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Exploring the comprehensive impact of landscape pattern changes on regional ecosystem service values (ESVs) over a long time series is significant for optimizing ecosystem management. This study took Hainan Tropical Rainforest National Park (HTRNP) as a case and first assessed its five vital ecosystem services (ESs): water supply (WS), water purification (WP), carbon storage (CS), soil retention (SR), and habitat quality (HQ). Based on the ESs assessment results, we further calculated their ESVs and quantified the responses of ESVs to landscape pattern changes during 1980–2020. The results revealed that: (1) Forestland is the basal landscape type of HTRNP. Landscape patterns changed significantly after 2000; the proportion of both cultivated land and grassland decreased, while the proportion of forestland, water, and construction land increased; with the areas and landscape dominance of both forestland and water increased, the agglomeration and connectivity of the overall landscape increased and its homogenization decreased. (2) WS, WP, CS, and SR services tended to weaken, and HQ service tended to strengthen. The spatial heterogeneities of WS and SR changed significantly over time. WS, HQ, SR, and CS are the main contributors to the total ESV. During 1980–2020, the four ESVs of WS, WP, SR, and CS showed a decreasing trend; HQ’s ESV tended to increase, and the total ESV tended to decrease. (3) The increase of areas and dominance in forestland and water was the main reason that HQ’s ESV tended to increase, and WP’s ESV and CS’s ESV tended to decrease. The construction land scale was relatively small, so its impacts on ESVs were limited. The responses of both WS’s ESV and SR’s ESV to landscape pattern changes were insignificant due to the impacts of topographic and climatic factors. The study results provide a reference for managing and optimizing HTRNP’s ecosystem to improve its integrated benefits of crucial ESs.

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Aggregate-associated OCS (A), TNS (B), and TPS (C) in Chinese fir plantations with different stand types. OCS, TNS and TPS stand for the stocks of organic carbon, total nitrogen, and total phosphorus in different aggregate sizes, respectively. ** and * stand for significant differences among the different soil depths at P < 0.01 and P < 0.05, respectively. nsStands for no significant differences among the different soil depths.
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Example of Mapillary images (https://www.mapillary.com/) taken at four points where the typical landscapes in Indonesia in Sumatra were recorded in field notes in 1978 (https://fieldnote.archiving.jp/): (a) rice paddy (geolocation based on the field note: 0°41’58.9”S, 100°36’02.4”E; geolocation based on the Mapillary image: 0°42’13.0”S, 100°35’56.0”E; location gap of about 500 m), (b) banana (geolocation based on the field note: 3°44’18.5”S, 104°39’33.6”E; geolocation based on the Mapillary image: 3°44’52.2”S, 104°39’28.1”E; location gap of about 1,100 m), (c) cassava (geolocation based on the field note: 5°19’12.9”S, 105°11’59.1”E; geolocation based on the Mapillary image: 5°18’40.0”S, 105°11’21.4”E; location gap of about 1,500 m), and (d) rubber (geolocation based on the field note: 5°19’49.6”S, 105°12’20.5”E; geolocation based on the Mapillary image: 5°19’54.4”S, 105°11’47.2”E; location gap of about 1,100 m). The Mapillary images are provided under the Creative Commons Attribution ShareAlike license (CC-BY-SA; https://www.mapillary.com/).
Review
22 February 2023

Recent advances in satellite-borne optical sensors led to important developments in the monitoring of tropical ecosystems in Asia, which have been strongly affected by recent anthropogenic activities and climate change. Based on our feasibility analyses conducted in Indonesia in Sumatra and Sarawak, Malaysia in Borneo, we discuss the current situation, problems, recent improvements, and future tasks regarding plant phenology observations and land-cover and land-use detection. We found that the Multispectral Instrument (MSI) on board the Sentinel-2A/2B satellites with a 10-m spatial resolution and 5-day observational intervals could be used to monitor phenology among tree species. For the Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary satellite with a 1,000-m spatial resolution and 10-min observational intervals, we found that the time-series in vegetation indices without gaps due to cloud contamination may be used to accurately detect the timing and patterns of phenology among tree species, although the spatial resolution of the sensor requires further improvement. We also found and validated that text and pictures with geolocation information published on the Internet, and historical field notes could be used for ground-truthing land cover and land use in the past and present time. The future development of both high frequency (≤ 10 min) and high spatial resolution (≤ 10 m) optical sensors aboard satellites is expected to dramatically improve our understanding of ecosystems in the tropical Asia.

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