主要研究方向：

  湿地遥感分类、湿地预测模拟、生态遥感监测与评估

主要担任课程：

  地理统计分析原理、地理智能分析

主要科研项目：

[1]     国家自然科学基金青年项目，42301413   ，“密集时序影像数据支持下的海岸带湿地遥感识别方法研究”，2024.01-2026.12，主持

[2]     环境遥感与数字城市北京市重点实验室开放课题，“预测未来多情景模式的土地利用变化及其对城市生态系统服务的影响”，2020.05-2021.05，主持

[3]     国家自然科学基金重点项目，U1901219，“粤港澳大湾区湿地资源遥感监测及其生态功能评估研究”，2020.01-2023.12，参与

[4]     生态环境部项目，2018-S075，“10个试点国家公园近20年生态变化遥感监测与评估分析”，2018.10-2019.06，参与

1.  主要学术论文（一作或通讯）：

[1]     Kaifeng   Peng, Weiguo Jiang, Peng Hou, et al. (2024).   Detailed wetland-type classification using Landsat-8 time-series images: a   pixel- and object-based algorithm with knowledge (POK), GIScience &   Remote Sensing, 61:1, 2293525. (SCI二区，IF=6.7)

[2]     Kaifeng   Peng, Weiguo Jiang, Xuejun Wang, et al.   (2023). Evaluation of future wetland changes under optimal scenarios and land   degradation neutrality analysis in the Guangdong-Hong Kong-Macao Greater Bay Area.   Science of the Total Environment, 879(March), 163111. (SCI一区，IF=9.8)

[3]     Kaifeng   Peng, Weiguo Jiang, Peng Hou, et al.(2023).   Continental-scale wetland mapping: a novel algorithm for detailed-type   wetland classification based on time series Sentinel-1 /2 images. Ecological   Indicators.148, 110113. （SCI二区，IF=6.9）

[4]     Kaifeng   Peng, Weiguo Jiang, Ziyan Ling Z, et al.   (2021). Evaluating the potential impacts of land use changes on ecosystem   service value under multiple scenarios in support of SDG reporting: A case study   of the Wuhan urban agglomeration. Journal of Cleaner Production, 307: 127321.   (SCI一区，IF=11.1，ESI高被引)

[5]     Kaifeng   Peng, Weiguo Jiang, Yue Deng, et al. (2020).   Simulating wetland changes under different scenarios based on integrating the   random forest and CLUE-S models: A case study of Wuhan Urban Agglomeration.   Ecological Indicators, 117: 106671. (SCI二区，IF=6.9)

[6]     彭凯锋,   蒋卫国,   侯鹏,   凌子燕,   牛振国,   毛德华,   黄卓.   结合多源专题数据和目视解译的大区域密集湿地样本数据生产.   遥感学报,   2024, 28(2): 334-345.(EI，中文核心)

[7]     彭凯锋,   蒋卫国,   侯鹏,   等.   三江源国家公园植被时空变化及其影响因子[J].   生态学杂志,   2020, 39(10): 3388-3396. (中文核心)

[8]     彭凯锋,   蒋卫国,   邓越.   武汉城市圈湿地受损程度识别及驱动因素分析[J].   自然资源学报,   2019, 34(8): 1694-1707. (CSSCI，中文核心)

[9] Kaifeng Peng,   Weiguo Jiang, Yue Deng. Simulating urban land use changes by incorporating   logistic regression and CLUE-S model: a case study of Wuhan city. 2021 28th   International Conference on Geoinformatics, pp. 1-5.（EI，会议论文）

2.      合作发表学术论文：

[1]     Zhuo   Li, Weiguo Jiang, Kaifeng Peng, et al. (2024). Comparative analysis of   land use change prediction models for land and fine wetland types: Taking the   wetland cities Changshu and Haikou as examples. Landscape and Urban Planning,   243, 104975. (SCI一区，IF=9.7)

[2]     Ze   Zhang, Weiguo Jiang, Kaifeng Peng, et al. (2023) Assessment of the   impact of wetland changes on carbon storage in coastal urban agglomerations   from 1990 to 2035 in support of SDG15.1. Science of the Total Environment,   877, 162824. (SCI一区，IF=9.8)

[3]     Zhuo   Li, Weiguo Jiang, Peng Hou, Kaifeng Peng, et al. (2023). Changes in   the ecosystem service importance of the seven major river basins in China   during the implementation of the Millennium development goals (2000–2015) and   sustainable development goals (2015–2020). Journal of Cleaner Production, 443,   139787. (SCI一区，IF=11.1)

[4]     Xiaoya   Wang, Weiguo Jiang, Kaifeng Peng, et al. (2022) A framework for fine   classification of urban wetlands based on random forest and knowledge rules:   taking the wetland cities of Haikou and Yinchuan as examples, GIScience &   Remote Sensing, 59:1, 2144-2163. (SCI二区，IF=6.7)

[5]     Geng   Zhipeng, Weiguo Jiang, Kaifeng Peng, et al. (2023) Wetland Mapping and   Landscape Analysis for Supporting International Wetland Cities: Case Studies   in Nanchang City and Wuhan City.  IEEE Journal of Selected Topics in   Applied Earth Observations and Remote Sensing, 16, 8858-8870. (SCI二区，IF=5.5)

[6]     邓雅文,   蒋卫国,   王晓雅,   彭凯锋.   基于随机森林算法和知识规则的国际湿地城市精细湿地分类——以常德市为例[J].   遥感学报,   2023, 27(06): 1426-1440. (EI，中文核心)

[7]     陈妍,   侯鹏,   王媛,   彭凯锋,   等.生态保护地协同管控成效评估[J].   自然资源学报,   2020, 35(04): 779-787. (CSSCI，中文核心)

发明专利

[1] 彭凯锋, 蒋卫国, 侯鹏,   王强,   王雪君.   基于密集时序遥感影像的长时序地物样本自动生产方法[P].   天津市:   CN117292154B, 2024-02-06.  

社会兼职：

International   Journal of Digital Earth，Ecological Indicator，IEEE   JSTAR等期刊审稿人