USING SUPPORT VECTOR MACHINE TO IDENTIFY LAND COVER CHANGE DURING COVID-19 PANDEMIC IN KOMODO NATIONAL PARK, INDONESIA

The Covid-19 pandemic affects many areas of life, including the tourism sector. Furthermore, it significantly reduced the number of people visiting tourist destinations, and the reduction has helped to improve the environment in the National Park. Therefore, this study aims to present a satellite image classification method using Support Vector Machine to identify changes in the vegetation area of Komodo National Park. The satellite image used was created with Google Earth Pro with a resolution of 1920 x 1280 pixels using data collected in 2019 and 2020 before and during the pandemic. This study focuses on six tourist destinations in Komodo National Park: Loh Liang, Loh Buaya, Padar Island, Kanawa Island, Pink Beach, and Loh Sebita. The image was pre-processed using radiometric calibration, atmospheric correction, and contrast enhancement. The results of the pre-processing showed that segmentation will be performed to distinguish the area between one class and another. Furthermore, the image will be classified into five classes using the Support Vector Machine, including Soil, Vegetation, Built-Up Area, Deep Water, and Shallow Water. The measurement of the area of vegetation from 2019 and 2020 using Otsu’s thresholding showed environmental changes. Meanwhile, environmental improvements occurred in seven areas in the vegetation area category, with a 31.86% rise from 2019 to 2020. The increase in the area of green areas in the Komodo National Park all because tourist restriction and there is no climate fluctuations during the time of study. © 2022, Russian Geographical Society. All rights reserved.

A framework for mapping and monitoring human-ocean interactions in near real-time during COVID-19 and beyond.

The human response to the COVID-19 pandemic set in motion an unprecedented shift in human activity with unknown long-term effects. The impacts in marine systems are expected to be highly dynamic at local and global scales. However, in comparison to terrestrial ecosystems, we are not well-prepared to document these changes in marine and coastal environments. The problems are two-fold: 1) manual and siloed data collection and processing, and 2) reliance on marine professionals for observation and analysis. These problems are relevant beyond the pandemic and are a barrier to understanding rapidly evolving blue economies, the impacts of climate change, and the many other changes our modern-day oceans are undergoing. The "Our Ocean in COVID-19″ project, which aims to track human-ocean interactions throughout the pandemic, uses the new eOceans platform (eOceans.app) to overcome these barriers. Working at local scales, a global network of ocean scientists and citizen scientists are collaborating to monitor the ocean in near real-time. The purpose of this paper is to bring this project to the attention of the marine conservation community, researchers, and the public wanting to track changes in their area. As our team continues to grow, this project will provide important baselines and temporal patterns for ocean conservation, policy, and innovation as society transitions towards a new normal. It may also provide a proof-of-concept for real-time, collaborative ocean monitoring that breaks down silos between academia, government, and at-sea stakeholders to create a stronger and more democratic blue economy with communities more resilient to ocean and global change.