Estimating forest carbon stocks in tropical dry forests of Zimbabwe: exploring the performance of high and medium spatial-resolution multispectral sensors

Gara, Tawanda W.; Murwira, Amon; Dube, Timothy; Sibanda, Mbulisi; Rwasoka, Donald Tendayi; Ndaimani, Henry; Chivhenge, Emmerson; Hatendi, Chipo M.

doi:10.2989/20702620.2016.1233751

Cited by 6 publications

(5 citation statements)

References 42 publications

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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%

“…To the best of our knowledge, no study has integrated optical and RADAR imagery for forest carbon modelling in mopane‐dominated dry savannah forest of Southern Africa. The few studies that have been conducted in these ecosystems mainly use optical sensors to model forest biomass or carbon (Dube et al, 2016; Gara et al, 2017). Dry savannah forests of Southern Africa are characterised by different vegetation structures compared to tropical monsoon, tropical rain, moist deciduous and temperate forests where radar data have been intensively used.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Specifically, very few studies have used the freely available multispectral data such as the Landsat‐8 to map grass biomass in patchy landscapes such as savannah biomes. In the few instances, that Landsat data has been used in savannah biomass it has been to estimate forest aboveground biomass or wood volume (Dube & Mutanga, 2015b; Dube et al., 2015; Gara et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Vashum and Jayakumar (2012) highlighted the importance of vegetation biomass in regulating the global carbon cycle. Several studies (Baudena et al., 2015; Dube et al., 2018; Gara et al., 2017; Ramoelo et al., 2012) have revealed that savannah ecosystems play an important role as atmospheric carbon storage sinks, however gaps in knowledge exists highlighting areas that need attention (Williams et al., 2007). Several researchers (Dube & Mutanga, 2015a; Li et al., 2016, 2017; Ramoelo et al., 2015; Singh et al., 2018) have focussed on large scale monitoring and estimation, using coarse resolution satellite data such as MODIS multispectral instruments in both woodlands and grasslands.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

2021

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Accurate information on the amount of aboveground grass biomass (AGBg) stored in savannah rangelands remains unknown. Therefore, in this study we assessed the capability of a new multispectral sensor (Landsat‐8 OLI) in estimating aboveground grass biomass stored within two different land use management units in savannah rangelands using the stepwise multiple linear regression methods. Specifically, we evaluated the performance of different Landsat‐8 OLI derivatives (i.e. (a) spectral bands, (b) derived vegetation indices and (c) a combination of spectral bands and vegetation indices) to quantify aboveground grass biomass. The results highlighted that the use of spectral bands as stand‐alone model variables yielded considerably high accuracies in terms of the coefficient of determination (r2) and the root mean square error (RMSE). An r2 of 0.73 (72.81%) and RMSE of 37.88% for the protected area and an r2 of 0.75 (75.18%) and with a RMSE of 33.16% for the nonprotected area were attained. The use of vegetation indices, however, demonstrated a very weak model performance yielding an r2 of 0.31 (30.68%) and a RMSE of 51.51%, for the protected area and an r2 of 0.50 (50.43%) and RMSE of 40% for the nonprotected areas. Comparatively, combining Landsat‐8 OLI derived spectral bands and vegetation indices demonstrated improved model performance, yielding an r2 of 0.92 (91.90%), and RMSE of 41.3% for the protected area and an r2 of 0.94 (93.82%) with a RMSE of 33.14% for the nonprotected area. Therefore based on the observed results, AGBg in savannah rangelands can be satisfactorily estimated using broadband multispectral derivatives.

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“…Fifteen research papers (10%) studied forest biomass, and fourteen publications (10%) assessed 'forest structure'. Studies on biomass included the estimation of AGB Rugege 2006, Dube et al 2018), and changes in carbon stock (Gara et al 2017). Some of the publications used National Forest Inventory (NFI) data (Verbesselt et al 2007, Halperin et al 2016, and field-based samples (Tsalyuk et al 2017, Mareya et al 2018 to estimate biomass in Southern Africa.…”

Section: Forest Biomass and Structuresmentioning

confidence: 99%

Remote sensing for monitoring tropical dryland forests: a review of current research, knowledge gaps and future directions for Southern Africa

David

Rosser

Donoghue

2022

Environ. Res. Commun.

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Climate change, manifest via rising temperatures, extreme drought, and associated anthropogenic activities, has a negative impact on the health and development of tropical dryland forests. Southern Africa encompasses significant areas of dryland forests that are important to local communities but are facing rapid deforestation and are highly vulnerable to biome degradation from land uses and extreme climate events. Appropriate integration of remote sensing technologies helps to assess and monitor forest ecosystems and provide spatially explicit, operational, and long-term data to assist the sustainable use of tropical environment landscapes. The period from 2010 onwards has seen the rapid development of remote sensing research on tropical forests, which has led to a significant increase in the number of scientific publications. This review aims to analyse and synthesise the evidence published in peer review studies with a focus on optical and radar remote sensing of dryland forests in Southern Africa from 1997-2020. For this study, 137 citation indexed research publications have been analysed with respect to publication timing, study location, spatial and temporal scale of applied remote sensing data, satellite sensors or platforms employed, research topics considered, and overall outcomes of the studies. This enabled us to provide a comprehensive overview of past achievements, current efforts, major research topics studies, EO product gaps/challenges, and to propose ways in which challenges may be overcome. It is hoped that this review will motivate discussion and encourage uptake of new remote sensing tools (e.g., Google Earth Engine (GEE)), data (e.g., the Sentinel satellites), improved vegetation parameters (e.g., red-edge related indices, vegetation optical depth (VOD)) and methodologies (e.g., data fusion or deep learning, etc.), where these have potential applications in monitoring dryland forests.

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Estimating forest carbon stocks in tropical dry forests of Zimbabwe: exploring the performance of high and medium spatial-resolution multispectral sensors

Cited by 6 publications

References 42 publications

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

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

Remote sensing of aboveground grass biomass between protected and non‐protected areas in savannah rangelands

Remote sensing for monitoring tropical dryland forests: a review of current research, knowledge gaps and future directions for Southern Africa

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