Syarifuddin Misbari

Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R2 = 0.977, p < 0.001) and OLI (R2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803 ± 0.47 kg in 2009, while it was 752.3 ± 0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB.

An approach for quantification of submerged seagrass biomass in shallow turbid coastal waters

In this paper, we introduced a new approach for absolute seagrass biomass determination using remote sensing technique. Two-step data processing of satellite remote sensing data was performed, namely: (i) detection and mapping of seagrass occurrence; and (ii) determination and mapping of seagrass biomass. The newly launched Landsat-8 Operational Land Imager band 2, 3 and 4 were used and applied in Merambong Shoals located in Straits of Johore, Malaysia that have various density of seagrass coverage in the region. The results indicated good agreement with in-situ verifications. The seagrass occurrence areas were classified at 90 percent overall classification accuracy (khat statistics=0.87) whilst the seagrass aboveground biomass was succesfully modelled agianst the in-situ observations (R2=0.9, p<;0.0001, n=1200). It is then extended to fully map the above seagrass biomass for the large coverage with good accuracy (RMSE+1.75g/m2).

Total aboveground biomass (TAGB) estimation using IFSAR: speckle noise effect on TAGB in tropical forest

Total Aboveground Biomass (TAGB) estimation is critically important to enhance understanding of dynamics of carbon fluxes between atmosphere and terrestrial ecosystem. For humid tropical forest, it is a challenging task for researchers due to complex canopy structure and predominant cloud cover. Optical sensors are only able to sense canopy crown. In contrast, radar technology is able to sense sub-canopy structure of the forest with penetration ability through the cloud for precise biomass estimation with validation from field data including diameter at breast height (DBH) of trees. This study is concerned about estimation of TAGB through the utilization of Interferometry Synthetic Aperture Radar (IFSAR). Based on this study, it is found that the stand parameters such as DBH and backscattered on IFSAR image has high correlation, R2=0.6411. The most suitable model for TAGB estimation on IFSAR is Chave Model with R2=0.9139. This study analyzes the impact brought by speckle noises on IFSAR image. It is found that filtering process has improves TAGB estimation about +30% using several filtering schemes especially Gamma filter for 11×11 window size. Using field data obtained from a primary tropical forest at Gerik, Perak, TAGBestimation can be validated and the assessment has been carried out.

Landslide Mapping and Assessment by Integrating Landsat-8, PALSAR-2 and GIS Techniques: A Case Study from Kelantan State, Peninsular Malaysia

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.

Light Penetration Ability Assessment of Satellite Band for Seagrass Detection Using Landsat 8 OLI Satellite Data

Seagrass distribution is controlled by light availability, especially at the deepest edge of the meadow. Light attenuation due to both natural and anthropogenically-driven processes leads to reduced photosynthesis. Reliability of satellite-based seagrass mapping under different water clarity that has different attenuation coefficient value is still not fully known. Understanding the minimum light requirements for growth is crucial when light conditions are insufficient to maintain a positive carbon balance, leading to a decline in seagrass growth and distribution. By comparing the seagrass-detected pixels at two different coastal locations with the corresponding depth from the nautical chart, the assessment of seagrass map derived from Landsat 8 OLI satellite data were performed. We presented the assessment of light penetration capability of Landsat 8 OLI bands in typical tropical coastal water of Malaysia, with special attention on the different water clarity that has different amount of light deprivation on the seagrass meadow.

Mapping snow-algae in Antarctic Peninsula with multi-temporal satellite remote sensing data

This article reports on the use of selected spectral image transform technique for detecting and mapping of snow algae growth using multi-temporal Landsat Thematic Mapper (TM), archived from early 80s to the recent Landsat-8 Operational Land Imager (OLI) acquired in March 2015. In-situ spectral radiometry samples during UTM-Antarctica summer 2015 expedition were used in air-to-ground correlation of the corresponding satellite data sets, later yield to retrieve snow algae occurrences in 2009-2015 summer. Multi-temporal analysis of the snow-vegetation growth extents extracted was then compared to against the climate-change effects of the corresponding area in Antarctic Peninsula. Hence, the indicator for snow-vegetation growth as effects of the climate-change could be inferred. Results indicated good agreement (Pearsons coefficient r=0.85, p<;0.01) between snow-vegetation growth extent with the temperature changes measured in the corresponding area. Recent in-situ verification of the selected sites in the region confirmed the agreements.