Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (8): 1295-1306.doi: 10.12082/dqxxkx.2019.180631
GENG Renfang1,2,FU Bolin1,*(),CAI Jiangtao1,3,CHEN Xiaoyu4,LAN Feiwu1,YU Hangming1,LI Qingxun1
Received:
2018-12-05
Revised:
2019-03-20
Online:
2019-08-25
Published:
2019-08-25
Contact:
FU Bolin
E-mail:fbl2012@126.com
Supported by:
GENG Renfang,FU Bolin,CAI Jiangtao,CHEN Xiaoyu,LAN Feiwu,YU Hangming,LI Qingxun. Object-Based Karst Wetland Vegetation Classification Method Using Unmanned Aerial Vehicle images and Random Forest Algorithm[J].Journal of Geo-information Science, 2019, 21(8): 1295-1306.DOI:10.12082/dqxxkx.2019.180631
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Tab. 6
Classification accuracy of the various types of karst wetland (%)"
类别 | 用户精度 | 生产者精度 | |||||||
---|---|---|---|---|---|---|---|---|---|
精度评估 | 标准差 | 95% 置信区间 | 精度评估 | 标准差 | 95% 置信区间 | ||||
狗牙根-白茅-水龙 | 92.86 | 2.00 | 88.94 | 96.77 | 82.98 | 3.16 | 76.78 | 89.18 | |
柑橘 | 100.00 | 0.00 | 100.00 | 100.00 | 100.00 | 0.00 | 100.00 | 100.00 | |
竹子-马甲子-桂花 | 70.59 | 3.54 | 63.66 | 77.52 | 82.76 | 6.22 | 70.57 | 94.95 | |
菩提树 | 79.31 | 3.14 | 73.15 | 85.47 | 82.14 | 7.46 | 67.52 | 96.76 | |
岩溶河流-岩溶湖泊 | 94.87 | 1.71 | 91.52 | 98.23 | 94.87 | 4.02 | 87.00 | 102.74 | |
水稻 | 100.00 | 0.00 | 100.00 | 100.00 | 100.00 | 0.00 | 100.00 | 100.00 | |
建设用地 | 92.31 | 2.07 | 88.25 | 96.36 | 85.71 | 23.17 | 40.30 | 131.13 |
Tab. 7
Confusion matrix of different karst wetland vegetation types"
类别 | 狗牙根-白茅-水龙 | 柑橘 | 竹子-马甲子-桂花 | 菩提树 | 岩溶河流-岩溶湖泊 | 水稻 | 建设用地 | 总计 |
---|---|---|---|---|---|---|---|---|
狗牙根-白茅-水龙 | 39 | 0 | 1 | 1 | 1 | 0 | 0 | 42 |
柑橘 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 3 |
竹子-马甲子-桂花 | 5 | 0 | 24 | 4 | 0 | 0 | 1 | 34 |
菩提树 | 1 | 0 | 4 | 23 | 1 | 0 | 0 | 29 |
岩溶河流-岩溶湖泊 | 1 | 0 | 0 | 0 | 37 | 0 | 1 | 39 |
水稻 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 6 |
建设用地 | 1 | 0 | 0 | 0 | 0 | 0 | 12 | 13 |
总计 | 47 | 3 | 29 | 28 | 39 | 6 | 14 | 166 |
[1] | Guo M, Li J, Sheng C , et al. A review of wetland remote sensing[J]. Sensors, 2017,17(4):777. |
[2] | Fickas K C, Cohen W B, Yang Z . Landsat-based monitoring of annual wetland change in the Willamette Valley of Oregon, USA from 1972 to 2012[J]. Wetlands Ecology and Management, 2016,24(1):73-92. |
[3] | 方朝阳, 邬浩, 陶长华 , 等. 鄱阳湖南矶湿地景观信息高分辨率遥感提取[J]. 地球信息科学学报, 2018,18(6):847-856. |
[ Fang C Y, Wu H, Tao C H , et al. The wetland information extraction research of Nanji Wetland in Poyang Lake based on high resolution remote sensing image[J]. Journal of Geo-information Science, 2018,18(6):847-856. ] | |
[4] | 马祖陆, 蔡德所, 蒋忠诚 . 岩溶湿地分类系统研究[J]. 广西师范大学学报·自然科学版, 2009,27(2):101-106. |
[ Ma Z L, Cai D S, Jiang Z C . About karst wetland classification system[J]. Journal of Guangxi Normal University: Natural Science Edition, 2009,27(2):101-106. ] | |
[5] | 王鹏, 万荣荣, 杨桂山 . 基于多源遥感数据的湿地植物分类和生物量反演研究进展[J]. 湿地科学, 2017,15(1):114-124. |
[ Wang P, Wan R R, Yang G S . Advance in classification and biomass estimation of plants in wetlands based on multi-source remote sensing data[J]. Wetland Science, 2017,15(1):114-124. ] | |
[6] | 付波霖, 李颖, 张柏 , 等. 基于多频率极化SAR影像的洪河国家级自然保护区植被信息提取研究[J]. 湿地科学, 2019,17(2):199-209. |
[ Fu B L, Li Y, Zhang B , et al. Vegetation information extraction of Honghe National Nature Reserve using multi-frequency polarization SAR images[J]. Wetland Science, 2019,17(2):199-209. ] | |
[7] | 张磊, 宫兆宁, 王启为 , 等. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取[J]. 遥感学报, 2019,23(2):313-326. |
[ Zhang L, Gong Z N, Wang Q W , et al. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images[J]. Journal of Remote Sensing, 2019,23(2):313-326. ] | |
[8] | 陈琳琳, 董雪梅, 詹佳琪 , 等. 基于面向对象的GF-1遥感影像采煤沉陷区湿地分类[J]. 农业工程学报, 2018,34(9):240-247. |
[ Chen L L, Dong X M, Zhan J Q , et al. Classification of wetland based on object-oriented method in coal mining subsidence area using GF-1 remote sensing image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(9):240-247. ] | |
[9] | Gu Z, Ju W, Li L , et al. Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nanjing city, China[J]. Advances in Space Research, 2013,51(7):1186-1194. |
