ForestGilgit BaltistanPinus gerardiana0.0253D2.6077[5]
Moist Temperate ForestKhyber PakhtunkhwaAbies pindrow0.0452(D2H)0.9029[5]
Moist Temperate ForestKhyber PakhtunkhwaPicea smithiana0.0821(D2H)0.8363[5]
Subtropical Broad-leaved forestKhyber PakhtunkhwaEucalyptus camaldulensis0.023(D2H)0.9985[5]
Dry Temperate ForestGilgit BaltistanQuercus ilex0.8277(D2H)0.6655[5]
Subtropical Pine ForestKhyber PakhtunkhwaPinus roxburghii0.0224(D2H)0.9767[43]
Dry Temperate ForestGilgit BaltistanCedrus deodara0.1779(D2H)0.8103[96]
Subtropical Broad-leaved ForestKhyber PakhtunkhwaAcacia modesta0.2267(D2H)0.8226[99]
Subtropical Broad-leaved ForestKhyber PakhtunkhwaOlea ferruginea7.8863 + 0.0556(D2H)[99]
Dry Tropical Thorn ForestKhyber PakhtunkhwaAcacia nilotica0.0493(D2H)0.9728[99]
FarmlandKhyber PakhtunkhwaPopulus deltoides0.0194(D2H)0.9654[99]
Subtropical Broad-leaved ForestKhyber PakhtunkhwaRobinea pseudoacacia0.2586(D2H)0.7786[99]
Dry Tropical Thorn ForestKhyber PakhtunkhwaZizyphus mauritianaEXP((−9.46108 + 0.52923*Ln(H) + 2.15113*Ln(D)))*0.8*1.4*1000[99]
FarmlandKhyber PakhtunkhwaPaulownia spp0.0165(D2H)0.9969[99]
Subtropical Broad-leaved ForestKhyber PakhtunkhwaDodonea viscosa0.928(D)02.018[99]
Table
Table 5. Volume allometric models for forest stand volume calculation.
Table 5. Volume allometric models for forest stand volume calculation.
Forest TypeTree SpeciesLocationTree ComponentAllometric Model
(Variable)		Source
Subtropical Broadleaved Evergreen forestOlea ferrugineaLehterarStemV = −0.320 + 0.041(x) − 1.44 (x)2
(DBH)		[48]
Sub Alpine forestBetula utilisKalamStemV = 0.9589 + 0.05625(x) − 0.001367(x)2
(DBH)		[19]
Dry Temperate forestCedrus deodaraChilasStemV = 0.038(D2H)
(DBH and H)		[98]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Khan, I.A.; Khan, W.R.; Ali, A.; Nazre, M. Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review. Forests 2021, 12, 586. https://doi.org/10.3390/f12050586

AMA Style

Khan IA, Khan WR, Ali A, Nazre M. Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review. Forests. 2021; 12(5):586. https://doi.org/10.3390/f12050586

Chicago/Turabian Style

Khan, Ishfaq Ahmad, Waseem Razzaq Khan, Anwar Ali, and Mohd Nazre. 2021. "Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review" Forests 12, no. 5: 586. https://doi.org/10.3390/f12050586

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop