基于遥感数据,以农业地区为重点,为伊朗建立先进的高分辨率土地利用/土地覆被数据集(ILULC-2022)

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-11-27 DOI:10.1016/j.compag.2024.109677
Neamat Karimi, Sara Sheshangosht, Maryam Rashtbari, Omid Torabi, Amirhossein Sarbazvatan, Masoumeh Lari, Hossein Aminzadeh, Sina Abolhoseini, Mortaza Eftekhari
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引用次数: 0

摘要

本研究提出了 2022 年伊朗的首个高分辨率土地利用/土地覆盖数据集(ILULC-2022),重点关注农业地区。本研究采用了两级决策树面向对象图像分析(OBIA-DT)模型,该模型包括对谷歌地球图像中的研究区域进行分割,并利用哨兵-2 卫星图像中的多时信息进行分类。在对精细分辨率图像进行分割后,OBIA-DT 模型的第一级基于所收集的实地数据集(共收集了约 52,000 个实地数据)建立了轻型 LULC 地图,该地图大致确定了农业用地的组成部分,但没有区分灌溉和非灌溉耕地。第二层使用从哨兵-2 图像和补充数据层中获得的多时指数,绘制出完整的土地利用、土地利用变化图,将耕地进一步区分为灌溉地和雨水灌溉地,并对灌溉地进行了四个不同的子分类。通过采用这种方法,伊朗所有流域的土地利用、土地利用变化和林业地图被划分为 16 个不同的等级,不同的农业用地被划分为两种雨水灌溉耕地(雨水灌溉农业和农林业)和五种灌溉耕地(果园、秋季作物、春季作物、多种作物和休耕作物)。根据收集到的实地数据,ILULC-2022 地图的总体准确度在从寒冷温带到炎热干旱等不同气候盆地中分别显示出 85% 到 97% 的范围。结果显示,主要灌溉作物类别的用户准确度和生产者准确度介于 91 % 到 96 % 之间。根据这项研究的结果,伊朗的农业总面积为 2090±210 万公顷,约占伊朗土地总面积的 13%。在这一农业区中,灌溉农业用地(包括灌溉地和果园)和雨水灌溉农业用地的面积分别为 1020±108 万公顷和 1070±102 万公顷,大部分农业区位于气候温和湿润的盆地。ILULC-2022 数据集是未来土地利用、土地利用变化和土地利用变化检测的基准,也是伊朗实现可持续发展目标的宝贵参考。
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An advanced high resolution land use/land cover dataset for Iran (ILULC-2022) by focusing on agricultural areas based on remote sensing data
This study presents the first high-resolution Land Use/Land Cover dataset for Iran in 2022 (ILULC-2022), with a particular emphasis on the agricultural areas. This research employed a two-level Decision Tree Object-Oriented Image Analysis (OBIA-DT) model which incorporated segmentation of the study area derived from Google Earth images, and classification using multi-temporal information derived from Sentinel-2 satellite imagery. After segmentation of fine resolution images, the first level of the OBIA-DT model established based on the collected field datasets (about 52,000 field data were collected) to build a light LULC map which broadly identified agricultural land components without differentiating between irrigated and non-irrigated cultivations. The second level used multi-temporal indices derived from Sentinel-2 imagery and supplementary data layers to produce a complete LULC map wherein cropland areas was distinguished further into irrigated and rainfed lands, with four distinctive sub-classifications for irrigated lands. By employing this approach, a LULC map of all basins of Iran were classified into sixteen distinct classes, with different agricultural lands divided into two rainfed croplands (rainfed farming and agroforestry) and five irrigated lands (orchards, fall crops, spring crops, multiple crops, and fallow crops). According to the collected field data, the overall accuracy of ILULC-2022 maps exhibited a range from 85 to 97 % for basins with varying climates ranging from cold and temperate to hot and dry, respectively. Results reveal that the major irrigated crop classes had a user’s accuracy and producer’s accuracy ranging from 91 % to 96 %. Based on the findings of this study, the total area of agricultures in Iran encompasses 20.9 ± 2.1 million ha, constituting approximately 13 % of the Iran’s total land area. Within this agricultural expanse, irrigated (comprising irrigated lands and orchards) and rainfed agricultural lands are delineated as 10.2 ± 1.08 and 10.7 × ± 1.02 million ha, respectively, with most agricultural areas located in basins with moderate to humid climates. The ILULC-2022 dataset serves as a benchmark for future LULC change detection and is a valuable reference for efforts aimed at achieving sustainable development goals in Iran.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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