南京林业大学学报(自然科学版) ›› 2024, Vol. 48 ›› Issue (5): 255-266.doi: 10.12302/j.issn.1000-2006.202306008
蒋路凡1(), 苏慧毅1, 张银1, 刘琴琴1, 张小伟2,*(), 李明诗1
收稿日期:
2023-06-12
修回日期:
2023-08-04
出版日期:
2024-09-30
发布日期:
2024-10-03
通讯作者:
* 张小伟(xiaoweiz099618@gmail.com),高级工程师。作者简介:
蒋路凡(lf_jiang@yeah.net)。
基金资助:
JIANG Lufan1(), SU Huiyi1, ZHANG Yin1, LIU Qinqin1, ZHANG Xiaowei2,*(), LI Mingshi1
Received:
2023-06-12
Revised:
2023-08-04
Online:
2024-09-30
Published:
2024-10-03
摘要:
【目的】为发挥不同单分类器各自的优势进而采用集成学习方式提高土地覆盖分类精度,据此比较不同土地覆盖变化模拟模型性能从而执行最优的土地覆盖变化预测,为土地资源合理开发与利用决策制定提供参考。【方法】基于南京市江宁区2000、2010和2020年的Landsat TM/OLI影像,结合研究区实际定义了水体、建筑、林地、草地、耕地和未利用地等6种土地覆盖分类体系,在测试了最大似然法、马氏距离法、最小距离法、神经网络和支持向量机等基分类器性能基础上,采用随机森林和证据理论2种不同的集成学习方法对5种基分类器的分类结果进行集成,比较了集成性能后构建了最终的土地覆盖分类结果。然后,基于2000和2010年的最优集成土地覆盖分类图,运用CA-Markov、PLUS和ANN-CA模型分别对2020年研究区的土地覆盖格局进行模拟,并将不同的模拟结果与2020年真实集成分类结果进行了空间一致性检验,以此确定土地覆盖变化预测的最佳模型并用其预测2030年江宁区的土地覆盖模式。【结果】在单分类器分类结果中,2000年支持向量机算法取得了最佳分类效果,总体精度达到了88.75%,Kappa系数为0.77;2010年神经网络方法表现最佳,总体精度为88.75%,Kappa系数为0.83;2020年最大似然法取得了最佳分类效果,总体精度为82.75%,Kappa系数为0.74。在2种集成方法中,随机森林在2000年取得了最佳集成分类效果,总体精度和Kappa系数分别为91.25%和0.85;证据理论在2010年取得了最佳集成效果,总体精度和Kappa系数分别为90.80%和0.86;随机森林在2020年取得了最佳集成效果,总体精度和Kappa系数分别为93.75%和0.91。就土地覆盖预测而言,PLUS模型获得了98.54%的空间一致性。根据PLUS模型预测2030年土地覆盖结果可知,江宁区各土地覆盖类型变化较小,建设用地略有扩张但范围有限,耕地稍减少但在可控范围内。林地、草地和未利用地变化较小,水体面积相对减少。【结论】恰当的集成策略能明显改进土地覆盖分类精度,合适的、适应局部土地利用状态和政策调控的预测模型可有效预测区域未来土地覆盖分布模式,它们能为区域土地利用、城市管理决策等提供更可靠的方法和数据支持。
中图分类号:
蒋路凡,苏慧毅,张银,等. 基于多分类器集成的土地覆盖分类及预测研究[J]. 南京林业大学学报(自然科学版), 2024, 48(5): 255-266.
JIANG Lufan, SU Huiyi, ZHANG Yin, LIU Qinqin, ZHANG Xiaowei, LI Mingshi. Land cover classification and prediction based on multiple classifier ensemble learning[J].Journal of Nanjing Forestry University (Natural Science Edition), 2024, 48(5): 255-266.DOI: 10.12302/j.issn.1000-2006.202306008.
表1
江宁区各单分类器土地覆盖分类精度"
单分类器 base classifier | 2000年 | 2010年 | 2020年 | ||||
---|---|---|---|---|---|---|---|
总体精度/% overall accuracy | Kappa系数 Kappa coefficient | 总体精度/% overall accuracy | Kappa系数 Kappa coefficient | 总体精度/% overall accuracy | Kappa系数 Kappa coefficient | ||
SVM | 88.75 | 0.77 | 88.75 | 0.81 | 80.25 | 0.70 | |
NN | 87.00 | 0.77 | 88.75 | 0.83 | 80.50 | 0.71 | |
MinDC | 68.25 | 0.53 | 80.00 | 0.70 | 76.50 | 0.63 | |
MLC | 78.50 | 0.66 | 71.25 | 0.60 | 82.75 | 0.74 | |
MahDC | 70.00 | 0.55 | 74.50 | 0.65 | 80.25 | 0.71 |
表2
两种集成分类结果精度验证统计参数"
集成分类器 integrate classifier | 2000年 | 2010年 | 2020年 | ||||
---|---|---|---|---|---|---|---|
总体精度/% overall accuracy | Kappa系数 Kappa coefficient | 总体精度/% overall accuracy | Kappa系数 Kappa coefficient | 总体精度/% overall accuracy | Kappa系数 Kappa coefficient | ||
随机森林 random forests | 91.25 | 0.85 | 85.50 | 0.80 | 93.75 | 0.91 | |
证据理论 evidence theory | 90.75 | 0.85 | 90.80 | 0.86 | 91.00 | 0.86 |
表3
Comparison of actual land cover area of Jiangning District in 2020 and predicted area of PLUS model in 2030 单位:km2"
项目 item | 耕地 crop land | 林地 forest land | 草地 grass land | 水体 water | 未利用地 unused land | 建筑 built-up land |
---|---|---|---|---|---|---|
2020年实际面积 actual area | 801.00 | 216.38 | 16.41 | 63.30 | 0.17 | 464.08 |
2030年PLUS预测面积 PLUS forecast area | 790.70 | 214.05 | 16.40 | 60.29 | 0.16 | 479.67 |
差值 difference | -10.30 | -2.33 | -0.01 | -3.01 | -0.01 | 15.59 |
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