Felix Kanungwe Kalaba

Protected forest areas are fraught with severe threats from mining, agriculture and settlement expansion, and unsustainable use of forest resources. Due to funding and technical challenges, the inadequate monitoring and lack of information limits the conservation efforts in Zambia in general and Solwezi district in particular. Field-based methods in monitoring forest quality and suitability are time consuming and inefficient especially in inaccessible areas. However, with the advent of technology, Geographical Information System (GIS) and remote sensing, important data for forest quality can easily be accessed. This study aimed at assessing the state of protected forest areas in Zambia’s Solwezi Copper mining district, prone to forest fragmentation. Furthermore, this study identifies suitable areas for conservation, based on standardized criteria that combine Multi Criteria Decision Analysis (MCDA) and GIS approach. A suitability model was developed for the selection of the suitable areas, and elimination of the unsuitable ones, using GIS and Analytical Hierarchy Process (AHP). Five suitability criteria and two restriction criteria were used in this model. The results show different ranked levels of suitability which include 15.4% restricted, 18.1% lowly suitable, 14.9% moderately suitable, 21.2% highly suitable and 30.4% extremely suitable. Our approach informs conservationists, and other stakeholders about the status of protected forest areas and avails novel opportunities for creating new ones. This study’s modelling approach can be a prerequisite to sustainable forest management by policy makers and practitioners, and an essential input into forest monitoring.

There is a need for accurate reporting of the second-growth forest in accounting for the contribution of secondary forests to achieving global carbon sequestration strategies. This study develops a model relating aboveground live biomass (AGLB) to Landsat reflectance measurements, and this method is used on multi spatial-temporal Landsat data (1998, 2001, 2004, 2011 and 2016) to assess changes in Miombo woodlands in Zambia. Results showed that the enhanced vegetation index was a better predictor of AGLB (r² = 0.96; RMSE = 0.24 t ha⁻¹) than the other assessed vegetation indices. Forest carbon stocks increased by an average rate of 3.65 ± standard error (SE): 0.56 t ha⁻¹ yr⁻¹ from 1998 to 2001, but a continual decline was evident for the periods 2001–2004 (−0.60 t ha⁻¹ yr⁻¹), 2004–2011 (−0.88 t ha⁻¹ yr⁻¹) and 2011–2016 (−2.03 t ha⁻¹ yr⁻¹). The study provides an empirical model for evaluating carbon stock changes.