本数据为盈科绿洲农田、湿地、戈壁、沙漠与荒漠观测的一个生长周期内的植被覆盖度数据集。数据观测从2012年5月25日开始到9月14日结束，7月下旬之前每5天观测1次，之后10天观测1次。\n
\n测量仪器与原理：\n
\n采用数码相机拍照的方法测量了盈科绿洲的农田、湿地、戈壁、沙漠与荒漠的典型地物的植被覆盖度。样方的设计、照片拍摄方法和数据处理方法都经过一定的分析和考虑。\n
\n具体分几条进行描述：\n
\n0. 测量仪器：简易观测架搭配数码相机，将数码相机置于支撑杆前端的仪器平台，保持拍摄的竖直向下，远程控制相机测量数据。观测架可以用来改变相机的拍摄高度，面向不同类型植被实现有针对性的测量。\n
\n1. 样方设置和“真值”获取：玉米等低矮植被样方大小10×10米，果树样方30米×30米。每次测量时沿两条对角线依次拍照，共取9张照片（当地表覆盖非常均一时也有少于9张的情况），均匀分布在样方内。9张相片处理得到各自覆盖度之后取平均，最终得到一个样方的覆盖度“真值”。\n
\n2. 拍摄方法：针对低矮植被如玉米，直接采用观测架观测，保证观测架上的相机距离植被冠层的高度远大于植被冠幅，在方形样方内沿着对角线采样，然后做算术平均。在视场角度不大(<30°)的情况下，视场内包括大于2个整周期的垄行，相片的边长与垄行平行；针对较高植被如果树，在树冠下面从下向上拍摄照片，叠加配合对树冠下地表低矮植被从上向下的拍摄，得到植株附近的覆盖度，再拍摄植株之间非树冠投影区域的低矮植被，计算植株间隙的覆盖度。最后通过树冠投影法，获得树冠的平均面积。根据垄行距离计算植株树冠下与植株间隙的面积比例，加权获得整个样方的覆盖度。\n
\n3. 数据处理方法：采用一种自动分类方法，具体见“参考文献”第３条文献（Liu et al., 2012)。通过RGB颜色空间转换到更容易区分绿色植被的Lab空间，对绿度分量a的直方图进行聚类，分离出绿色植被和非绿色背景2组分，获得单张相片的植被覆盖度。该方法的优点在于其算法简单、易于实现而且自动化程度和精度较高。今后还需要更多的快速、自动、准确的分类方法，最大限度发挥数码相机方法的优势。\n
\n配套数据：\n
\n在记录表中文字记录了植被的种类、株高、垄宽、行宽、拍摄高度信息，同时附有数码相机拍摄的场景照片和田埂照片（农田）。\n
\n数据处理：\n
\n基于数字图像里面的分类方法，对植被和非植被像元分类后得到相片代表样方的植被覆盖度。
", "ds_source": "简易观测架搭配数码相机，将数码相机置于支撑杆前端的仪器平台，保持拍摄的竖直向下，远程控制相机测量数据。样方设置和“真值”获取：玉米等低矮植被样方大小10×10米，果树样方30米×30米。每次测量时沿两条对角线依次拍照，共取9张照片（当地表覆盖非常均一时也有少于9张的情况），均匀分布在样方内。9张相片处理得到各自覆盖度之后取平均，最终得到一个样方的覆盖度“真值”。
", "ds_process_way": "拍摄方法：针对低矮植被如玉米，直接采用观测架观测，保证观测架上的相机距离植被冠层的高度远大于植被冠幅，在方形样方内沿着对角线采样，然后做算术平均。在视场角度不大(<30°)的情况下，视场内包括大于2个整周期的垄行，相片的边长与垄行平行；针对较高植被如果树，在树冠下面从下向上拍摄照片，叠加配合对树冠下地表低矮植被从上向下的拍摄，得到植株附近的覆盖度，再拍摄植株之间非树冠投影区域的低矮植被，计算植株间隙的覆盖度。最后通过树冠投影法，获得树冠的平均面积。根据垄行距离计算植株树冠下与植株间隙的面积比例，加权获得整个样方的覆盖度。\n
\n数据处理方法：采用一种自动分类方法，具体见5“建议参考文献”第0条文献。通过RGB颜色空间转换到更容易区分绿色植被的Lab空间，对绿度分量a的直方图进行聚类，分离出绿色植被和非绿色背景2组分，获得单张相片的植被覆盖度。该方法的优点在于其算法简单、易于实现而且自动化程度和精度较高。今后还需要更多的快速、自动、准确的分类方法，最大限度发挥数码相机方法的优势。
", "ds_quality": "数据质量良好
", "ds_acq_start_time": "2012-05-25 00:00:00", "ds_acq_end_time": "2012-09-14 00:00:00", "ds_acq_place": "黑河流域,中游人工绿洲试验区,沙漠,戈壁,湿地,荒漠,农田", "ds_acq_lon_east": 100.37277777777777, "ds_acq_lat_south": 38.855000000000004, "ds_acq_lon_west": 100.37249999999999, "ds_acq_lat_north": 38.85527777777778, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 9555843687, "ds_files_count": 4562, "ds_format": "excel", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "ae99d340-1571-49c6-b467-c5051b768c17.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据由“黑河生态水文遥感试验（HiWATER）”产生，用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "c94b3578-20da-4346-9de9-c702b6ca8983", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596 ", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-09-01 09:30:06", "last_updated": "2025-06-30 16:35:38", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.1893", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing experiment: vegetation coverage data set in the middle reaches of Heihe River Basin (may 25-september 14, 2012)", "ds_format": "Excel", "ds_source": "The simple observation frame is equipped with a digital camera. The digital camera is placed on the instrument platform at the front end of the support rod to keep the shooting vertical and downward, and remotely control the camera to measure data. Quadrat setting and \"true value\" acquisition: quadrat size of low vegetation such as corn 10 × 10m, fruit tree quadrat 30m × 30 meters. Take photos along two diagonals in turn during each measurement, and take a total of 9 photos (less than 9 when the surface coverage is very uniform), which are evenly distributed in the quadrat. After 9 photos are processed to obtain their respective coverage, take the average, and finally get the \"true value\" of the coverage of a quadrat
", "ds_quality": "Good data quality
", "ds_ref_way": "", "ds_abstract": "This data is the vegetation coverage data set in a growth cycle of farmland, wetland, Gobi, desert and desert observation in Yingke oasis. Data observation starts from May 25, 2012 to September 14, 2012. It is observed once every 5 days before late July and once every 10 days thereafter.\nMeasuring instrument and principle:\nThe vegetation coverage of farmland, wetland, Gobi, desert and desert in Yingke oasis was measured by digital camera. The quadrat design, photo shooting method and data processing method have been analyzed and considered.\nIt is described in several articles:\n0 Measuring instrument: a simple observation frame is equipped with a digital camera. The digital camera is placed on the instrument platform at the front end of the support rod to keep the shooting vertical and downward, and remotely control the camera to measure data. The observation frame can be used to change the shooting height of the camera and realize targeted measurement for different types of vegetation.\none Quadrat setting and \"true value\" acquisition: quadrat size of low vegetation such as corn 10 × 10m, fruit tree quadrat 30m × 30 meters. Take photos along two diagonals in turn during each measurement, and take a total of 9 photos (less than 9 when the surface coverage is very uniform), which are evenly distributed in the quadrat. After 9 photos are processed to obtain their respective coverage, take the average, and finally get the \"true value\" of the coverage of a quadrat.