Evaluation Land Use Cover Changes Over 29 Years in Papua Province of Indonesia Using Remote Sensing Data

Abstract

Land use/cover change (LUCC) observation and determination have been extensively discussed in natural resources management, biodiversity and ecosystem preservation, land management also climate changes studies. An evaluation of the LUCC in Merauke, the easternmost city of Indonesia, was conducted to gain relevant information in agriculture and forestry based on historical data from remotely sensed land cover data. To obtain the historical dynamics of the LUCC, a supervised classification algorithm was implemented to the Landsat images of 1990, 1996, 2009, 2015, and 2019 as well as statistical analysis Wilcoxon signed-rank. This study aims to investigate and analyze the LUCC over a period of 29 years. The results show a significant decline in the forest area by around 7% in 2019. As compared to the LUCC in 1990, agriculture sectors such as dry-land agriculture, shrub-mixed dry land and paddy field gained an area higher than 50%, while the area of the water body is a declined by 0.61 % over the period. The evaluation of LUCC will help government development plans in the protection of commodities in this Province and the promotion for biomass and bioenergy resources.

Mixed-Methods Approach to Land Use Renewal Strategies in and around Abandoned Airports: The Case of Beijing Nanyuan Airport

“…Notably, the resolution of the imagery for each year was 30 m by 30 m, with medium accuracy with sufficient bands. Therefore, to accurately classify the land use of remote sensing images, a supervised classification technique was employed [34][35][36]. The present study adopted the Chinese land use classification system developed by Liu Jiyuan to categorize the land use types based on the natural conditions and characteristics of the site.…”

BSDSNet: Dual-Stream Feature Extraction Network Based on Segment Anything Model for Synthetic Aperture Radar Land Cover Classification

“…Precise land cover classification data play a vital role in regional development, such as natural resource management [1] and agricultural planning [2]. Numerous land classification tasks have relied on conventional optical imagery [3].…”

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

“…Multiple examples of accurate plant height estimates using UAV imagery have been shown in crops such as barley ( Hordeum vulgare L.) (Bendig et al ., 2014; Herzig et al ., 2021), cabbage ( Brassica oleracea var. capitata L.) (Moeckel et al ., 2018), cotton ( Gossypium hirsutum L.) (Feng et al ., 2019; Liu et al ., 2020), eggplant ( Solanum melongena L.) (Moeckel et al ., 2018), faba bean ( Vicia faba L.) (Ji et al ., 2022), maize (Anthony et al ., 2014; Geipel et al ., 2014; Grenzdörffer, 2014; Su et al ., 2019; Varela et al ., 2017; Anderson et al ., 2019; Malambo et al ., 2018; Shi et al ., 2016; Tirado et al ., 2020; Adak, Murray, Božinović, et al ., 2021; Letsoin et al ., 2023), potato ( Solanum tuberosum L.) (de Jesus Colwell et al ., 2021; Xie et al ., 2022; Njane et al ., 2023), rapeseed ( Brassica napus L.) (Xie et al ., 2021), sorghum ( Sorghum bicolor L.) (Chang et al ., 2017; Shi et al ., 2016; Watanabe et al ., 2017; Gano et al ., 2021), soybean ( Glycine max L.) (Li et al ., 2022), tomato ( Solanum lycopersicum L.) (Moeckel et al ., 2018), and wheat ( Triticum aestivum L.) (Holman et al ., 2016; Madec et al ., 2017; Michalski et al ., 2018; Volpato et al ., 2021). Studies on plant height using UAVs have shown variable levels of success when compared to manual measurements due to plant structure, field layout, and improvements in best practices as more research was completed (Holman et al ., 2016; Sweet et al ., 2022).…”