Indigenous forest wood volume estimation in a dry savanna, Zimbabwe: exploring the performance of high-and-medium spatial resolution multispectral sensors

Gara, Tawanda W.; Murwira, Amon; Ndaimani, Henry; Chivhenge, Emmerson; Hatendi, Chipo M.

doi:10.1080/0035919x.2015.1057269

Cited by 5 publications

(4 citation statements)

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“…The use of GAMs and nonlinear models, such as the sigmoidal and exponential models in this study, for predicting AGB has shown promise in a number of studies in different biomes (e.g., Moisen and Frescino 2002;Hall et al 2006;Anaya et al 2009;McRoberts et al 2015;Kattenborn et al 2015). However, it appears that most approaches to estimate and map AGB in the miombo ecoregion used linear ordinary least squares (Ryan et al 2012;Kashindye et al 2013;Mitchard et al 2013;Solberg et al 2015;Naesset et al 2016), despite the nonlinear relationships between forest attributes and remotely-sensed data (Sedano et al 2008;Banskota et al 2014;Lu et al 2016 ;Gara et al 2015). We found GAM's to perform as well as nonlinear models with respect to fit and validation statistics; however, when applied to the population of predictor data the bootstrap confidence interval was less efficient than the null model.…”

Section: Discussionmentioning

confidence: 95%

“…Two studies that estimated miombo tree volume (m 3 •ha −1 ) using Landsat also provide a useful comparison. Employing cross-validation to compute RMSPE%, Pereira (2006) reported 48 % in Mozambique using Landsat and k-Nearest Neighbor imputation, while Gara et al (2015) averaged 62 % using Landsat and nonlinear models. Our chosen model (sigmoidal) has estimates of RMSE% at 47 and RMSPE% at 58 %, indicating that our fit and validation statistics are comparable to similar studies using large-scale inventories in the miombo ecoregion using optical remotely-sensed data (Pereira 2006) and active remote sensing data such as ALS or radar (Solberg et al 2015;Mauya et al 2015;Naesset et al 2016).…”

Section: Discussionmentioning

confidence: 99%

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Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

et al. 2016

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Background: Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. In many tropical developing countries, this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB. Methods: Using National Forest Inventory (NFI) data for a~1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Results: Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was~36.8 tonnes per hectare (i.e., 57 % of the mean). However, the sigmoidal model was approximately 30 % more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total AGB for the study area at 64,526,209 tonnes (+/− 477,730), with a confidence interval 20 times more precise than a simple designbased estimate. Conclusions: Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

et al. 2016

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“…Studies that have examined the utility of radar remote sensing have mainly focused on tropical forests while savannah dry forests of Southern Africa have remained under‐reported. The few studies conducted in these ecosystems used Landsat data only and regressed forest carbon stocks to vegetation indices using simple parametric regression (Gara et al, 2015, 2017). In addition to integrating ALOS PALSAR and Landsat ETM+, our study demonstrates the utility of ANN—a parametric machine‐learning algorithm to estimate and map forest carbon stocks.…”

Section: Discussionmentioning

confidence: 99%

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane‐dominated African savannah dry forest

et al. 2023

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This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R 2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R 2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R 2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands.

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“…In order to increase the quantity and quality of manure, most crop leftovers are either transported to the kraal or grazed by animals in situ. Agricultural systems are primarily mixed crop-livestock systems with low crop production (Murwira et al, 2015). Droughts are frequent, and rainfall is unimodal, irregular, and highly variable both within and between seasons (Challinor et al, 2007).…”

Section: Farmer's Capacitymentioning

confidence: 99%

An Assessment of the Sustainability of Conservation Farming in Promoting Food Security Among Smallholder Farmers in Change Ward

Mathe

2023

IJAGST

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The study assessed the sustainability of conservation farming in promoting food security among smallholder farmers in Change Ward. The study findings were analyzed using a sustainable livelihood theoretical framework using a qualitative research approach where interviews, focus group discussions, and observations were used. It was found that the farmers are growing a variety of crops, which is resulting in improved crop diversification and more resilience among farmers. This has a positive bearing on increasing food availability, the variety of crops is leading to improved nutritional status at the household level. Also, their resilience to climate change has thus far improved because of conservation agriculture which in turn increases the probability of its sustainability among farmers as they are seeing its benefits. Furthermore, vulnerabilities of this strategy were also highlighted which might pose a threat towards its sustainability, these include dependence on government inputs, too laborious, climate change which is resulting in high and unpredictable rains which come for a short period. The study found out that farmers own land for their agricultural purposes, and have family support which tends to reduce workload at the fields, farmer groups and technical support from the Agritex officers. However, during the research, some participants had a divergent view on the sustainability of this strategy as they do have assets such as land, they are not in any farmers group which has a bearing on the workload. Thus, the study shows that conservation agriculture is one of the key strategies that is being practiced by smallholder farmers towards the improvement of their food security at home. This strategy is sustainable among farmers as there are assets that are put in place that could help the farmers to continue with the strategy in ensuring food security. However, in ensuring its sustainability there were some loopholes which needs to be addressed inorder to address the sustainability aspect of this strategy such as clear and improved land ownership, women empowerment which will go a long way in addressing the shortage of financial resources. Government should introduce and subsidise the mechanization of this strategy as a way of reducing its workload in the fields. There is need for the empowerment of smallholder farmers on soft skills such as unity and respect among the farmers.

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Indigenous forest wood volume estimation in a dry savanna, Zimbabwe: exploring the performance of high-and-medium spatial resolution multispectral sensors

Cited by 5 publications

References 35 publications

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane‐dominated African savannah dry forest

An Assessment of the Sustainability of Conservation Farming in Promoting Food Security Among Smallholder Farmers in Change Ward

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