Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index
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摘要:目的利用可见光植被指数快速准确获取矿山排土场坡面植被盖度，为乌海矿区排土场坡面植被调查提供有效方法。方法选取乌海市典型矿山排土场，通过样方调查法、无人机遥感及可见光植被指数计算筛选适于研究区排土场坡面植被提取的可见光植被指数，并估算其植被盖度，试为排土场坡面植被盖度提取提供新方法。结果结果表明：（1）不同可见光植被指数提取植被效果存在一定差异，其中绿红比值指数（RGRI）和绿蓝比值指数（BGRI）的灰度图中越暗的部分代表植被指数越大，而其他常见可见光植被指数是越亮的部分代表植被指数越大。（2）研究区中不同可见光植被指数灰度图像特征值基本分布在[− 1，1]范围内，由蓝、绿波段构建的归一化绿蓝差异指数（NGBDI）和绿蓝比值指数（BGRI）的灰度图中植被与裸地像元值范围有较大重叠，即存在部分混淆。（3）常见可见光植被指数中，可见光波段差异植被指数（VDVI）可以快速准确提取研究区排土场坡面植被，通过人工目视解译及误差矩阵得到VDVI植被指数提取结果平均识别精度在93.4%，表明VDVI植被指数更加适用于乌海市矿山排土场坡面植被提取，优于其他常见可见光植被指数，利用该方法估算可得研究区坡面植被盖度约20.4%。结论可见光植被指数作为一种非监督分类方法，无需人工选择参考地物即可提取植被，可以作为矿山排土场坡面植被盖度调查的一种新方法，具有广阔的应用前景，同时研究表明VDVI植被指数在提取乌海市矿山排土场坡面植被盖度时具有较高提取精度，对指导当地矿山排土场植被恢复具有实际意义。Abstract:ObjectiveThis paper aims to use visible vegetation index to get the slope vegetation coverage of mine dump quickly and accurately, and to provide an effective method for the vegetation investigation of slope in Wuhai mining area, Inner Mongolia of northern China.MethodThis study selected one of the typical mine dumps in the Wuhai City to choose a suitable visible vegetation index for local research, estimate the dump vegetation coverage, and furthermore try to provide a new method for extracting the dump slope vegetation coverage by means of Quadrat survey, UAV remote sensing and visible vegetation index.Result(1) Different visible vegetation indexes had varied vegetation extraction effects. Among them, the darker part of red-green ratio index (RGRI) and blue-green ratio index (BGRI) represented the larger vegetation index, while the brighter part of other common visible vegetation indexes represented the larger vegetation index. (2) Gray image feature values of different visible vegetation indexes mainly distributed in range of [− 1, 1] in the research area. There was a big overlap of pixel value between vegetation and land in the gray image of normalized green-blue difference index (NGBDI) and BGRI constructed from blue and green bands, which means some partial confusion between them. (3) Visible-band difference vegetation index (VDVI) can extract slope vegetation quickly and accurately among common visible vegetation indices. The average recognition accuracy of VDVI vegetation index was 93.4% by manual visual interpretation and error matrix, showing that VDVI vegetation index could be more suitable for the vegetation extraction on the mine dump in Wuhai City, and was better than the vegetation index of other common visible light. The vegetation coverage of the study area was approximately 20.4% by this method.ConclusionVisible vegetation index, an unsupervised classified method, could be a new method for investigating vegetation coverage of mine dump slope without selecting reference features by people but extract slope vegetation coverage directly and has a broad application prospect. The study also indicates that VDVI shows higher extraction accuracy of extracting vegetation coverage of mine dump slope in the Wuhai City, which means a practical significance to guide the vegetation restoration in local mine dump.