[10] | 李德仁, 李明 . 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报·信息科学版, 2014,39(5):505-513. |
[ Li D R, Li M . Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University, 2014,39(5):505-513. ] | |
[11] | Juliane B, Kang Y, Helge A , et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barely[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,39:79-87. |
[12] | 晏磊, 廖小罕, 周成虎 , 等. 中国无人机遥感技术突破与产业发展综述[J]. 地球信息科学学报, 2019,21(4):476-495. |
[ Yan L, Liao X H, Zhou C H , et al. The impact of UAV remote sensing technology on the industrial development of China: A review[J]. Journal of Geo-information Science, 2019,21(4):476-495. ] | |
[13] | 宋晓阳, 黄耀欢, 董东林 , 等. 融合数字表面模型的无人机遥感影像城市土地利用分类[J]. 地球信息科学学报, 2018,20(5):703-711. |
[ Song X Y, Huang Y H, Dong D L , et al. Urban land use classification from UAV remote sensing images based on digital surface model[J]. Journal of Geo-information Science, 2018,20(5):703-711. ] | |
[14] |
井然, 宫兆宁, 赵文吉 , 等. 基于无人机SfM数据的挺水植物生物量反演[J].生态学报,2017,37(22):7698-7709.
doi: 10.5846/stxb201609221908 |
[ Jing R,Gong Z N, Zhao W J , et al. Estimating biomass of emergent aquatic plants based on UAV SfM data[J].Acta Ecologica Sinica, 2017,37(22):7698-7709. ]
doi: 10.5846/stxb201609221908 |
|
[15] | 周在明, 杨燕明, 陈本清 . 基于无人机遥感监测滩涂湿地入侵种互花米草植被覆盖度[J]. 应用生态学报, 2016,27(12):3920-3926. |
[ Zhou Z M, Yang Y M, Chen B Q . Fractional vegetation cover of invasive spartina alterniflora in coastal wetland using unmanned aerial vehicle (UAV) remote sensing[J]. Chinese Journal of Applied Ecology, 2016,27(12):3920-3926. ] | |
[16] | Cao J, Leng W, Liu K , et al. Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models[J]. Remote Sensing, 2018,10(1):89. |
[17] | 肖武, 任河, 吕雪娇 , 等. 基于无人机遥感的高潜水位采煤沉陷湿地植被分类[J]. 农业机械学报, 2019,50(2):177-186. |
[ Xiao W, Ren H, Lu X J , et al. Vegetation classification based on UAV remote sensing in coal mining subsidence wetland with high ground-water level[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(2):177-186. ] | |
[18] | 邵亚, 蔡崇法, 赵悦 , 等. 桂林会仙湿地沉积物中磷形态及分布特征[J]. 环境工程学报, 2014,8(12):5311-5317. |
[ Shao Y, Cai C F, Zhao Y , et al. Forms and distribution of phosphorus in sediments of Huixian Wetland in Guilin[J]. Chinese Journal of Environmental Engineering, 2014,8(12):5311-5317. ] | |
[19] | 吴应科, 莫源富, 邹胜章 . 桂林会仙岩溶湿地的生态问题及其保护对策[J]. 中国岩溶, 2006,25(1):85-88. |
[ Wu Y K, Mo Y F, Zou S Z . Ecologic problem and protection method of karst wetland in Huixian, Guilin[J]. Carsologica Sinica, 2006,25(1):85-88. ] | |
[20] | 周晓敏, 赵力彬, 张新利 . 低空无人机影像处理技术及方法探讨[J]. 测绘与空间地理信息, 2012,35(2):182-184. |
[ Zhou X M, Zhao L B, Zhang X L . Discussion on technique and method of low level UAV image processing[J]. Geomatics & Spatial Information Technology, 2012,35(2):182-184. ] | |
[21] | Moffett K B, Gorelick S M . Distinguishing wetland vegetation and channel features with object-based image segmentation[J]. International Journal of Remote Sensing, 2013,34(4):1332-1354. |
[22] | Drăguţ L, Csillik O, Eisank C , et al. Automated parameterisation for multi-scale image segmentation on multiple layers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,88:119-127. |
[23] | Duro D C, Franklin S E, Dubé M G . A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery[J]. Remote sensing of environment, 2012,118:259-272. |
[24] | 詹国旗, 杨国东, 王凤艳 , 等. 基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究[J]. 地球信息科学学报, 2018,20(10):1520-1528. |
[ Zhan G Q, Yang G D, Wang F Y , et al. The random forest classification of wetland from GF-2 imagery based on the optimized feature space[J]. Journal of Geo-information Science, 2018,20(10):1520-1528. ] | |
[25] |
Breiman L . Random forests[J]. Machine Learning, 2001,45(1):5-32.
doi: 10.1023/A:1010933404324 |
[26] | Breiman L . Bagging predictors[J]. Machine Learning, 1996,24(2):123-140. |
[27] | Gislason P O, Benediktsson J A, Sveinsson J R . Random forests for land cover classification[J]. Pattern Recognition Letters, 2006,27(4):294-300. |
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