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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Abrupt changes in climatic conditions, exploitation of natural resources, and land degradation exacerbate soil erosion. This study provides a comprehensive assessment of soil erosion dynamics in Pakistan for 2005 and 2015 at 1 km 2 spatial resolution using six influencing factors: rainfall erosivity (R), soil erodibility (K), slope-length (L), slopesteepness (S), cover management (C), and conservation practice (P). The wellaccepted revised universal soil loss equation (RUSLE) was implemented to estimate soil erosion. The soil erosion maps were categorized into four classes: low (<1), medium (1-5), high (5-20), and very high (>20)and transitions among these classes were determined by applying the change matrix method to assess soil erosion (ton ha -1 yr -1 ) changes from 2005 to 2015. Furthermore, spatial patterns and soil erosion dynamics were evaluated for seven administrative units of Pakistan to examine conservation efforts and ecosystem services. Major findings of this study indicate, that at the national scale, an estimated soil erosion of 1.79 ± 11.52 ton ha -1 yr -1 in 2005, which increased to 2.47 ± 18.14 ton ha -1 yr -1 in 2015. This intensification of soil erosion is coupled with land cover and land use changes (LCLUC) due to population expansion, infrastructural development, and exploitation of natural resources. This study's analytical framework and outcomes could be used for developing effective conservation policies to control soil erosion at various spatial scales in Pakistan.

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This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

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Escalation in population over time triggered the abrupt exploitation of natural resources for human survival trough industrialization that ultimately caused splendid increase in the waste generation. This industrial development resulted in the migration of rural community toward urban areas. Management of urban waste is a great challenge for the urban administration. However, technologies have been developed to manage the waste in environmental friendly and sustainable manner. Sanitary landfill sites are one of the latest methods of disposing the municipal solid waste in an environment friendly and sustainable manner. Government and administrative authorities are adopting this technology for the management of urban solid waste. Present study is about identifying landfill sites for the Sahiwal city with an area of 1160 square kilometer and projected population of 1.57 million persons in 2016. Geographical Information System (GIS) is used for the identification of appropriate landfill site (LFS) that can fulfill the need of city in future and is selected based on the sustainable and eco-friendly criteria. The criteria are developed keeping in view the proximity from several land-use features i.e. water bodies, roads, settlements, agricultural land, bare land and existing disposal sites. The weights of the criteria are quantified using pair-wise comparison method in Analytic Hierarchy Process (AHP). The weights are incorporated in GIS spatial data environment and are assigned to proximity threshold of each criterion. Ultimately a map for each criterion is developed highlighting suitable, least suitable, less suitable and unsuitable areas with respect to each specific criterion. These maps are spatially overlaid which result in a final map that identifies most suitable landfill sites for solid waste disposal. The five identified sites are then prioritized based on their distances from the city center and area available. All identified sites are on the bare land and contain considerable buffer from environmentally sensitive receptors. A. Ahmad et al.

Investigating information on land cover changes is an indispensable task in studies related to the variation of the environment. Land cover changes can be monitored using multi-temporal satellite images at different scales. The commonly used method is the post-classification change detection which can figure out the replacement of a land cover by the others. However, the magnitude and dimension of the changes are not been always exploited. This study employs the mixture of categorical and radiometric change methods to investigate the relations between land cover classes and the change magnitude, the change direction of land covers. Applying the Change Vector Analysis (CVA) method and unsupervised classification for two Landsat images acquired at the same day of years in 2000 and in 2017 in Duy Tien district, the experimental results show that a low magnitude of change occurs in the largest area of direction I and direction IV regarding the increase of Normalized Difference Vegetation Index (NDVI), but the opposite trend of (Bare soil Index) BI in the rice field. Alternately, the high magnitude of change is seen in the build-up class which occupies the smallest area with 1700 ha. The characterized changes produced by the CVA method provide a picture of change dynamics of land cover over the period of 2000-2017 in the study area.