Application of 3D data derived from images captured using unmanned aerial vehicles (UAVs) in forest biomass estimation has shown great potential in reducing costs and improving the estimates. However, such data have never been tested in miombo woodlands. UAV-based biomass estimation relies on the availability of reliable digital terrain models (DTMs). The main objective of this study was to evaluate application of 3D data derived from UAV imagery in biomass estimation and to compare impacts of DTMs generated based on different methods and parameter settings. Biomass was modeled using data acquired from 107 sample plots in a forest reserve in miombo woodlands of Malawi. The results indicated that there are no significant differences (p = 0.985) between tested DTMs except for that based on shuttle radar topography mission (SRTM). A model developed using unsupervised ground filtering based on a grid search approach, had the smallest root mean square error (RMSE) of 46.7% of a mean biomass value of 38.99 Mg·ha⁻¹. Amongst the independent variables, maximum canopy height (Hmax) was the most frequently selected. In addition, all models included spectral variables incorporating the three color bands red, green and blue. The study has demonstrated that UAV acquired image data can be used in biomass estimation in miombo woodlands using automatically generated DTMs.

In this study we present general (multiple tree species from several sites) above- and belowground biomass models for trees in the miombo woodlands of Malawi. Such models are currently lacking in the country. The modelling was based on 74 trees comprising 33 different species with diameters at breast height (dbh) and total tree height (ht) ranging from 5.3 to 2 cm and from 3.0 to 25.0 m, respectively. Trees were collected from four silvicultural zones covering a wide range of conditions. We tested different models including dbh, ht and wood specific gravity ( ρ ) as independent variables. We evaluated model performance using pseudo-R², root mean square error (RMSE), a covariance matrix for the parameter estimates, mean prediction error (MPE) and relative mean prediction error (MPE%). Computation of MPE% was based on leave-one-out cross-validation. Values of pseudo-R² and MPE% ranged 0.82–0.97 and 0.9%–2.8%, respectively. Model performance indicated that the models can be used over a wide range of geographical and ecological conditions in Malawi.

Unmanned aerial systems (UASs) and photogrammetric structure from motion (SFM) algorithms can assist in biomass assessments in tropical countries and can be a useful tool in local greenhouse gas accounting. This study assessed the influence of image resolution, camera type and side overlap on prediction accuracy of biomass models constructed from ground-based data and UAS data in miombo woodlands in Malawi. We compared prediction accuracy of models reflecting two different image resolutions (10 and 15 cm ground sampling distance) and two camera types (NIR and RGB). The effect of two different side overlap levels (70 and 80%) was also assessed using data from the RGB camera. Multiple linear regression models that related the biomass on 37 field plots to several independent 3-dimensional variables derived from five UAS acquisitions were constructed. Prediction accuracy quantified by leave-one-out cross validation increased when using finer image resolution and RGB camera, while coarser resolution and NIR data decreased model prediction accuracy, although no significant differences were observed in absolute prediction error around the mean between models. The results showed that a reduction of side overlap from 80 to 70%, while keeping a fixed forward overlap of 90%, might be an option for reducing flight time and cost of acquisitions. Furthermore, the analysis of terrain slope effect in biomass predictions showed that error increases with steeper slopes, especially on slopes greater than 35%, but the effects were small in magnitude.

Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on efficiency of UAS-assisted biomass estimates in the dry tropical miombo woodlands of Malawi. The results of a design-based field sample inventory assisted by three-dimensional point clouds obtained from aerial imagery acquired with a UAS showed that the root mean square errors as well as the standard error estimates of mean biomass decreased as sample plot sizes increased. Furthermore, relative efficiency values over different sample plot sizes were above 1.0 in a design-based and model-assisted inferential framework, indicating that UAS-assisted inventories were more efficient than purely field-based inventories. The results on relative costs for UAS-assisted and pure field-based sample plot inventories revealed that there is a trade-off between inventory costs and required precision. For example, in our study if a standard error of less than approximately 3 Mg ha⁻¹ was targeted, then a UAS-assisted forest inventory should be applied to ensure more cost effective and precise estimates. Future studies should therefore focus on finding optimum plot sizes for particular applications, like for example in projects under the Reducing Emissions from Deforestation and Forest Degradation, plus forest conservation, sustainable management of forest and enhancement of carbon stocks (REDD+) mechanism with different geographical scales.

Spatial time-series measurements of forest degradation rates are important for estimating national greenhouse gas emissions but have been challenging for open forests and woodlands. This lack of quantitative data on forest degradation rates, location and biomass is an important constraint to developing national REDD+ policy. In Malawi, and in most countries in Africa, most assessments of forest cover change for carbon emissions monitoring tend to report only deforestation in the public forest estate managed by the government, even when important forest degradation also occurs in agricultural areas, such as customary forests and other tree-based systems. This study has resulted in: (a) a new robust forest map for Malawi, (b) spatial and quantitative measurements of both forest degradation and deforestation, and (c) a demonstration of the approach through the introduction of a tool that maps across the broad landscape of forests and trees outside of forests. The results can be used to support REDD+ National Forest Monitoring Systems. This analysis produces new estimates of landscape-wide deforestation rates between 2000–2009 (22,410 ha yr⁻¹) and 2009–2015 (38,937 ha yr⁻¹). We further produce new estimates of the rate of forest degradation between 2000–2009 (42,961 ha yr⁻¹) and 2009–2015 (71,878 ha yr⁻¹). The contribution of these new tools and estimates to capacities for calculating carbon emissions are important, increasing prospects for full REDD+ readiness across semi-arid Africa.

The objective of this study was to develop general (multispecies) models for prediction of total tree, merchantable stem and branch volume including options with diameter at breast height (dbh) only, and with both dbh and total tree height (ht), as independent variables. The modelling data set was based on destructively sampled trees and comprised 74 trees from 33 tree species, collected from four forest reserves located in different ecological zones of Malawi. The dbh and ht ranges for the data set were 5.3–111.2 cm and 3.0–25.0 m, respectively. A number of alternative model forms were tested and the final model selection was based on root mean square error (RMSE) values calculated using a leave-one-out cross-validation procedure. The model performances and the evaluations of the finally selected models (R 2 range 0.72 to 0.92; RMSE range 38% to 71%; mean prediction errors range −1.4% to 1.3%) suggest that all models can be used over a wide range of geographical and ecological conditions in Malawi with ...