SYNERGISTIC METHODS IN REMOTE SENSING DATA ANALYSIS FOR TROPICAL COASTAL ECOSYSTEMS MONITORING

Tropical coastal environments around the world have undergone rapid changes which made consequent of their ecosystems degradation inevitable. Despite numerous attempts to map their extent and distribution from space, the ability to relate the surface signals reflected and subsequently measured by remote imaging sensors to the biophysical characteristics of coastal habitat targets have been given inadequate attention. The dynamic characteristic of the coastal shallow water areas including tidal and wave forcing, water quality and environmental stresses, both natural and anthropogenic complicate this task. Hence there is a need to consider these factors in understanding images obtained from different sources taken at various periods. This research focuses on synergistic methods in multi-source image processing for assessing benthic coastal habitats such as corals, seagrass, and algae by examining image data covering these types of environment from space through the aid of theoretical remote sensing approaches. Our study area covers the Fukido river mouth area and Shiraho reef of Ishigaki Island located in southern Ryukus, Japan. Images were acquired from satellite-borne Ikonos, SPOT, ASTER and Landsat respectively in 2002. Principles of BRDF (bidirectional reflectance distribution functions) modelling, shallow water optics and radiative transfer have been utilized to explain shallow water reflectance values with biophysical properties such as distribution, abundance, morphology and depth as controlling parameters. To reinforce parameterizations and to validate computational results, field surveys were conducted to gather in-situ data including water quality (mainly chlorophyll-a and turbidity), sea surface conditions, benthic habitat cover, abundance and distribution, some of which are synchronous with image acquisition. Spectral profiles across the reef area benthic cover were also used for calibrating reflectance

A LiDAR-based flood modelling approach for mapping rice cultivation areas in Apalit, Pampanga

Majority of rice cultivation areas in the Philippines are susceptible to excessive flooding owing to intense rainfall events. The study introduces the use of fine scale flood inundation modelling to map cultivation areas in Apalit, a rice-producing municipality located in the province of Pampanga in the Philippines. The study used a LiDAR-based digital elevation model (DEM), river discharge and rainfall data to generate flood inundation maps using LISFLOOD-FP. By applying spatial analysis, rice cultivation zone maps were derived and four cultivation zones are proposed. In areas where both depth and duration exceed threshold values set in this study, varieties tolerant to stagnant flooding and submergence are highly recommended in Zone 1, where flood conditions are least favorable for any existing traditional lowland irrigation varieties. The study emphasizes that a decline in yield is likely as increasing flood extents and longer submergence periods may cause cultivation areas for traditional irrigated lowland varieties to decrease over time. This decrease in yield may be prevented by using varieties most suitable to the flooding conditions as prescribed in the rice zone classification. The method introduced in this study could facilitate appropriate rice cultivation in flood-prone areas

A LiDAR-based flood modelling approach for mapping rice cultivation areas in Apalit, Pampanga

Majority of rice cultivation areas in the Philippines are susceptible to excessive flooding owing to intense rainfall events. The study introduces the use of fine scale flood inundation modelling to map cultivation areas in Apalit, a rice-producing municipality located in the province of Pampanga in the Philippines. The study used a LiDAR-based digital elevation model (DEM), river discharge and rainfall data to generate flood inundation maps using LISFLOOD-FP. By applying spatial analysis, rice cultivation zone maps were derived and four cultivation zones are proposed. In areas where both depth and duration exceed threshold values set in this study, varieties tolerant to stagnant flooding and submergence are highly recommended in Zone 1, where flood conditions are least favorable for any existing traditional lowland irrigation varieties. The study emphasizes that a decline in yield is likely as increasing flood extents and longer submergence periods may cause cultivation areas for traditional irrigated lowland varieties to decrease over time. This decrease in yield may be prevented by using varieties most suitable to the flooding conditions as prescribed in the rice zone classification. The method introduced in this study could facilitate appropriate rice cultivation in flood-prone areas

