| Detection of catchment-scale gully-affected areas using unmanned aerial vehicle (UAV) on the Chinese Loess Plateau K Liu, H Ding, G Tang, J Na, X Huang, Z Xue, X Yang, F Li ISPRS International Journal of Geo-Information 5 (12), 238, 2016 | 64 | 2016 |
| Automatic recognition of loess landforms using Random Forest method W Zhao, L Xiong, H Ding, G Tang Journal of Mountain Science 14, 885-897, 2017 | 49 | 2017 |
| Extraction of terraces on the Loess Plateau from high-resolution DEMs and imagery utilizing object-based image analysis H Zhao, X Fang, H Ding, J Strobl, L Xiong, J Na, G Tang ISPRS International Journal of Geo-Information 6 (6), 157, 2017 | 47 | 2017 |
| An object-based approach for two-level gully feature mapping using high-resolution DEM and imagery: A case study on hilly loess plateau region, China K Liu, H Ding, G Tang, AX Zhu, X Yang, S Jiang, J Cao Chinese Geographical Science 27, 415-430, 2017 | 46 | 2017 |
| Large-scale mapping of gully-affected areas: An approach integrating Google Earth images and terrain skeleton information K Liu, H Ding, G Tang, C Song, Y Liu, L Jiang, B Zhao, Y Gao, R Ma Geomorphology 314, 13-26, 2018 | 39 | 2018 |
| Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping H Ding, K Liu, X Chen, L Xiong, G Tang, F Qiu, J Strobl Remote Sensing 12 (5), 21, 2020 | 27 | 2020 |
| The effect of terrain factors on rice production: A case study in Hunan Province HD Chenzhi Wang, Zhao Zhang, Jing Zhang, Fulu Tao, Yi Chen Journal of Geographical Sciences 29 (2), 287-305, 2019 | 26 | 2019 |
| Object‐based large‐scale terrain classification combined with segmentation optimization and terrain features: A case study in China NP Jiaming Na, Hu Ding, Wufan Zhao, Kai Liu, Guoan Tang Transactions in GIS 25 (4), 2021 | 22 | 2021 |
| Space-for-time substitution in geomorphology: A critical review and conceptual framework X Huang, G Tang, T Zhu, H Ding, J Na Journal of Geographical Sciences 29, 1670-1680, 2019 | 22 | 2019 |
| Evaluation of three different machine learning methods for object-based artificial terrace mapping—a case study of the loess plateau, China H Ding, J Na, S Jiang, J Zhu, K Liu, Y Fu, F Li Remote Sensing 13 (5), 1021, 2021 | 21 | 2021 |
| Stability analysis unit and spatial distribution pattern of the terrain texture in the northern Shaanxi Loess Plateau H Ding, J Na, X Huang, G Tang, K Liu Journal of Mountain Science 15 (3), 577-589, 2018 | 16 | 2018 |
| Cellular automata for simulating land-use change with a constrained irregular space representation: A case study in Nanjing city, China J Zhu, Y Sun, S Song, J Yang, H Ding Environment and Planning B: Urban Analytics and City Science 48 (7), 1841-1859, 2021 | 12 | 2021 |
| UAV-based terrain modeling under vegetation in the chinese Loess plateau: A deep learning and terrain correction ensemble framework J Na, K Xue, L Xiong, G Tang, H Ding, J Strobl, N Pfeifer Remote Sensing 12 (20), 3318, 2020 | 10 | 2020 |
| Large-scale detection of the tableland areas and erosion-vulnerable hotspots on the Chinese Loess Plateau K Liu, J Na, C Fan, Y Huang, H Ding, Z Wang, G Tang, C Song Remote Sensing 14 (8), 1946, 2022 | 8 | 2022 |
| Evaluation of mangrove wetlands protection patterns in the Guangdong–Hong Kong–Macao Greater Bay area using time-series Landsat imageries T He, Y Fu, H Ding, W Zheng, X Huang, R Li, S Wu Remote Sensing 14 (23), 6026, 2022 | 6 | 2022 |
| Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China J Na, J Zhu, J Zheng, S Di, H Ding, L Ma ISPRS International Journal of Geo-Information 10 (5), 308, 2021 | 6 | 2021 |
| Rotation-aware building instance segmentation from high-resolution remote sensing images W Zhao, J Na, M Li, H Ding IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 5 | 2022 |
| 湖南省地形因素对水稻生产的影响 王琛智， 张朝， 张静， 陶福禄， 陈一， 丁浒 地理学报 73 (9), 1792-1808, 2018 | 5 | 2018 |
| 基于坡度和坡向分析的 DCT 域 DEM 数字水印算法 刘爱利， 丁浒， 田丹， 王丽， 王少峰 武汉大学学报: 信息科学版 41 (7), 903-910, 2016 | 5 | 2016 |
| Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China X Wang, R Li, H Ding, Y Fu Remote Sensing 14 (3), 753, 2022 | 4 | 2022 |
Guoan TangNanjing Normal UniversityVerified email at njnu.edu.cn
