Investigating optimal time window for UAV terrain measuring by considering weather and land cover conditions: Taking the Northeast Black Soil Region as an example
WANG Yuepu1, QIN Wei2, YANG Wentao1, WANG Yujie1, WANG Yunqi1, JIANG Tao1
1. School of Soil and Water Conservation, Beijing Forestry University, 100083, Beijing, China; 2. Department of Sediment Research, China Institute of Water Resources and Hydropower Research, 100044, Beijing, China
Abstract：[Background] The Structure from Motion (SfM) technology based on the Unmanned Aerial Vehicle (UAV) platform is an important optical remote sensing technology to measure high-precision digital terrain. The SfM technology is often influenced by clouds and vegetation cover, whereas the UAV platform could be impacted by winds. This work aims to explore the optimal time window to measure terrain by the UAV-SfM survey in the Black Soil Region of Northeast China by considering dynamic changes of local climate and vegetation.[Methods] The study area covers three climatic zones:The cold temperate zone, the middle temperate zone and the warm temperate zone. For different climatic zones in the Black Soil Region of Northeast China, the climate data of a typical site from 1988 to 2017 and the dynamics of vegetation coverage and snow cover in 2020 were analyzed. Typical DJI UAV operating parameters were used as the initial screening conditions for annual average climate data (temperature, wind speed and precipitation), and the medium vegetation coverage condition (FVC<0.6) was then used as the screening basis for vegetation coverage to determine the appropriate period for the UAV terrain survey.[Results] 1) The temperature and wind speed of the three meteorological stations were analyzed comprehensively. It was found that the temperature curve changed as a quadratic function, and the average wind speed curve and the maximum continuous wind speed curve had the same trend. Temperature and wind speed were satisfied in the cold temperate zone from mid-April to early October, in the middle temperate zone from early April to mid-October, and in the warm temperate zone from mid-March to mid-November. 2) The probability of daily precipitation curves in the three meteorological stations fluctuated greatly and were not significantly representative, thus it could not be used as the standard for estimating investigating optimal time window. 3) By analyzing the land cover conditions of the three sites, it was found that the ground was covered with snow from October to March of the following year, and the vegetation coverage exceeded 0.6 from May to October, which made it impossible to carry out UAV topographic survey. The cover conditions that met terrain acquisition range from March to the end of April and from the end of September to November. Comprehensive analysis of meteorological and cover conditions, we found that the most suitable periods for the cold, middle and warm temperate zones were from mid-April to early May, from mid-April to mid-May, and from mid-March to the end of April or from the end of September to the beginning of November.[Conclusions] By applying the results of this paper, the UAV-SfM survey may effectively avoid adverse periods and ensure obtaining the best results. The findings of this work could guide terrain measuring works in the field of soil and water conservation.
王玥璞, 秦伟, 杨文涛, 王玉杰, 王云琦, 蒋涛. 基于气象和覆被条件考量的无人机地形勘察适宜期——以东北黑土区为例[J]. 中国水土保持科学, 2021, 19(4): 121-128.
WANG Yuepu, QIN Wei, YANG Wentao, WANG Yujie, WANG Yunqi, JIANG Tao. Investigating optimal time window for UAV terrain measuring by considering weather and land cover conditions: Taking the Northeast Black Soil Region as an example. SSWC, 2021, 19(4): 121-128.
徐朋,徐伟诚,罗阳帆,等. 基于无人机可见光遥感影像的耕地精准分类方法研究[J]. 中国农业科技导报,2019,21(6):79. XU Peng,XU Weicheng,LUO Yangfan,et al. Precise classification of cultivated Land based on visible remote sensing image of UAV[J]. Journal of Agricultural Science and Technology,2019,21(6):79.
[2]
许子乾. 基于无人机航测与激光雷达技术的林分特征及生物量估测[D]. 南京:南京林业大学,2019:2. XU Ziqian. Estimationon stand characteristic parameters and biomass of forest by UAV aerial photogrammetry and LiDAR[D]. Nanjing:Nanjing Forestry University,2019:2.
[3]
汤国安. 我国数字高程模型与数字地形分析研究进展[J]. 地理学报,2014,69(9):1305. TANG Guoan. Progress of DEM and digital terrain analysis in China[J]. Acta Geographica Sinica,2014,69(9):1305.
[4]
史扬子,黄婷婷,罗建勇,等. SfM摄影测量法测定切沟的精度评价[J]. 水土保持研究,2020,27(1):39. SHI Yangzi,HUANG Tingting,LUO Jianyong,et al. Evaluation on the accuracy of structure from motion (SfM) photogrammetry on gully surveying[J]. Research of Soil and Water Conservation,2020,27(1):39.
[5]
梁石, 刘万青, 王宁, 等. 消费型无人机用于估算生产建设项目人工堆积体土石方量[J]. 中国水土保持科学, 2020,18(3):132. LIANG Shi,LIU Wanqing,WANG Ning,et al. Application of consumer drone in the earthwork estimation of artificial deposit in construction project[J].Science of Soil and Water Conservation,2020,18(3):132.
