Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Zhu, Yuanhui; Li, Kai; Liu, Lin; Wang, Shugong; Liu, Hongxing

doi:10.3390/rs70912192

Cited by 103 publications

(85 citation statements)

References 62 publications

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“…Zhou and colleagues [45] exploited VIs and texture images derived from Quickbird and topographic information to derive AGB of black locust plantations in the Loess Plateau of China. Multispectral high-resolution Worldview-2 images were also used by Zhu et al [46] to derive AGB for mangrove ecosystems using neural networks algorithms. Pereira et al [47] used GeoEye-1 imagery to estimate biomass and carbon stock of coffee crops in Brazil exploiting a series of VIs.…”

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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Periurban forests are key to offsetting anthropogenic carbon emissions, but they are under constant threat from urbanization. In particular, secondary Neotropical forest types in Andean periurban areas have a high potential to store carbon, but are currently poorly characterized. To address this lack of information, we developed a method to estimate periurban aboveground biomass (AGB)-a proxy for multiple ecosystem services-of secondary Andean forests near Bogotá, Colombia, based on very high resolution (VHR) GeoEye-1, Pleiades-1A imagery and field-measured plot data. Specifically, we tested a series of different pre-processing workflows to derive six vegetation indices that were regressed against in situ estimates of AGB. Overall, the coupling of linear models and the Ratio Vegetation Index produced the most satisfactory results. Atmospheric and topographic correction proved to be key in improving model fit, especially in high aerosol and rugged terrain such as the Andes. Methods and findings provide baseline AGB and carbon stock information for little studied periurban Andean secondary forests. The methodological approach can also be used for integrating limited forest monitoring plot AGB data with very high resolution imagery for cost-effective modelling of ecosystem service provision from forests, monitoring reforestation and forest cover change, and for carbon offset assessments.

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Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Clerici

Rubiano

Abd-Elrahman

et al. 2016

Forests

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“…For instance, in Qi'ao Island, Guangdong Province, China, predicted AGB of mature Kandelia candel mangroves ranged from 15.51 kg/m 2 to 40.66 kg/m 2 with the average value of 24.77 kg/m 2 , and artificially-restored Sonneratia apetala mangroves ranged from 3.4 kg/m 2 to 23.42 kg/m 2 with the average value of 11.38 kg/m 2 [16]. However, in this study, we did not find Kandelia candel and Sonneratia apetala mangroves, and thus, this limits the comparison possibilities.…”

Section: Predicating Agbmentioning

confidence: 99%

“…Compared to other terrestrial ecosystems, a few studies associated with field samples and remotely-sensed data for producing thematic maps over mangrove forests can be found [11][12][13][14][15][16]. Clough et al [17], Green and Clark [18] and Green et al [14] established a relationship between vegetation indices derived from remotely-sensed data and in situ LAI samples for mangroves.…”

Section: Introductionmentioning

confidence: 99%

“…To the same extent, we could find only a few studies that mapped AGB of mangroves over a large area. For example, recently, Zhu et al [16] retrieved mangrove AGB from field data and WorldView-2 (WV2) satellite images. This study tested Sonneratia apetala and Kandelia candel mangrove species and a limited number of vegetation indices for AGB mapping over the study site.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

2016

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Abstract:Mangroves are one of the most productive coastal communities in the world. Although we acknowledge the significance of ecosystems, mangroves are under natural and anthropogenic pressures at various scales. Therefore, understanding biophysical variations of mangrove forests is important. An extensive field survey is impossible within mangroves. WorldView-2 multi-spectral images having a 2-m spatial resolution were used to quantify above ground biomass (AGB) and leaf area index (LAI) in the Rapid Creek mangroves, Darwin, Australia. Field measurements, vegetation indices derived from WorldView-2 images and a partial least squares regression algorithm were incorporated to produce LAI and AGB maps. LAI maps with 2-m and 5-m spatial resolutions showed root mean square errors (RMSEs) of 0.75 and 0.78, respectively, compared to validation samples. Correlation coefficients between field samples and predicted maps were 0.7 and 0.8, respectively. RMSEs obtained for AGB maps were 2.2 kg/m 2 and 2.0 kg/m 2 for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.

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“…The object-based image analysis is based on segmentation, which can divide the image into ecologically significant, spatially continuous, and spectrally homogeneous objects or pixel groups [18]. Image segmentation was processed in eCogniton Developer 9.0 using the multiresolution segmentation (MRS) algorithm, which is a very popular segmentation algorithm for land use and mangrove classification [17,20,[27][28][29]. MRS is a "bottom-up" merging approach, which begins with pixel-sized objects and iteratively grows to merge small objects into a larger one until the homogeneous threshold of the image object is exceeded.…”

Section: Feature Selection Mangroves Classification and Image Segmementioning

confidence: 99%

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

et al. 2018

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Abstract:In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms-decision tree (DT), support vector machine (SVM), and random forest (RF)-were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar's test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.

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Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images

Cited by 103 publications

References 62 publications

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

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