THE PHIL-LIDAR 2 PROGRAM: NATIONAL RESOURCE INVENTORY OF THE PHILIPPINES USING LIDAR AND OTHER REMOTELY SENSED DATA

The Philippines embarked on a nationwide mapping endeavour through the Disaster Risk and Exposure Assessment for Mitigation (DREAM) Program of the University of the Philippines and the Department of Science and Technology (DOST). The derived accurate digital terrain models (DTMs) are used in flood models to generate risk maps and early warning system. With the availability of LiDAR data sets, the Phil-LiDAR 2 program was conceptualized as complementary to existing programs of various national government agencies and to assist local government units. Phil-LiDAR 2 aims to provide an updated natural resource inventory as detailed as possible using LiDAR point clouds, LiDAR derivative products, orthoimages and other RS data. The program assesses the following natural resources over a period of three years from July 2014: agricultural, forest, coastal, water, and renewable energy. To date, methodologies for extracting features from LiDAR data sets have been developed. The methodologies are based on a combination of object-based image analysis, pixel-based image analysis, optimization of feature selection and parameter values, and field surveys. One of the features of the Phil-LiDAR 2 program is the involvement of fifteen (15) universities throughout the country. Most of these do not have prior experience in remote sensing and mapping. With such, the program has embarked on a massive training and mentoring program. The program is producing more than 200 young RS specialists who are protecting the environment through RS and other geospatial technologies. This paper presents the program, the methodologies so far developed, and the sample outputs

MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a−c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r2 of 0.79 and an RMSE of 0.44 Mg using only four features, namely, σ°VH GLCM variance, σ°VH GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR

ESTIMATION OF MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES AND BIOPHYSICAL VARIABLES DERIVED FROM OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE AND SENTINEL-2

Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10 m, 20 m and 60 m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r2) values were obtained using multispectral band predictors for Sentinel-2 (r2 = 0.89) and Planetscope (r2 = 0.80); and vegetation indices for RapidEye (r2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r2 ranging from 0.62 to 0.92. Based on the r2 and root-mean-square errors (RMSE’s), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r2 = 0.92) and RapidEye data (r2 = 0.91)

FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM

ESTIMATING DBH OF TREES EMPLOYING MULTIPLE LINEAR REGRESSION OF THE BEST LIDAR-DERIVED PARAMETER COMBINATION AUTOMATED IN PYTHON IN A NATURAL BROADLEAF FOREST IN THE PHILIPPINES

Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett’s Test for Spherecity (BTS)

NATIONWIDE NATURAL RESOURCE INVENTORY OF THE PHILIPPINES USING LIDAR: STRATEGIES, PROGRESS, AND CHALLENGES

The Philippines has embarked on a detailed nationwide natural resource inventory using LiDAR through the Phil-LiDAR 2 Program. This 3-year program has developed and has been implementing mapping methodologies and protocols to produce high-resolution maps of agricultural, forest, coastal marine, hydrological features, and renewable energy resources. The Program has adopted strategies on system and process development, capacity building and enhancement, and expanding the network of collaborations. These strategies include training programs (on point cloud and image processing, GIS, and field surveys), workshops, forums, and colloquiums (program-wide, cluster-based, and project-based), and collaboration with partner national government agencies and other organizations. In place is a cycle of training, implementation, and feedback in order to continually improve the system and processes. To date, the Program has achieved progress in the development of workflows and in rolling out products such as resource maps and GIS data layers, which are indispensable in planning and decision-making. Challenges remains in speeding up output production (including quality checks) and in ensuring sustainability considering the short duration of the program. Enhancements in the workflows and protocols have been incorporated to address data quality and data availability issues. More trainings have been conducted for project staff hired to address human resource gaps. Collaborative arrangements with more partners are being established. To attain sustainability, the Program is developing and instituting a system of training, data updating and sharing, information utilization, and feedback. This requires collaboration and cooperation of the government agencies, LGUs, universities, other organizations, and the communities