\ntwo Shooting method: for low vegetation such as corn, directly use the observation frame to ensure that the height of the camera on the observation frame from the vegetation canopy is much greater than the vegetation canopy, sample along the diagonal in the square quadrat, and then make arithmetic average. When the field of view angle is small (< 30 °), the field of view includes more than 2 full cycle ridges, and the side length of the photo is parallel to the ridges; For higher vegetation trees, take photos from bottom to top under the canopy, overlay and cooperate with the shooting of low vegetation on the ground under the canopy from top to bottom to obtain the coverage near the plants, and then take photos of low vegetation in the non canopy projection area between plants to calculate the coverage of plant gap. Finally, the average area of tree crown is obtained by crown projection method. According to the ridge distance, the area ratio between the plant crown and the plant gap is calculated, and the coverage of the whole quadrat is weighted.\nthree Data processing method: an automatic classification method is adopted. For details, see article 3 of \"references\" (Liu et al., 2012). The RGB color space is converted to the lab space which is easier to distinguish the green vegetation, and the histogram of the green component A is clustered to separate the two components of green vegetation and non green background, so as to obtain the vegetation coverage of a single photo. The advantage of this method is that its algorithm is simple, easy to implement, and has high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to give full play to the advantages of digital camera methods.\nSupporting data:\nThe type, plant height, ridge width, row width and shooting height of vegetation are recorded in the record table, and the scene photos and ridge photos (farmland) taken by digital camera are attached.\nData processing:\nBased on the classification method in the digital image, the vegetation coverage of the photo representative quadrat is obtained after classifying the vegetation and non vegetation pixels.
", "ds_time_res": "日", "ds_acq_place": "Heihe River Basin, pilot area of artificial oasis in the middle reaches, desert, Gobi, wetland, desert, farmland", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Shooting method: for low vegetation such as corn, directly use the observation frame to ensure that the height of the camera on the observation frame from the vegetation canopy is much greater than the vegetation canopy, sample along the diagonal in the square quadrat, and then make arithmetic average. Small angle in field of view (In the case of< 30 °), the field of view includes more than 2 full cycle ridges, and the side length of the photo is parallel to the ridges; For higher vegetation trees, take photos from bottom to top under the canopy, overlay and cooperate with the shooting of low vegetation on the ground under the canopy from top to bottom to obtain the coverage near the plants, and then take photos of low vegetation in the non canopy projection area between plants to calculate the coverage of plant gap. Finally, the average area of tree crown is obtained by crown projection method. According to the ridge distance, the area ratio between the plant crown and the plant gap is calculated, and the coverage of the whole quadrat is weighted.\n
\nData processing method: an automatic classification method is adopted. See article 0 of 5 \"recommended references\" for details. The RGB color space is converted to the lab space which is easier to distinguish the green vegetation, and the histogram of the green component A is clustered to separate the two components of green vegetation and non green background, so as to obtain the vegetation coverage of a single photo. The advantage of this method is that its algorithm is simple, easy to implement, and has high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to give full play to the advantages of digital camera methods
", "ds_ref_instruction": "This data is generated by \"Heihe eco hydrological remote sensing experiment (hiwater)\". When using the data, users should clearly state the source of the data in the text and quote the reference method provided by this metadata in the reference part." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "植被", "机载地面遥感", "鱼眼相机", "植被盖度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "戈壁", "荒漠", "中游人工绿洲试验区", "沙漠", "湿地", "农田", "黑河流域" ], "ds_time_tags": [ 2012 ], "ds_contributors": [ { "true_name": "马明国", "email": "mmg@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "穆西晗", "email": "muxihan@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院遥感科学国家重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "穆西晗", "email": "muxihan@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院遥感科学国家重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "穆西晗", "email": "muxihan@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院遥感科学国家重点实验室", "country": "中国" }, { "true_name": "马明国", "email": "mmg@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "遥感及产品" }