SPECTRAL LIBRARY OF NATIVE VEGETATION SPECIES
RESEARCH ABSTRACTS
Carla Mae M. Arellano, Aeron Adrian C. Maralit, Enrico C. Paringit, Czar Jakiri S. Sarmiento, Ayin M. Tamondong,
Regine Anne G. Faelga, Fe Andrea M. Tandoc, Rusty A. Lopez, Fatima Joy O. Pamittan, Celeste Z. Vidad
"Multi-temporal Analysis of Radar Backscatter of Dense and Sparse Forests
Using Sentinel-1A Collection in Google Earth Engine"
Radar data has been historically expensive and complex to process. However, in this milieu of cloud-computing platforms and open-source datasets, radar data analysis became more efficient and expanding its application in exploratory researches. This study performed multi-temporal analysis of radar backscatter to characterize dense and sparse forest from Sentinel-1 images. The area of study are reforested sites under the National Greening Program (NGP) of the Philippines. Ground data were collected: (1) in 2019, from a 1.35 ha -site in Brgy. Calula, Ipil, Zamboanga Sibugay, (2) in 2019, from a 1.10 ha- site in Brgy. Cabatuanan, Basay, Negros Oriental, and (3) from PhilLiDAR 2 – Project 3: FRExLS’ 2.4 ha -validated site in Ubay, Bohol. SAR intensities and NDVI values were derived from Sentinel-1 and Landsat images from Google Earth Engine, which is a cloud-based platform with a repository of satellite images and functionalities for data extraction and processing. The temporal variation in the C-band radar backscatter from 2014 to 2018 were analyzed. The results show, for the whole period of analysis, that: in VH polarization, dense forests range from -10 to -21 dB and sparse forests range from -14 to -21 dB, and in VV polarization, dense forests range from -5 to -13 dB and sparse forests range from -10 to -15 dB. Forest backscatter are expected to saturate over time, especially in dense forests. These variations are due to differences in forest species, landscape, environmental and climatic drivers, and phenomenon or interventions on the site.
Regine Anne G. Faelga, Czar Jakiri S. Sarmiento, Enrico C. Paringit, Ayin M. Tamondong, Aeron Adrian C. Maralit,
Fe Andrea M. Tandoc, Carla Mae M. Arellano, Rusty A. Lopez, Fatima Joy O. Pamittan, Celeste Z. Vidad
"Monitoring and Assessment of the National Greening Program in the Philippines Using Satellite Imageries and Financial Analytics: Approaches and Challenges"
The National Greening Program (NGP) is the flagship government reforestation effort of the Philippines. The NGP was implemented from 2011. The need to come up with a monitoring tool to scientifically quantify the implementation status of this program has been identified; thus the creation of the nationwide Monitoring and Assessment of Planting and other Activities (MAPA2) under the Digital Imaging for Monitoring and Evaluation Project (Project DIME). The approach is to utilize optical imagery such as Landsat, along with spatial and financial information to come up with a monitoring system for NGP. A total of 96,711 declared sites from 2011 to 2018 has been processed for NDVI time-series analysis to determine vegetation growth before and after its implementation. Scatterplot analysis was used for the spatial and financial information relating area and seedlings ratio to the appropriated budget. Field surveys were done to validate results from imagery. The approach was able to assess concluded, ongoing, and proposed projects for NGP. However, data inconsistencies and access should be addressed to make the system effective.
Rusty A. Lopez, Aeron Adrian C. Maralit, Enrico C. Paringit, Czar Jakiri S. Sarmiento, Ayin M. Tamondong,
Regine Anne G. Faelga, Fe Andrea M. Tandoc, Carla Mae M. Arellano, Fatima Joy O. Pamittan, Celeste Z. Vidad
"Development of an Automated System for Monitoring the Performance of Reforestation Activities Using Google Earth Engine"
Reforestation activities are vital to preserve the remaining forest resources of the Philippines. The National Greening Program was established in 2011 to facilitate these activities. It is logically and physically challenging for forest managers to monitor the vegetation growth of all planting sites by conducting field observations. Remote sensing contributes to a more efficient forest growth assessment. The purpose of the study is to test the capability of Google Earth Engine (GEE) to promptly evaluate the growth performance of all reforestation sites using available satellite imageries. Through the GEE platform, satellite data were collected and corresponding vegetation indices were computed for each planting site. Normalized Difference Vegetation Index time series covering the period from 2005 to present were generated to evaluate the vegetation growth of around 96,711 reforestation sites. Trend line comparisons of the vegetation index before and after planting were compared to detect vegetation change. On a nationwide level, results prove that GEE is capable of producing fully-automated vegetation growth assessments based on decades-worth of time series data for the Philippines
Aeron Adrian C. Maralit, Czar Jakiri S. Sarmiento, Rusty A. Lopez, Regine Anne G.Faelga, Enrico C. Paringit, Ayin G. Tamondong
"Site Suitability Classification Model for Reforestation Activity
Using Open Source Remote Sensing and GIS Data"
The negative effects of global warming has gained popular significance over the past few decades. Rapid deforestation and forest degradation are proven driving factors. Planting more trees is a logical mitigating solution. Conducting large-scale reforestation is challenging in all levels especially for foresters and environmental planners. One of the challenges stem from the determination of suitable planting areas dependent on the type of reforestation activity (e.g. planting timber, mixed/fruit trees, fuelwood, urban and mangroves). In this paper, open source remote sensing data were utilized to determine appropriate reforestation activities for a particular region. The study created a model to locate and classify suitable areas and their effective reforestation activities given several quantified environmental and biophysical factors. Data from 766 Philippine reforestation sites planted since 2013 were used to validate. Nine (9) suitability parameters were extracted for each site and used to run the classification model. These parameters were derived from open source remote sensing and Geographic Information System (GIS) data. The classification model accuracy generated a kappa coefficient of about 0.60 attesting that open source remote sensing and GIS data can be used to predict location-specific reforestation activity effectiveness. It was found that the most significant parameters determining the success of the model are bulk density, palmer’s drought severity index, slope, elevation and soil moisture.
