Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods

Halperin, James; LeMay, Valerie; Coops, Nicholas C.; Verchot, Louis; Marshall, Peter; Lochhead, Kyle

doi:10.1016/j.rse.2016.03.028

Cited by 40 publications

(55 citation statements)

References 55 publications

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“…These results indicate a strong potential for the use of non-green land cover indices, together with greenness-based land cover metrics, to model tree basal area and biomass across African TDF landscapes. Other recent studies have shown the value of using not only multiple non-green and green land cover metrics, but also ancillary data on landscape variables to map aspects of tree structure across African forests (Hansen et al 2016, Halperin et al 2016. Strong total canopy cover relationships to basal area and biomass also suggest that emerging radar sensors, such as Sentinel-1, may hold promise for mapping miombo and other African TDF biomass in combination with optical satellite data.…”

Section: 2mentioning

confidence: 99%

“…Greenness-related field and satellite measures underlie many widely used global methods for optical remote sensing of tree structure (Hansen et al 2013. However, past studies in African dry forests have found that greenness measures alone, such as the satellite-derived normalized difference vegetation index (NDVI), have correlated only moderately to canopy and forest structure (Ribeiro et al 2008, Halperin et al 2016. Non-green land cover components, such as woody stem density and canopy structure, or patterns in leaf litter, senesced grass or exposed substrate, can also relate to forest structure.…”

Section: Introductionmentioning

confidence: 99%

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Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

Mayes

Mustard

Melillo

et al. 2017

Environ. Res. Lett.

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Section: 2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

Mayes

Mustard

Melillo

et al. 2017

Environ. Res. Lett.

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“…Synthetic Aperture Radar (SAR) emits microwave signals that are able to penetrate cloud making SAR imagery attractive for the study of persistently or seasonally cloudy areas. However, SAR data suffers from geometric distortion and shadowing in areas with steep terrain because the sensors use directional signals, which when combined with high cost and the historically low spatial resolution of available data has restricted the use of SAR to monitor vegetation in mountainous environments (Halperin et al, 2016;Sinha et al, 2015). To our knowledge, SAR has not been used to study the mountain treeline.…”

Section: Sensor Typementioning

confidence: 99%

Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution

Morley

Donoghue

Chen

et al. 2018

Ecological Informatics

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. A B S T R A C TSpecies range shifts have been well studied in light of rising global temperatures and the role climate plays in restricting species distribution. In mountain regions, global trends show upward elevational shifts of altitudinal treelines. However, there is significant variation in response between geographic locations driven by climatic and habitat heterogeneity and biotic interactions. Accurate estimation of treeline shifts requires fine-scale patterns of forest structure to be discriminated across mountain ranges. Satellite remote sensing allows detailed information on forest structure to be extrapolated across mountain ranges, however, variation in methodology combined with a lack of information on accuracy and repeatability has led to high uncertainty in the utility of remotely sensed data in studies of mountain treelines. We unite three themes; suitability of remote sensing products, ecological relevance of classifications and effectiveness of the training and validation process in relation to the study of mountain treeline ecotones. We identify needs for further research comparing the utility of different remotely sensed data sets, better characterisation of treeline structure and incorporation of accuracy assessment. Collectively, the improvements we describe will significantly improve the utility of remote sensing by facilitating a more consistent approach to defining geographic variation in treeline structure, improving our ability to link processes from stand to regional scale and the accuracy of range shift assessments. Ultimately, this advance will enable better monitoring of mountain treeline shifts and estimation of the associated to biodiversity and ecosystem function.

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“…To date, medium spatial and spectral resolution imageries have been widely used in CC estimating, such as Landsat series imageries and moderate resolution imaging spectroradiometer (MODIS) imageries [4,[7][8][9][10][11]. However, mixed pixels are common in heterogeneous land cover, which can result in inaccurate estimates of CC [4,[12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

Zhou

Dian

Wang

et al. 2020

Forests

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Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and the heterogeneous pixel values of the same canopy. In this paper, we compared the capacity of two high spatial resolution sensors (SPOT6 and GF2) using three ensemble learning models (Adaptive Boosting (AdaBoost), Gradient Boosting (GDBoost), and random forest (RF)), to estimate canopy cover (CC) in a Chinese northern subtropics forest. Canopy cover across 97 plots was measured across 41 needle forest plots, 24 broadleaf forest plots, and 32 mixed forest plots. Results showed that (1) the textural features performed more importantly than spectral variables according to the number of variables in the top ten predictors in estimating canopy cover (CC) in both SPOT6 and GF2. Moreover, the vegetation indices in spectral variables had a lower relative importance value than the band reflectance variables. (2) GF2 imagery outperformed SPOT6 imagery in estimating CC when using the ensemble learning model in our data. On average across the models, the R2 was almost 0.08 higher for GF2 over SPOT6. Likewise, the average RMSE and average MAE were 0.002 and 0.01 lower in GF2 than in SPOT6. (3) The ensemble learning model showed good results in estimating CC, yet the different models performed a little differently in the results. Additionally, the GDBoost model performed the best of all the ensemble learning models with R2 = 0.92, root mean square error (RMSE) = 0.001 and mean absolute error (MAE) = 0.022.

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Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods

Cited by 40 publications

References 55 publications

Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

Integrating remote sensing and demography for more efficient and effective assessment of changing mountain forest distribution

Comparison of GF2 and SPOT6 Imagery on Canopy Cover Estimating in Northern Subtropics Forest in China

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