Ms. Charmaine A. Cruz Profile

Charmaine A. Cruz

at Univ of the Philippines

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Proceedings Article | 24 October 2018 Paper

Remote sensing-based estimation of seagrass percent cover and LAI for above ground carbon sequestration mapping

Ayin Tamondong, Charmaine Cruz, Rey Rusty Quides, Mikko Garcia, John Andrew Cruz, Jaime Guihawan, Ariel Blanco

Proceedings Volume 10778, 1077803 (2018) https://doi.org/10.1117/12.2324695

KEYWORDS: Carbon, Satellites, Earth observing sensors, Carbon sequestration, Satellite imaging, Remote sensing, Ecosystems

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Seagrasses are distinct flowering plants which thrive underwater. They are part of a complex ecosystem that supports different forms of life. Recent studies found out that coastal wetlands – mangroves, saltmarshes, and seagrass, are far more proficient in sequestering and storing carbon than terrestrial ecosystems. Although seagrasses occupy only 0.2% of the area of the oceans, they sequester approximately 15% of total carbon storage in the ocean. Several remote sensing techniques are available to map and monitor seagrasses but most of them focus only on extent and area coverage. To estimate the carbon sequestration of seagrass beds, aside from extent, other parameters are needed such as leaf area index, percent cover, density, biomass etc., However, there are limits in mapping seagrass parameters using remote sensing. The reflectance measured by sensors is affected by other factors such as water absorption, turbidity, dissolved organic matter, depth and phytoplankton which affects the backscattering of energy. In this study, different remotely sensed datasets and field data were used to measure the parameters needed to estimate the carbon sequestration. Multispectral satellite images such as Sentinel-2 and PlanetScope were utilized to map the distribution and percent cover. High-resolution RGB images obtained by unmanned aerial vehicle (UAV) were also utilized to correlate field data gathered parameters. Field data such as species, percent cover, leaf area index, canopy height and above ground biomass were gathered in situ. Data extracted from different remote sensing technologies were put together to support the estimation of carbon sequestration of seagrass beds.

Proceedings Article | 14 December 2016 Presentation

Mapping of coral reefs using the combined bathymetric lidar and CASI datasets (Conference Presentation)

Charmaine Cruz, Ayin Tamondong, Gay Go

Proceedings Volume 9999, 99990I (2016) https://doi.org/10.1117/12.2241965

KEYWORDS: LIDAR, Remote sensing, Data modeling, Associative arrays, Data acquisition, Environmental sensing, Imaging systems, Spatial resolution, Spectral resolution, Reflectivity

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Mapping of coral reefs provides information to support the conservation and monitoring of this vulnerable benthic habitat. Coral reef environment has a high level of complexity and spatial heterogeneity, however, typical maps derived using remote sensing data only includes classification of benthic communities. The study aims to update the status of coral reef classification through the advancement of remote sensing technology in the Philippines. This shows the coral community condition in the area. With the use of hyperspectral Compact Airborne Spectrographic Imager (CASI) and bathymetric LiDAR, data were acquired in Apo Reef, Province of Mindoro. Apo Reef is known as the second largest contiguous coral reef in the world. The image taken has a spatial resolution of 0.5 meters with spectral resolution of approximately 10nm between 385nm to 1047nm wavelength regions. Pre-processing of LiDAR data includes extraction of surface bottom and generating derivatives such as Digital Surface Model (DSM), Digital Terrain Model (DTM), rugosity, and slope. Data on spectral reflectance of coral reef types and other substrates, bathymetry, validation points and geotagged underwater video were gathered in situ simultaneous with the image acquisition. Derivative analysis is then applied to the field spectra to determine the wavelength bands for discriminating coral reef types. The optimal subset bands and LiDAR derivatives were used in classifying coral reef types using the supervised classification. Geotagged photos and sampling points were used to validate and assess the accuracy of the map.

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