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.

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.

In the late 1960s, the Islamic Republic of Pakistan’s capital shifted from Karachi to Islamabad, officially named Islamabad Capital Territory (ICT). In this aspect, the ICT is a young city, but undergoing rapid expansion and urbanization, especially in the last two decades. This study reports the measurement and characterization of ICT land cover change dynamics using Landsat satellite imagery for the years 1976, 1990, 2000, 2010, and 2016. Annual rate of change, landscape metrics, and urban forest fragmentation spatiotemporal analyses have been carried out, along with the calculation of the United Nations Sustainable Development Goal (SDG) indicator 11.3.1 Land Consumption Rate to the Population Growth Rate (LCRPGR). The results show consistent increase in the settlement class, with highest annual rate of 8.79% during 2000–2010. Tree cover >40% and 500 acres’ class decreased from 392 km² (65.41%) to 241 km² (55%), and ‘patch forest’ class increased from 15 km² (2.46%) to 20 km² (4.54%), from 1976 to 2016. The LCRPGR ratio was 0.62 from 1976 to 2000, increasing to 1.36 from 2000 to 2016.