尼日利亚奥贡州 Olokemeji 森林保护区智能森林生态系统可持续性评估中的地理信息系统和人工智能应用

V. A. Ijaware
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引用次数: 0

摘要

多年来,人类人口的增加加速了人为活动的增长,导致森林保护区转为其他土地用途。因此,研究人员必须通过使用地理信息系统(GIS)和人工智能(AI),采用省时、自动化和低成本的方法,重点绘制森林保护区地图,以保护现有森林保护区及其可持续性评估的实施。本研究旨在利用地理信息系统和人工智能应用,对奥贡州的 Olokemeji 森林保护区进行智能森林生态系统可持续性评估,目标如下:(i) 评估森林生态系统的现状;(ii) 确定研究区域面临的潜在威胁和风险;(iii) 制定保护和保存森林生态系统的可持续管理战略。为此,本研究采用地理信息系统和人工智能来评估 Olokemeji 森林保护区森林生态系统的空间特征。使用的主要数据包括陆地卫星图像、地面坐标和研究问卷。在数据生成和分析过程中,使用了 ArcGIS 10.2 软件中的基于对象的分类、归一化植被指数(NDVI)和土地利用土地覆盖。结果表明,2013 年,研究区约有 1657.115 公顷被密林覆盖,而到 2023 年,密林覆盖面积将减少到 1188.060 公顷,两者相差约 469.055 公顷。通过利用人工智能实施智能森林监测和评估系统,政府和商业集团应制定法规,重点降低森林保护区不断升级的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GIS and Artificial Intelligence Application in Smart Forest Ecosystem Sustainability Evaluation of Olokemeji Forest Reserve, Ogun State, Nigeria
The increase in human population over the years has accelerated growth in anthropogenic activities, which have led to the conversion of forest reserves to other land uses. In the sequel, it has become imperative for researchers to focus on the mapping of forest reserves through the use of GIS and Artificial Intelligence (AI) with time-efficient, automated, and low-cost methods to preserve the existing forest reserve and its sustainability evaluation implementation. This research aimed at utilizing GIS and artificial intelligence applications in smart forest ecosystem sustainability evaluation of Olokemeji forest reserve, Ogun State, with the following objectives: (i.) assessment of the current state of the forest ecosystem.(ii.) identify potential threats and risks to the study area and (iii.) develop sustainable management strategies for its conservation and preservation. In pursuance of this, GIS and AI were deployed in this study to assess the spatial characteristics of the forest ecosystem in Olokemeji forest reserve. Landsat imagery, ground coordinates, and a research questionnaire were the major data used. Object-based classification, Normalized Difference Vegetation Index (NDVI), and Land Use Land Cover in ArcGIS 10.2 software was deployed in data generation and analysis. The results showed that in 2013, about 1657.115 ha of the study area was occupied by dense forest cover while in 2023, it decreased to 1188.060 ha, with a difference of about 469.055 ha. By implementing smart forest monitoring and evaluation systems that use artificial intelligence, the government and commercial groups should set regulations focused on reducing the escalating risks to forest reserves.
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