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Keywords:
- unmanned aerial vehicles /
- visible-bands /
- vegetation index /
- dump /
- Wuhai City
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植物寄生线虫对农林业发展构成严重的威胁[1-2]，为了防治线虫病害，并避免传统化学防治带来的环境污染[3]，开发利用杀线虫生防制剂已成为当今的研究热点[4-5]。真菌次生代谢产物具有药效高、环境相容性好、不容易造成二次污染等优点已被越来越多的人所关注[6-7]。丝状真菌Sr18(Syncephalastrum racemosum)(简称Sr18真菌)，是一株以松材线虫(Bursaphelenchus xylophilus)为靶标，自主分离获得的具有广谱杀线虫活性的丝状真菌，其代谢物对大豆孢囊线虫、甘薯茎线虫、南方根结线虫等都具有良好的防治作用，用1/2浓度的Sr18发酵液处理松材线虫、南方根结线虫、大豆孢囊线虫等不同线虫24 h，校正死亡率均能达到100%[8-9]。前期研究通过实验已证实发酵液中杀线虫活性组分主要为小分子物质[10]，其小分子活性组分具有水溶性好且耐热的优势。进一步的机理研究表明：小分子活性组分对线虫体内的超氧化物岐化酶(Superoxide dismutase, SOD)、过氧化物酶(Peroxidase, POD)、过氧化氢酶(Catalase, CAT)等抗氧化保护酶系，神经递质相关的总胆碱酯酶(Total Cholinesterase, TChE)，以及解毒酶谷胱甘肽转硫酶(Glutathione S-transferases, GST)均有明显的抑制作用[11]。同时还发现小分子活性组分对线虫卵的孵化也有很强的抑制作用，原始浓度发酵液处理南方根结线虫的虫卵48 h，孵化抑制率能达到85%以上[9]。在此基础上，实验借助场发射扫描电镜和高分辨透射电镜的手段，对1/2浓度Sr18发酵液小分子活性组分处理后的松材线虫进行了体表和内部结构观察[12-13]，旨在为进一步揭示Sr18发酵液小分子活性组分毒杀线虫的机制提供依据。
1. 材料与方法
1.1 实验材料
1.1.1 供试线虫
松材线虫，中国农业科学院植物保护研究所提供。
1.1.2 菌种
Sr18真菌，由本研究室筛选及保藏。
灰葡萄孢霉(Batrytis cinerea)(简称BC菌)，由天津师范大学提供。
1.2 实验方法
1.2.1 发酵液的小分子活性组分的制备
在工厂用5t发酵罐采取优化放大工艺[14](发酵条件：罐压0.1 mPa；排气1；温度27 ℃；转速120 r/min，变频器324.7；发酵时间40 h)制得Sr18发酵液[10]。
取原浓度的Sr18发酵液，加入等量无菌水稀释成1/2浓度的Sr18发酵液，经微孔滤膜(0.22 μm)过滤除菌后，采用截流分子量为6 000 da的UEOS-503型中空纤维膜组件对发酵液进行超滤分级[15]，除去大分子物质后所得到的小分子收集物即为实验所用的1/2浓度的Sr18发酵液小分子活性组分。
1.2.2 松材线虫培养与收集
在无菌操作条件下，将灰葡萄孢霉接种于察氏培养基上，21 ℃下避光培养7 d[16]，待菌丝长满培养皿后，将松材线虫接种于培养皿进行培养繁殖，26 ℃下避光培养5~7 d。
培养好的线虫采用贝尔曼(Baermann)漏斗法[17]进行收集，用无菌水离心洗涤3次(2 000 r/min，3 min)，制备成约15 000条/mL的悬液，供试。
1.2.3 松材线虫的处理
参照日本Kawazu等[18]处理线虫的实验方法(即1 mL杀线虫药剂+1 500头松材线虫)，取24孔板，每孔加200 μL线虫悬液，实验组加入2 mL 1/2浓度的Sr18发酵液小分子活性组分，对照组则加入等量的无菌水，于26 ℃培养箱中处理1~3 d，每24 h取样观察1次。
1.2.4 扫描电镜(SEM)制样
将处理的线虫按照预定时间(24、48、72 h)取样，离心后用无菌水洗4次，加入用磷酸缓冲液配制的2.5%戊二醛，4 ℃过夜后，再用0.1 M PBS漂洗3次，每次15 min；然后用磷酸缓冲液配制的1%锇酸室温下固定2 h，PBS漂洗3次，每次15 min；分别用30%、50%、70%、80%、90%、100%I、100%Ⅱ梯度乙醇溶液脱水，每次10 min；六甲基二硅胺烷法干燥；离子溅射镀膜仪镀膜[19]；场发射扫描电镜(型号：Nova NanoSEM 230)观察，拍照记录，每24 h取样观察1次。
