|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Stable classi?cation with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017显示文摘As the world strives to reduce the impact of population growth, urbanization, agricultural expansion, and climate change on food security, energy and water shortage, resource over-exploration, biodiversity loss, environmental pollution, and ultimately human health, timely and higher resolution land cover information is urgently needed to achieve the sustainable development goals of the United Nations. | Peng Gong Han Liu Meinan Zhang Congcong Li Jie Wang Huabing Huang Nicholas Clinton Luyan Ji Wenyu Li Yuqi Bai Bin Chen Bing Xu Zhiliang Zhu Cui Yuan Hoi Ping Suen Jing Guo Nan Xu Weijia Li Yuanyuan Zhao Jun Yang Chaoqing Yu Xi Wang Haohuan Fu Le Yu Iryna Dronova Fengming Hui Xiao Cheng Xueli Shi Fengjin Xiao Qiufeng Liu Lianchun Song | 2019 | Science Bulletin2019，64，6： | 166 |
| 2 | The first all-season sample set for mapping global land cover with Landsat-8 data显示文摘We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping. | Congcong Li Peng Gong Jie Wang Zhiliang Zhu Gregory S. Biging Cui Yuan Tengyun Hu Haiying Zhang Qi Wang Xuecao Li Xiaoxuan Liu Yidi Xu Jing Guo Caixia Liu Kwame O. Hackman Meinan Zhang Yuqi Cheng Le Yu Jun Yang Huabing Huang Nicholas Clinton | 2017 | Science Bulletin2017，62，7： | 21 |
| 3 | Projected impacts of climate change on protected birds and nature reserves in China显示文摘Knowledge about climate change impacts on species distribution at national scale is critical to biodiversity conservation and design of management programs.Although China is a biodiversity hot spot in the world,potential influence of climate change on Chinese protected birds is rarely studied. Here, we assess the impact of climate change on 108 protected bird species and nature reserves using species distribution modeling at a relatively fine spatial resolution(1 km) for the first time. We found that a large proportion of protected species would have potential suitable habitat shrink and northward range shift by 77–90 km in response to projected future climate change in 2080. Southeastern China would suffer from losing climate suitability, whereas the climate conditions in Qinghai–Tibet Plateau and northeastern China were projected to become suitable for more protected species. Onaverage, each protected area in China would experience a decline of suitable climate for 3–4 species by 2080. Climate change will modify which species each protected area will be suitable for. Our results showed that the risk of extinction for Chinese protected birds would be high, even in the moderate climate change scenario. These findings indicate that the management and design of nature reserves in China must take climate change into consideration. | Xueyan Li Nicholas Clinton Yali Si Jishan Liao Lu Liang Peng Gong | 2015 | Science Bulletin2015，60，19： | 8 |
| 4 | 基于元胞自动机降尺度方法的1km分辨率全球土地利用数据集(2010~2100)(英文)显示文摘 | 李雪草 俞乐 徐伊迪 杨军 宫鹏 Terry Sohl Nicholas Clinton Wenyu Li Zhiliang Zhu Xiaoping Liu | 2016 | Science Bulletin2016，61，21： | 3 |
| 5 | FROM-GC: 30 m global cropland extent derived through multisource data integration显示文摘We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps(i.e.FROM-GLC,Finer Resolution Observation and Monitoring,Global Land Cover;FROM-GLC=agg)and a 250-m cropland probability map.A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples.A decision tree was then applied to combine two 250-m cropland masks:one existing mask from the literature and the other produced in this study,with the 30-m global land cover map FROM-GLC-agg.For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical(FAOSTAT)database,a final global cropland extent map was composited from the FROM-GLC,FROM-GLC-agg,and two masked crop=land layers.From this map FROM-GC(Global Cropland),we estimated the global cropland areas to be 1533.83 million hectares(Mha)in 2010,which is 6.95 Mha(0.45%)less than the area reported by the Food and Agriculture Organization(FAO)of the United Nations for the year 2010.A country-by=country comparison between the map and the FAOSTAT data showed a linear relationship(FROM-GC=1.05*FAOSTAT-1.2(Mha)with R^(2)=0.97).Africa,South America,Southeastern Asia,and Oceania are the regions with large discrepancies with the FAO survey. | Le Yu Jie Wang Nicholas Clinton Qinchuan Xin Liheng Zhong Yanlei Chen Peng Gong | 2013 | International Journal of Digital Earth2013，6，6： | 3 |
| 6 | Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods显示文摘 | Iryna Dronova Peng Gong Nicholas E. Clinton Lin Wang Wei Fu Shuhua Qi Ying Liu | 2012 | Remote Sensing of Environment2012，，： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员