Spatiotemporal evolution and multi-scenario prediction of habitat quality in the Yellow River Basin

Recent high-profile efforts have called for integrating ecosystem-service values into important societal decisions, but there are few demonstrations of this approach in practice. We quantified ecosystem-service values to help the largest private landowner in Hawaii, Kamehameha Schools, design a land-use development plan that balances multiple private and public values on its North Shore land holdings (Island of O'ahu) of ∼10,600 ha. We used the InVEST software tool to evaluate the environmental and financial implications of seven planning scenarios encompassing contrasting land-use combinations including biofuel feedstocks, food crops, forestry, livestock, and residential development. All scenarios had positive financial return relative to the status quo of negative return. However, tradeoffs existed between carbon storage and water quality as well as between environmental improvement and financial return. Based on this analysis and community input, Kamehameha Schools is implementing a plan to support diversified agriculture and forestry. This plan generates a positive financial return ($10.9 million) and improved carbon storage (0.5% increase relative to status quo) with negative relative effects on water quality (15.4% increase in potential nitrogen export relative to status quo). The effects on water quality could be mitigated partially (reduced to a 4.9% increase in potential nitrogen export) by establishing vegetation buffers on agricultural fields. This plan contributes to policy goals for climate change mitigation, food security, and diversifying rural economic opportunities. More broadly, our approach illustrates how information can help guide local land-use decisions that involve tradeoffs between private and public interests.

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).