1.2.5 透射电镜(TEM)制样
按照电镜样品制备的常规方法[20]，用PBS反复清洗包含有线虫的小琼脂块，2.5%戊二醛和1%锇酸双重固定(37 ℃，12 h；45 ℃，12 h；60 ℃，24 h)，分别用30%、50%、70%、80%、90%、100%I、100%Ⅱ梯度乙醇溶液脱水，每次10 min；环氧树脂梯度浸透，包埋，LEICA EMUC6超薄切片机进行超薄切片(70~80 nm)，铜网收集，醋酸双氧铀-柠檬酸铅双重染色[21]，Hitachi H-600透射电子显微镜观察并拍照，每24 h取样观察1次。
2. 结果与分析
2.1 松材线虫虫体及体壁的SEM观察
正常线虫及小分子活性组分处理后的线虫SEM观察结果如图 1所示。观察发现，对照组线虫虫体丰满，呈自然弯曲状态(图 1A)，线虫头部与虫体分界明显，体壁环纹清晰，尾部表面光滑，雄虫可见交合刺外凸。
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图 1 线虫虫体低倍观察结果A.对照(Bar=100 μm)；B.处理3天后的线虫(Bar=200 μm)。Figure 1. Ultrastructure observation of Bursaphelenchus xylophilus at low magnificationA, normal Bursaphelenchus xylophilus(Bar = 100 μm); B, 3 days treated Bursaphelenchus xylophilus by Sr18(Bar = 200 μm).与对照组相比，小分子活性组分处理后的线虫虫体皱缩并扭曲(图 1B)，体壁环纹模糊，表面凹凸不平，附着的细菌消失，表皮呈片状脱落，体壁出现明显的局部凹陷，放大后可见体表内陷形成的孔洞(图 2A、2B)，并可见内容物从体壁破损的孔洞溢出(图 2C)。
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图 2 处理后线虫体表SEM观察结果A.处理1天后线虫体表出现孔洞(Bar=4 μm)；B.放大后的孔洞(Bar=1 μm)；C.处理3天后内溶物由孔洞溢出(←)(Bar=1 μm)。Figure 2. Ultrastructure observation of the Bursaphelenchus xylophilu's body surface at high magnificationA, holes on body surface of nematode treated 1 day by Sr18(Bar=4 μm); B, magnifying hole on body surface(Bar=1 μm); C, dissolved substance inside the cell flew out from the holes after 3 days treated(Bar=1 μm).2.2 松材线虫头部表观结构的SEM观察
由图 3A和图 3B可以看出，正常线虫的头部与虫体界限分明, 连接处环形平台清晰，头部六片唇瓣之间有凹陷间隔(图 3B)，并可观察到唇片上存在乳状突起，唇片上角质环纹明晰, 6个唇片在口周形成角质口环, 环中可见口针外凸。此外，还发现线虫体表局部有携带细菌[22]的存在(图 3A)。与之相对应，小分子组分处理过的线虫头部和虫体皱缩严重，连接处环形平台缢缩；唇瓣之间有凹陷间隔消失，难以分辨出6片唇，角质口环和横纹消失、口针完全被破坏(图 3C)。
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图 3 线虫头部SEM观察结果A.对照线虫头部及体壁的细菌(Bar=20 μm)；B.对照线虫头部(Bar=3 μm)；C.处理3天后萎缩的线虫头部(Bar=1 μm)。Figure 3. Ultrastructure observation of the Bursaphelenchus xylophilu's head at high magnificationA, head and bacteria on body wall of normal nematode(Bar=20 μm); B, head of normal nematode(Bar=3 μm); C, shrinking head of nematode treated 3 days by Sr18(Bar=1 μm).2.3 松材线虫尾部的SEM观察
