Presentation on “Isabela Land Cover Assessment and Watershed Mapping” by Dr
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BUILDING A BENCHMARK FOR CHANGE: Land Cover Updating in Isabela For Sustainable Future Presentation on “Isabela Land Cover Assessment and Watershed Mapping” by Dr. J. Andres Ignacio December 07, 2020 PRESENTATION OUTLINE Structure and Key Talking Points for Discussion PRESENTATION OUTLINE • 1.) ESSC-ILCA Project Overview a.) Context of the Problem b.) Project Objectives c.) Methodologies Implemented d.) Project Scope and Data limitations • 2.) Land Cover Updating: The Research Process a.) Data acquisition, preparation and input b.) Analysis, validation and finalization c.) Knowledge generation and method documentation d.) Knowledge sharing and collaboration • 3.) Land Cover Assessment: Initial Results and Findings a.) Overall accuracy and validity b.) Forest and land cover statistics c.) Upland and protected areas • 4.) Sample ILCA datasets integration: Watershed Mapping ESSC-ILCA PROJECT OVERVIEW Context of the Problem | Project Objectives | Methodologies Implemented | Project Scope and Data Limitations CONTEXT OF THE PROBLEM Biological Diversity NSMNP Water Regulation Cultural Identity CONTEXT OF THE PROBLEM • The existing spatial on the Northern Sierra Madre and the province of Isabela might be limited in terms of its timeliness and usefulness in land management initiatives. NSMNP• More recent advances in remote sensing technology will allow to create a more holistic, realistic and robust baseline maps. PROJECT OBJECTIVES San Pablo • ILCA’s goal is to contribute to Cabagan monitoring forest and land cover Maconacon change in the Sierra Madre landscape Tumauini Divilacan (especially to the 9 municipalities that are part of the NSMP). Ilagan City Palanan • Produce an updated and detailed land cover assessment for the San Mariano province of Isabela, which encompasses the Northern Sierra Dinapigue Madre Natural Park. METHODOLOGIES IMPLEMENTED Recent Advances in Remote Sensing Technologies and Multi-stakeholder Engagement Approach • The classification methodology employed by ESSC, Sen2Agri, was developed by its Belgian partner Universite Catholique de Louvain and is used by the European Space Agency as its primary toolbox for land cover assessment using data from the Sentinel 2 satellite platform. Sentinel 2 allows much more frequent data collection for any point on Earth (every 5 days) and provides high accuracy and rapid land cover data. • Specifically, it has the following strengths and advantages: 1.) High Temporal and Spatial Resolution Satellite Imagery 2.) Avoidance of fuzzy and confusing land use and/or land cover classes PROJECT SCOPE AND DATA LIMITATIONS 1.) The project focuses on creating a robust baseline and updated data for the land cover of the province. No further land cover analysis (i.e. change detection) were conducted since no existing comparable data is available. Yet, standardization is possible to make initial assumptions. 2.) Primary and secondary data used for validating identified forest formations and classifications identified is limited. Further studies on this can be explored by interested individuals or groups. 3.) Due to COVID19 restrictions, validation sessions were done remotely. Technical difficulties and recurring localized lockdowns in most of the LGUs resulted to a 73% completion rate of online consultations. PROJECT SCOPE AND DATA LIMITATIONS 4.) Data collection challenges in ground truthing activities for some of the barangays in selected municipalities/cities due to safety issues (i.e. unstable weather conditions and presence of rebel groups) 5.) Misclassification of few selected crops such as calamansi and banana identified in limited spaces, reflected by smaller pixels in the output maps. 6.) Majority of the training datasets built and gathered from the field survey were focused on accessible areas particularly near existing road networks. LAND COVER UPDATING: The Research Process Data Acquisition, Preparation, and Input | Analysis, Validation and Finalization | Knowledge Generation and Method Documentation | Knowledge Sharing and Collaboration Objective 4: Knowledge Sharing THE RESEARCH PROCESS and Collaboration Objective 3: Knowledge Generation and Method Documentation Objective 2: Analysis, Validation and Finalization Objective 1: Data Acquisition, Preparation and Input THE RESEARCH PROCESS 1.1 Inception meeting and consultation with local stakeholders and collaborators in the province KEY ACCOMPLISHMENTS AND MILESTONES Objective 1: Consultative meetings with key local collaborators in Data Acquisition, Preparation and the province particularly Mabuwaya Foundation, ISU- Input ✓ Cabagan and CVPED Joint PAMB En Banc Resolution No. 01, series of 2019: Approving the Request of ESSC to Conduct ILCA ✓ Project