Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

Kamal, Muhammad; Phinn, Stuart; Johansen, Kasper

doi:10.3390/rs6020984

Cited by 56 publications

(37 citation statements)

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“…Semi-variograms are used for measuring the degree of dissimilarity between observations as a function of distance [46], and a review of its application in remote sensing was provided by Curran and Atkinson [47]. As demonstrated by Cohen [48], Johansen and Phinn [49], and Kamal et al [30], the semi-variogram can be used to explore and describe the spatial variation of objects of interest in the image data in various forest environments. The equation for semi-variance γ(h) is:…”

Section: Mangrove Vegetation Structure Characterizationmentioning

confidence: 99%

“…The semi-variograms in this paper were derived from 21-line transects positioned across the mangrove zonations on the WV-2 image, coinciding with the field transects. We used all bands of the WV-2 pan-sharpened data to produce semi-variograms, but only the green band is presented because it depicts the highest detailed level of mangrove vegetation information compared to the other bands [30].…”

Section: Mangrove Vegetation Structure Characterizationmentioning

confidence: 99%

“…Hence, remote sensing can provide information on mangroves at multiple scale levels depending on the user's need. By synthesizing the knowledge of the hierarchical structures of mangroves [20,21,23,29] with the empirical study of the optimum pixel size to extract mangrove features from remotely-sensed images [30], we can establish explicit relationships between spatial and temporal scales of mangrove features and the corresponding image spatial resolution at which to map these features ( Figure 1). This relationship guides the image analysts or interpreters to select the optimal image spatial resolution in order to accurately map a specific mangrove feature.…”

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Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

2015

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Providing accurate maps of mangroves, where the spatial scales of the mapped features correspond to the ecological structures and processes, as opposed to pixel sizes and mapping approaches, is a major challenge for remote sensing. This study developed and evaluated an object-based approach to understand what types of mangrove information can be mapped using different image datasets (Landsat TM, ALOS AVNIR-2, WorldView-2, and LiDAR). We compared and contrasted the ability of these images to map five levels of mangrove features, including vegetation boundary, mangrove stands, mangrove zonations, individual tree crowns, and species communities. We used the Moreton Bay site in Australia as the primary site to develop the classification rule sets and Karimunjawa Island in Indonesia to test the applicability of the rule sets. The results demonstrated the effectiveness of a conceptual hierarchical model for mapping specific mangrove features at discrete spatial scales. However, the rule sets developed in this study require modification to map similar mangrove features at different locations or when using image data acquired by different sensors. Across the hierarchical levels, smaller object sizes (i.e., tree crowns) required more complex classification rule sets. Incorporation of contextual information (e.g., distance and elevation) increased the overall mapping accuracy at the mangrove stand level (from 85% to 94%) and mangrove zonation level (from 53% to 59%). We found that higher image spatial resolution, larger object size, and fewer land-cover classes result in higher mapping accuracies. This study highlights the potential of selected images and mapping techniques to map mangrove features, and provides guidance for how to do this effectively through multi-scale mangrove composition mapping.

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Section: Mangrove Vegetation Structure Characterizationmentioning

confidence: 99%

Section: Mangrove Vegetation Structure Characterizationmentioning

confidence: 99%

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Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

2015

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“…It examines the spatial structure of mangrove features to understand the relationships between mangrove features and the optimum pixel size required to identify and map these features. This paper was published in Remote Sensing journal (Kamal et al 2014). No paper publication is associated with this chapter.…”

Section: Chapter 2: Research Approachmentioning

confidence: 99%

“…By synthesising the knowledge of the hierarchical structures of mangroves (Farnsworth 1998;Twilley et al 1999;Berger et al 2008;Feller et al 2010) with the empirical study of the optimum pixel size to extract mangrove features from remotely-sensed images (Kamal et al 2014), explicit relationships between spatial and temporal scales of mangrove features and the corresponding image spatial resolution at which to map these features, can be established ( Figure 4.1). This relationship guides the image analysts or interpreters to select the optimal image spatial resolution in order to accurately map a specific mangrove feature.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing for multi-scale mangrove mapping

