Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

Fatoyinbo, Lola; Feliciano, E. A.; Lagomasino, David; Lee, Seung Kuk; Trettin, Carl C.

doi:10.1088/1748-9326/aa9f03

Cited by 67 publications

(47 citation statements)

References 56 publications

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“…Hence, the inaccuracy in the field data (resulting from the use of poorly adapted allometries) yields bias in the prediction models, which translates into large errors when such models are applied on a landscape scale. The large prediction errors reported in [48], and the bias detected with height class (underestimation of low AGB and overestimation of high AGB values) may also be a consequence of not using species-specific allometric equations.…”

Section: Discussionmentioning

confidence: 99%

“…The best independent variables to estimate AGB for a boreal forest showed by [23] were one variable related to canopy height and another one related to canopy density (similar to point density here). A simple canopy height metric was also used to predict AGB in East African mangroves [48].…”

Section: Discussionmentioning

confidence: 99%

“…Other studies reported higher accuracies, such as in rainforest (R 2 = 0.90, RMSE = 38.3 t·ha −1 ) [79] and temperate forest (R 2 = 0.89, RMSE = 50.2 t·ha −1 [80] and R 2 = 0.93, RMSE = 33.9 t·ha −1 [81]), among other results reported in the literature. In comparable settings, the study of [48] reports relative RMSE errors of 23-33% to be compared with the calibration RMSE(LOO) of 12%.…”

Section: Discussionmentioning

confidence: 99%

“…Discounting aerial roots from AGB also creates a discrepancy between the vegetation probed by lidar at low tide and the vegetation contributing to the AGB measured in the field. The estimation of mangrove biomass using lidar data has so far been restricted to a handful of studies [36,37,42,46,47], most of which used satellite lidar only with the notable exception of the study by Fatoyinbo et al [48]. In the latter study, a coarse lidar metric (highest point per 10 × 10 m grid cell) was used as the sole predictor in the AGB model.…”

Section: Introductionmentioning

confidence: 99%

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Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

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Remote sensing techniques offer useful tools for estimating forest biomass to large extent, thereby contributing to the monitoring of land use and landcover dynamics and the effectiveness of environmental policies. The main goal of this study was to investigate the potential use of discrete return light detection and ranging (lidar) data to produce accurate aboveground biomass (AGB) maps of mangrove forests. AGB was estimated in 34 small plots scatted over a 50 km 2 mangrove forest in Rio de Janeiro, Brazil. Plot AGB was computed using either species-specific or non-species-specific allometric models. A total of 26 descriptive lidar metrics were extracted from the normalized height of the lidar point cloud data, and various model forms (random forest and partial least squares regression with backward selection of predictors (Auto-PLS)) were tested to predict the recorded AGB. The models developed using species-specific allometric models were distinctly more accurate (R 2 (calibration) = 0.89, R 2 (validation) = 0.80, root-mean-square error (RMSE, calibration) = 11.20 t·ha −1 , and RMSE(validation) = 14.80 t·ha −1 ). The use of non-species-specific allometric models yielded large errors on a landscape scale (+14% or −18% bias depending on the allometry considered), indicating that using poor quality training data not only results in low precision but inaccuracy at all scales. It was concluded that under suitable sampling pattern and provided that accurate field data are used, discrete return lidar can accurately estimate and map the AGB in mangrove forests. Conversely this study underlines the potential bias affecting the estimates of AGB in other forested landscapes where only non-species-specific allometric equations are available.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

et al. 2018

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“…Mangrove forests cover 0.12% of the world's total land area (Dodd and Ong 2008) and 0.7% of the total global area of tropical forests (Giri et al 2011). Mangroves buffer coastal regions from storm surges and erosion (Das and Vincent 2009), play an important role in carbon sequestration (Donato et al 2011, Siikamäki et al 2012, Fatoyinbo et al 2018 and support the livelihood of dwelling populations (Walters et al 2008). This ecosystem provides a variety of important provisional ecosystem services to local communities, including wood, firewood or building supplies for houses, huts, fences, matting and scaffolds (Millenium Ecosystem Assessment 2005, Duke et al 2007, Ellison 2008, Walters et al 2008, Rajkaran and Adams 2012, Adite et al 2013.…”

Section: Introductionmentioning

confidence: 99%

Strong seedling recruitment does not limit mangrove vulnerability to harvest

Gaoue

Yessoufou

2019

Environ. Res. Lett.