Fatima Joy O. Pamittan, Czar Jakiri S. Sarmiento, Ayin M. Tamondong, Enrico C. Paringit, Regine Anne G. Faelga,
Aeron Adrian C. Maralit, Fe Andrea M. Tandoc, Carla Mae M. Arellano,​ Rusty A. Lopez, Celeste Z. Vidad
"Validating the Accuracy of Open-Source High Resolution Satellite Images
in Forest Inventory and Monitoring using Object-Based Image Analysis (OBIA)"
Establishment of systems for ground-based forest inventory and monitoring can be costly and challenging to implement for large-scale projects like the National Greening Program (NGP) of the Philippine government. Utilization of satellite images could be an apt alternative to the manual, ground-based methodology of forest inventory and monitoring since it requires much less time and resources, and it could be more cost-efficient if open-source datasets are used. This study compared the statistics (individual tree, tree crown, and tree height) derived from high resolution open-source satellite images (optical images of Worldview-3, Kompsat-3, and Planetscope from Philippine Earth Data Resource and Observation (PEDRO), Digital Elevation Model (DEM) of Shuttle Radar Topography Mission (SRTM) from PhilGIS, and Digital Surface Model (DSM) of ALOS-2 from the Japan Aerospace Exploration Agency (JAXA) to the validation data gathered through fieldwork on an NGP site in Basay, Negros Oriental using Object-Based Image Analysis (OBIA).
Fe Andrea M. Tandoc, Czar Jakiri S. Sarmiento, Ayin M. Tamondong, Enrico C. Paringit, Regine Anne G. Faelga,
Aeron Adrian C. Maralit, Carla Mae M. Arellano,​ Rusty A. Lopez, Fatima Joy O. Pamittan, Celeste Z. Vidad
"Canopy Cover And Biomass Estimation From Different Satellite Data
for Acacia mangium Plantation Basay, Negros Oriental"
Forest assessment and measurement can be costly, laborious and time-consuming when done manually. Remote Sensing aids by providing data of sufficient accuracy for large tracts of forest lands in the form of maps. These data can then assist in decision-making for better forest management. This study estimated canopy cover and above-ground biomass, both primary forest measurement parameters, using remotely-sensed data. Different satellite images such as Landsat, Quickbird, Kompsat, Planetscope and RapidEye were used to estimate canopy cover and above-ground biomass. these will then be compared to measurements obtained from a manual inventory—in this case, of an Acacia mangium plantation. The manual inventory was conducted in a National Greening Program (NGP) site in Basay, Negros Oriental. Field inventory involved Static Global Navigation Satellite System (GNSS) surveys and Total Station surveys to get the accurate location of trees present in the plot. Diameter-at-breast was measured for all trees. Height and crown diameter were measured for at least 10 percent of all trees in the plot.
Celeste Z. Vidad, Czar Jakiri S. Sarmiento, Ayin M. Tamondong, Enrico C. Paringit, Regine Anne G. Faelga,
Aeron Adrian C. Maralit, Fe Andrea M. Tandoc, Carla Mae M. Arellano,​ Rusty A. Lopez, Fatima Joy O. Pamittan
"Spectral Characterization of a Closed Canopy
and Open Canopy Forest in Northern Sierra Madre"
Abstract: Forest lands play crucial roles in nutrient recycling and climate regulation. The change of closed canopy forests to open canopy forests may indicate disturbance within the closed canopy forest. Within the local context of the Philippines, few studies have been conducted to monitor changes in closed canopy forest lands. Efforts to do so are limited by the spatial extent, remoteness and ruggedness of closed canopy forests. Satellite imagery can cover the spatial extent of forest lands as well as provide constant revisit periods for monitoring. However, while multispectral imaging can detect changes in land cover, it is not as effective when detecting the subtler change from closed canopy to open canopy. This study aims to provide baseline spectral characterization of a closed canopy forest. For this study, a hyperspectral sensor (EO1-Hyperion) with 198 band channels ranging from 426.82nm to 2395.50nm and a pixel size of 30m was used to characterize the spectral variations of closed canopy forest, open canopy forest, shrubs and cropland in Northern Sierra Madre, Philippines. Multiple endmember spectral mixture analysis (MESMA) was employed to sort the image into classes as well as to characterize intra-spectral variations among the identified classes. Several collection of spectral library endmembers were assembled and used to classify the hyperspectral image. The spectral libraries were optimized by using Endmember Average Root Mean Square Error (EAR), Minimum Average Spectral Angle (MASA) and Iterative Endmember Selection (IES). Results show that the highest accuracy was obtained using the spectral library optimized using EAR. Spectral distinction is minimal when classifying between open forest and shrub.