Kamal¹

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Understanding the relationships between the size of mangrove vegetation features and the optimum image pixel size required to map these features is essential to support effective mapping and monitoring activities in this environment. Currently mangroves are under pressure from anthropogenic and natural disturbances, and up-to-date and accurate spatial information is required to support their management. Addressing ecological problems at the correct spatial scale is essential in mangrove environments. There is a lack of knowledge on the types and biophysical properties of mangroves, which can be mapped at different image spatial resolutions. This thesis integrated the spatial and temporal dimension of remote sensing data into a spatio-temporal continuum of mangrove ecology and developed guidelines for multi-scale image-based mangrove mapping. Three objectives were addressed to achieve the aim: (1) characterising mangrove spatial structure to produce an optimum pixel resolution scheme for image-based mangrove mapping; (2) assessing the applicability of the scheme to selected images for mangrove composition and leaf area index (LAI) mapping; and (3) developing guidelines for multi-scale mangrove mapping. The research sites were located in Moreton Bay, Australia, and Karimunjawa Island, Indonesia. Landsat TM, ALOS AVNIR-2, and WorldView-2 images were used for both sites; with additional LiDAR data and a very high-spatial resolution aerial photograph for Moreton Bay.After two introductory chapters, chapter three focused on the development of a method for estimating the optimum pixel size to map different sizes of mangrove features accurately. The extent of dominant mangrove structural features including tree/shrub crowns, canopy gaps and vegetation formation or community, could be detected using semi-variogram analysis applied to image datasets with different spatial resolutions. The findings showed a gradual loss of mangrove information detail with increasing pixel size. Specific mangrove features could be optimally mapped from a specific pixel size and spectral bands or indices. A pixel size of ≤ 2 m was suitable for mapping canopy and inter-canopy-related features within mangrove vegetation features (such as shrub crown, canopy gaps and single tree crowns), while a pixel size of ≥ 4 m was appropriate for mapping mangrove vegetation formation, communities and larger mangrove features. An optimum pixel resolution scheme was produced for mangrove mapping that served as a basis for an inversion approach to map mangrove features using remote sensing image datasets.Chapters 4 and 5 focused on the application of the optimum pixel size scheme to the selected images with different spatial resolutions, to map mangrove composition and LAI, respectively.Object-based image analysis successfully produced mangrove composition maps at discrete spatial ii scales. The findings suggested that the accuracy of the maps was a result of the interaction between the image spatial resolution, the scale of the targeted objects and t...

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Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

Doughty

Ambrose

Okin

et al. 2021

Remote Sens Ecol Conserv

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Coastal wetland biomass is an important indicator of wetland productivity, carbon storage, health, and vulnerability to climate change. The ability to estimate aboveground biomass (AGB) in wetlands at ecologically relevant scales is complicated by the spatial variability inherent to patterns in wetland vegetation and the biogeomorphic processes that help create them. Remote sensing provides an approach for mapping wetland biomass, but the spatial resolutions of satellite and airborne imagery often constrain the types of ecological patterns and processes that can be detected. Unmanned Aerial Vehicles (UAVs) have previously been used to capture fine-scale (≤1 m) variability in AGB in coastal wetland settings. However, it remains unclear if a UAV approach to estimating wetland biomass is transferrable across diverse wetland sites or how it compares to commonly used satellite-based approaches. Here, we test the capabilities of UAVs in remotely quantifying AGB and compare biomass estimation using UAV and Landsat satellite imagery (30 m resolution) in several wetland sites in Southern California. Field surveys highlight significant spatial variability in wetland plant community AGB and height that influence remote biomass estimation. Relationships between UAV vegetation indices and AGB were site-specific and influenced by vegetation types. Biomass estimation using UAVs (r 2 = 0.40, RMSE = 534.6 g m −2 ) showed better correlation with NDVI than a Landsatbased approach (r 2 = 0.26, RMSE = 596.8 g m −2 ). We found combining highresolution UAV AGB maps and Landsat NDVI to develop AGB models showed the highest correlation (r 2 = 0.45, RMSE = 659.7 g m −2 ) and provided additional spatial information to aid scaling field data to satellite imagery. Overall, UAVs captured more spatial complexity in aboveground biomass at finer scales than is possible with moderate-resolution Landsat pixels, indicating that UAVs can be used to characterize patterns of within-marsh variability resulting from local-scale (≤ 100s of meters) ecological processes.

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Characterizing the Spatial Structure of Mangrove Features for Optimizing Image-Based Mangrove Mapping

Cited by 56 publications

References 43 publications

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

Remote sensing for multi-scale mangrove mapping

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

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