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Ecological studies on the impacts of timber harvesting contributed to inform sustainable management strategies of tropical forests. However, these studies rely strongly on two major assumptions: (i) strong seedlings recruitment predispose for positive population dynamics, and (ii) more adult trees is a guarantee for a strong reproductive capability of the population. These assumptions are applied without accounting for the life history of the harvested species. Here, we revisit these assumptions in light of the life history theory, which predicts different responses of short-and long-lived species to perturbation. We predict that harvesting adults, rather than reducing seedling recruitment of longlived species, would have greater impact on population dynamics. We tested our prediction on three mangrove species in South Africa. First, we reconstructed the projection matrices for three mangrove species in the Mngazana Estuary of Eastern Cape province of South Africa. Next, we simulated a range of harvest intensities for different life stages and explored how harvesting influences population dynamics. Finally, we examined the age-specific mortality trajectories for all three species. We found that populations of all three species were closer to their stable stage distribution. Contrary to popular belief, we found that changes in seedling recruitment will have minimal effects on mangrove longterm population dynamics, a finding consistent with the life history theory. However, contrary to expectation, simulating harvest of adults had minimal effect on population dynamics. This is due to low reproductive values for these late stages. Our analysis of age-specific mortality trajectories further provided evidence for positive senescence particularly for Avicennia which was the least resilient to harvest. Our findings cast doubt on the traditional forest management strategies that rely strongly on seedling density as a metric of sustainability and forbid unquestionably harvesting large individuals. We call for caution while generalizing forest management strategies irrespective of the life history of the species at hand.

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Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

Aja,

Miyittah,

Angnuureng

2023

Ecology and Evolution

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Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations using solely optical satellite data. Therefore, it is essential to conduct a more precise evaluation using data from several sources and appropriate models in order to manage the mangrove ecosystem as effectively as feasible. In this study, the status of the mangrove ecosystem and its potential contribution to coastal resilience were evaluated using the Google Earth Engine (GEE) and the InVEST model. The GEE was used to map changes in mangrove and other land cover types for the years 2009 and 2019 by integrating both optical and radar data. The quantity allocation disagreement index (QADI) was used to assess the classification accuracy. Mangrove height and aboveground biomass density were estimated using GEE by extracting their values from radar image clipped with a digital elevation model and mangrove vector file. A universal allometric equation that relates canopy height to aboveground biomass was applied. The InVEST model was used to calculate a hazard index of every 250 m of the shoreline with and without mangrove ecosystem. Our result showed that about 16.9% and 21% of mangrove and other vegetation cover were lost between 2009 and 2019. However, water body and bare land/built‐up areas increased by 7% and 45%, respectively. The overall accuracy of 2009 and 2019 classifications was 99.6% (QADI = 0.00794) and 99.1% (QADI = 0.00529), respectively. Mangrove height and aboveground biomass generally decreased from 12.7 to 6.3 m and from 105 to 88 Mg/ha on average. The vulnerability index showed that 23%, 51% and 26% of the coastal segment in the presence of mangrove fall under very low/low, moderate and high risks, respectively. Whereas in the absence of mangrove, 8%, 38%, 39% and 15% fall under low, moderate, high and very high‐risk zones, respectively. This study will among other things help the stakeholders in coastal management and marine spatial planning to identify the need to focus on conservation practices.

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Estimating mangrove aboveground biomass from airborne LiDAR data: a case study from the Zambezi River delta

Cited by 67 publications

References 56 publications

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data

Strong seedling recruitment does not limit mangrove vulnerability to harvest

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

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