No abstract

Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.

Forest ecosystems play an important role in maintaining the stability of the biosphere and improving the ecological environment. The valuation of forest ecosystem services provides data to support the implementation of forest ecosystem conservation and the development of ecological-compensation standards. We used multiple sources of data, such as remote-sensing and ground data, and we employed the methods of substitute market, shadow project, and contingent valuation. We valued the forest ecosystem services of Pudacuo National Park in Shangri-La, China, which consisted of six functions: soil conservation, forest nutrient retention, water conservation, carbon fixation and oxygen released, forest health care, and atmospheric environmental purification. The results showed that: the value of forest ecological services in Pudacuo National Park was 4.49 × 109 yuan·a−1, with higher values of carbon fixation and oxygen released, water conservation, and forest health care, in the following order: carbon fixation and oxygen released (3.85 × 109 yuan·a−1), water conservation (3.40 × 108 yuan·a−1), forest health care (1.44 × 108 yuan·a−1), soil conservation (1.15 × 108 yuan·a−1), forest nutrient retention (3.29 × 107 yuan·a−1), and atmosphere environmental purification (1.17 × 107 yuan·a−1). In addition, the value of services per stand and unit area is discussed, and the results of the study will inform the government’s ecological-compensation criteria in high-quality environmental areas.