A Diversity Inventory Monitoring System of Riparian Vegetation

D. P. Putra, Nanda Satya Nugraha, T. Suparyanto, A. A. Hidayat, D. Sudigyo, B. Pardamean
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引用次数: 1

Abstract

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.
河岸植被多样性清查监测系统
为了加强保护区生物多样性的保护工作,森林清查对了解保护区自然演替过程和制定监测策略具有重要意义。此外,树木库存旨在监测该地区的产量。更具体地说,流域地区的树木清查在实现可持续发展目标(SDG)方面发挥着关键作用,特别是在河岸地带,这也是森林绿区的重要组成部分。然而,传统的盘存方法费时费力,因此需要开发一个专家系统来协助盘存监测。在本研究中,我们通过移动应用程序开发了一个监测系统,以收集,分析和可视化树木库存数据。该应用程序包括计算给定输入的保护区内所有树种的树木生物多样性、分布和丰富度所需的算法。在模型验证阶段,我们比较了传统的库存方法和我们提出的基于应用程序的方法,以计算两个河岸地点的多样性库存:Klaten保护公园和Wonosobo保护公园。经过3天的区域数据采集,与人工方法相比,我们提出的系统读取数据的准确率可以达到90%以上。这表明该系统可以帮助林业工人在不同地点进行更有效的树木清查。
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