Estimating canopy cover using a Spatially Balanced Sampling approach: A case study of miombo woodlands in Western Tanzania

Abstract

Long-term monitoring is essential to understand the impacts of land use and climate change on miombo woodlands. This study introduces an innovative monitoring design for miombo woodlands with a two-stage sampling utilizing spatially balanced techniques to estimate the area and canopy cover of miombo woodland across the Tabora, Sikonge, Mlele, and Tanganyika districts. The first step involved the selection of 68 tracts, each comprising an average of 1025 plots, with the aid of spatially balanced sampling. Each of the 69,716 plots was classified into closed (canopy cover > 70%), open (40% ≤ canopy cover ≤ 70%), very open (10% ≤ canopy cover < 40%), and non-miombo (canopy cover < 10%) based on woodland cover derived from Sentinel 2 images, followed by the second step consisting of stratified random sampling and inventorying of 2,690 plots within 68 tracts. Using PlanetScope images, we determined the canopy cover for the 2,690 plots selected in the second step and reclassified them accordingly. Employing the Horvitz–Thompson estimator, our results showed that miombo woodlands in these districts cover 37,359 ± 4,618 km² with an average canopy cover of 55% ± 5%. Closed miombo woodland (canopy cover > 70%) was the dominating woodland type, covering 29,546 ± 4,382 km² of the study area with an average canopy cover of 84% ± 7%. The study's innovative sampling design provides reliable estimates of the area of miombo woodlands and average canopy cover, with relative standard errors consistently below 25%, offering a robust foundation for monitoring different miombo types.