in all Protected Areas in Isabela (3 DENR- PAMB’s in Isabela) Resolution No. 441, series of 2019: Signing of Memorandum of Agreement with the Provincial ✓ Government of Isabela THE RESEARCH PROCESS 1.2 Researched (desktop and on-site) and conducted ground truthing activities to gather, acquire or purchase existing paper and digital maps, studies, reports, tables and data in other formats Objective 1: Data Acquisition, KEY ACCOMPLISHMENTS AND MILESTONES Preparation and Input 67 relevant studies and reports were acquired from local stakeholders, partners and collaborators in the ✓ field (i.e. Mabuwaya Foundation) 302 additional geospatial documents were collected from both national line agencies and local ✓ government units Collected 13,088 geotagged photos from 36 out of 37 municipalities (97%) and 914 out of 1,055 barangays ✓ (89%) THE RESEARCH PROCESS 2.1 Employed a classification procedure which takes the most updated training and validation polygons Objective 2: into account (i.e. April 2018 – February 2020) to Analysis, Validation produce the best classification output and Finalization KEY ACCOMPLISHMENTS AND MILESTONES 589 satellite images were pre-processed but only 350 images were further used for feature extraction and ✓ analyzed; 10 iterations conducted Around 12,208 training polygons were created and ✓ prepared prior and during the project 19 land cover features were distinguished; labels for ✓ major lakes and other identifiable rivers were added THE RESEARCH PROCESS 2.2 Improvement and enhancement of the training datasets were made through independent accuracy Objective 2: assessments and stakeholders’ validation Analysis, Validation and Finalization KEY ACCOMPLISHMENTS AND MILESTONES Online training course was designed to involve interested local collaborators in the enhancement of ✓ built training datasets, resulting to an additional 748 training polygons based on the capstone project outputs In lieu of the multi-stakeholder validation sessions, ILCA Virtual ‘Mapathon’ was held from August to ✓ November. A total of 27 out of 37 (73%) participated in individual online consultation THE RESEARCH PROCESS Objective 3: 3.1 Created a simple interactive web maps Knowledge Generation and with uploaded geospatial datasets for easy Method Documentation access of interested local stakeholders, partners and collaborators. KEY ACCOMPLISHMENTS AND MILESTONES Uploaded output maps and shapefiles along with metadata and process documentation, in a separate ✓ web page dedicated for ILCA datasets THE RESEARCH PROCESS Objective 4: 4.1 Local stakeholders were given technical Knowledge Sharing training that focuses on integrating data and Collaboration outputs from the project. Also, to maximize the potential of the datasets local stakeholders and collaborators are encouraged to continually update and validate the GIS database KEY ACCOMPLISHMENTS AND MILESTONES Maintained strategic partnerships with local stakeholders and collaborators by strengthening ✓ ‘online’ community of practice (i.e ILCA-GIS group) Initial commitments and interests were forwarded by identified local partners in using the ILCA datasets ✓ produced for further studies, analysis and verification (i.e. Mabuwaya Foundation and ISU-CVPED) LAND COVER ASSESSMENT: Initial Results and Findings Overall Accuracy and Validity | Land Cover Statistics | Upland and protected areas INITIAL RESULTS AND FINDINGS: Overall Accuracy and Validity Overall Accuracy and Validity of ILCA Training Datasets 1 0.9 0.95 0.9 0.8 0.83 0.79 0.7 0.6 NT 0.5 1st OT 2nd OT 0.4 VM 0.3 0.2 0.1 0 ANNUAL OA ANNUAL KAPPA PERENNIAL OA PERENNIAL KAPPA NT – No training datasets 1st OT – 1st online training data enhancement Source: Isabela Land Cover Assessment 2020, ESSC-ILCA 2nd OT – 2nd online training data enhancement VM – Virtual ‘mapathon’ series INITIAL RESULTS AND FINDINGS: Overall Accuracy and Validity • Participation of local stakeholders in the validation of initial post- processed output maps increases the overall accuracy and precision of the training datasets • Involvement of local stakeholders in such process foster transparency in the process of delivering outputs. This also adds credibility and shared ownership of the final outcomes • Collaboration with local stakeholders and collaborators also promotes data acceptability which will facilitate the use of the final outputs of the ILCA project INITIAL RESULTS AND FINDINGS: Isabela Land Cover Statistics 2020 ISABELA LAND COVER STATISTICS Forest Areas Annual Cropland Other Land Land with Tree Cover Perennial Cropland 4% 10% 14% 41% 31% Source: Isabela Land Cover Assessment 2019, ESSC-ILCA INITIAL RESULTS AND FINDINGS: Forests Land Area Statistics in Isabela, 2019 % Share to the Forest Land Area of Isabela Mangrove 0.14 Ultramafic Forest 13.4 Limestone Forest 4.75 Secondary Forest 41.77 Primary