Remote sensing for multi-scale mangrove mapping

Abstract

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...

Priority Areas for Mangrove Conservation to Support Disaster Mitigation Efforts in Pacitan Bay

“…Some of the characteristics of mangroves that are used as keys to remote sensing interpretation are mangrove location, zoning patterns, canopy texture characteristics, and canopy's spectral reflectance characteristics [16]. Heenkenda et al [17] stated that there are two challenges in mapping wetland vegetation (including mangroves) using remote sensing techniques.…”

Estimation Model of Mangrove Carbon Stock Using LDCM Imagery

“…where: = measured carbon stock, = carbon stock estimation, ̅ = average measured carbon stock (Weng, 2010a;Chuvieco et al, 2010;(Köhlet al, 2006)Vicharnakorn et al, 2014Kamal, 2015;Alparone et al, 2015;Thenkabail, 2016;Alan et al, 2017;)…”

“…This result was different from the Sentinel-2 imagery (published in Muhsoniet al, 2018) at 0.247056 ton 100m -2 , although the best vegetation index corresponded to NNIP. Kamal (2015) performed a study in Karimunjawa using Landsat TM imagery, and showed SR as the best index, with RMSE of 1.23 ton 900m -2 . Meanwhile, SPOT 5 and Landsat TM were used by Hamdanet al (2013) and NDVI was obtained as superior, alongside the adoption of non-linear regression.…”

The Effect of Field Spectral Reflectance Measurement Distance to the Spectral Reflectance ofRhizophora stylosa

“…The selection of sample location was guided by WorldView-2 pan-sharpened image (0.5m pixel size), that is by identifying accessible Rhizophora stylosa at the edge of the mangrove forest where measurement of spectral reflectance at different distance can be performed. From previous research [27] Avicennia marina, Rhizophora stylosa, Bruguiera gymnorrhiza, and Lumnitzera racemosa can be distinguished by visual interpretation of WV-2 images because they have specific canopy patterns and clustered at specific locations. From this site, a single sampling point was taken purposively based on the availability of target species and measured at several distances.…”