[6]
廖凯涛, 宋月君, 张金生, 等. 无人机遥测技术在水土保持生态果园改造监测中的应用[J]. 中国水土保持科学, 2017,15(5):135. LIAO Kaitao,SONG Yuejun,ZHANG Jinsheng,et al. UAV remote sensing technology in the application of the ecological orchard construction of soil and water conservation[J].Science of Soil and Water Conservation, 2017,15(5):135.
[7]
LIAO J, ZHOU J, YANG W. Comparing LiDAR and SfM digital surface models for three land cover types[J]. Open Geosciences, 2021, 13(1):497.
[8]
林鑫,庞勇,李春干. 无人机密集匹配点云与机载激光雷达点云的差异分析[J]. 林业资源管理,2020(3):58. LIN Xin,PANG Yong,LI Chungan. Analysis of the deference between the UAV dense matching point cloud and airborne LiDAR point cloud[J]. Forest Resources Management,2020(3):58.
[9]
熊保颂. 基于便携式无人机SfM方法的活动构造地貌位错测量应用研究[D]. 武汉:中国地震局地震研究所,2020:2. XIONG Baosong. Offset measurement along active fault based on portable UAV and structure from motion[D]. Wuhan:Institute of Seismology China Earthquake Administration,2020:2.
[10]
王伟,董晓虎,付晶,等. 电力无人机气象环境适应性试验及防倾覆策略[J]. 湖北电力,2019,43(6):7. WANG Wei,DONG Xiaohu,FU Jin,et al. Meteorological environment adaptability test and overturning prevention strategies for UAV in power system[J]. Hubei Electric Power,2019,43(6):7.
[11]
DEMITRIT Y, VERLING S, STASTNY T, et al. Model-based wind estimation for a hovering VTOL tailsitter UAV[C]//IEEE International Conference on Robotics & Automation. IEEE, 2017.
[12]
全国水土保持规划编制工作领导小组办公室,水利部水利水电规划设计总院. 中国水土保持区划[M]. 北京:中国水利水电出版社,2016:95. Office of the Leading Group for the Preparation of National Water and Soil Conservation Planning by the General Institute of Water Resources, Hydropower Planning and Design of the Ministry of Water Resources. China water and soil conservation regionalization[M]. Beijing:China Water Conservancy and Hydropower Press,2016:95.
[13]
郑景云,卞娟娟,葛全胜,等. 1981-2010年中国气候区划[J]. 科学通报,2013,58(30):3088. ZHENG Jinyun,BIAN Juanjuan,GE Quansheng,et al. The climate regionalization in China for 1981-2010[J]. Chinese Science Bulletin,2013,58(30):3088.
[14]
YANG W,WANG Y,SUN S,ET al. Using Sentinel-2 time series to detect slope movement before the Jinsha River landslide[J]. Landslides,2019,16(7).
[15]
DEFRIES R S,TOWNSHEND J R G. NDVI-derived land cover classifications at a global scale[J]. International Journal of Remote Sensing,1994,15(17):3567.
[16]
ZHU Z,WOODCOCK C E. Continuous change detection and classification of land cover using all available Landsat data[J]. Remote Sensing of Environment:An Interdisciplinary Journal, 2014,144(17):152.
[17]
李苗苗. 植被覆盖度的遥感估算方法研究[D]. 北京:中国科学院研究生院(遥感应用研究所),2003:22. LI Miaomiao. The method of vegetation fraction estimation by remote sensing[D]. Beijing:Institude of Remote Sensing Applications Chinese Academy of Sciences,2003:22.
[18]
APPEL I,SALOMONSON V V. Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results[J]. IEEE Transactions on Geoscience and Remote Sensing,2006,44(7):1747.
[19]
SHARMA V,BENNIS M,KUMAR R. UAV-assisted heterogeneous networks for capacity enhancement[J]. IEEE communications letters,2016,20(6):1207.
[20]
毕海芸,郑文俊,曾江源,等. SfM摄影测量方法在活动构造定量研究中的应用[J]. 地震地质,2017,39(4):656. BI Haiyun,ZHENG Wenjun,ZENG Jiangyuan,et al. Application of SfM photogrammetry method to the quantitative study of active tectonics[J]. Seismology and Geology,2017,39(4):656.
[21]
周为峰. 基于遥感和GIS的区域土壤侵蚀调查研究[D]. 北京:中国科学院研究生院(遥感应用研究所), 2005:37. ZHOU Weifeng. Regional soil erosion assessment with remote sensing and geography information system[D]. Beijing:Institute of Remote Sensing Applications Chinese Academy of Sciences. 2005:37.
[22]
丁艳玲. 植被覆盖度遥感估算及其真实性检验研究[D]. 长春:中国科学院研究生院(东北地理与农业生态研究所),2015:45. DING Yanling. Research on the estimation of fractional vegetation cover and the validation of fractional vegetation cover product[D]. Changchun:Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,2015:45.
2021, Vol. 19 