松材线虫的尾部为生殖孔所在部位，其结构的完好与否与繁殖密切相关。观察发现，正常线虫尾部因雌雄而异，雄虫侧面观察呈尖形(图 1A)，可以观察到交合刺，尾尖片状翼膜清晰(图 4A)；小分子活性组分处理后，尾部与虫体变化相似，表面凹凸不平，严重皱缩，交合刺消失，但尾尖片状翼膜变化不大(图 4B、4C)。
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图 4 线虫尾部SEM观察结果A.对照线虫尾部及交合刺(Bar=20 μm)；B.处理2天后线虫尾部缢缩，肿胀物形成(Bar=20 μm)；C.肿胀物(Bar=5 μm)。Figure 4. Ultrastructure observation of the Bursaphelenchus xylophilu's tail at high magnificationA, tail and spicule of normal nematode(Bar=20 μm); B, shrinking tail and swellings of nematode treated 2 days by Sr18(Bar=20 μm); C, magnifying swelling on body surface(Bar=5 μm).2.4 松材线虫内部结构的TEM超微观察
实验对小分子活性组分处理后引起的的松材线虫内部结构变化进行了TEM超微观察。观察发现，对照组线虫表皮、体壁等结构完整，其3层结构(角质层、下皮层、肌肉层)完整；体腔内的细胞界限清晰，细胞核核膜完整，核糖体丰富，分布均匀；线粒体形态完好、且多聚集于肌纤维[20]附近，内质网等内膜结构清楚(图 5A、5B)。
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图 5 线虫内部结构透射电镜(TEM)观察结果A.对照线虫肌纤维(M)及线粒体(MI)(Bar=500 nm)；B.正常的细胞核(n)(Bar=500 nm)；C.处理1天后的细胞核(n)及内质网(ER)(Bar=500 nm)；D.处理2天后的体壁(↘)及细胞损伤(Bar=500 nm)；E.处理3天后体壁分离(Bar=2 μm)；F.处理3天后体壁分离后组织细胞结构消失(Bar=2 μm)。Figure 5. Ultrastructure observation of the Bursaphelenchus xylophilu's internal structure at high magnificationA, muscle fiber and mitochondria of normal nematode(Bar=500 nm); B, cell nucleus of normal nematode(Bar=500 nm); C, damaged cell nucleus and endoplasmic reticulum of nematode treated 1 day by Sr18(Bar=500 nm); D, swellings on body wall and cellular damage of nematode treated 2 days by Sr18(Bar=500 nm); E, separation of cuticle and coelom of nematode treated 3 days by Sr18 (Bar=2 μm); F, disappearance of tissue and cell structure after separation of nematode treated 3 days by Sr18 (Bar=2 μm).经小分子活性组分处理后，体腔内部分细胞受损明显，细胞核形状不规则，核膜受损，染色质分散，内质网出现断裂，核糖体集聚，线粒体明显固缩(图 5C、5D)，局部体壁和体腔分离，肌纤维严重受损，已看不清体壁层次，并形成空腔，体腔内细胞间界限勉强可见；最终体内细胞界限消失，失去细胞功能(图 5E、5F)。
3. 讨论与结论
扫描电镜及透射电镜的超微结构观察结果显示，Sr18发酵液分离得到的小分子活性组分对松材线虫的杀线作用是“内外兼顾”的，全面影响线虫的结构。就超微观察结果而言，观察到体壁受损，层次消失，肌细胞溶解，线粒体、内质网等细胞器受损。核糖体聚集通常也是细胞损伤的重要指征，从而导致线虫死亡，表明其作用机理应为毒杀，而非触杀[23]。前期酶学研究结果也显示，Sr18发酵液的小分子活性组分对ATP、TChE、GST、以及SOD、POD、CAT等酶均有抑制作用[10]。这与超微观察的结果相对应。失去了这些酶的保护，细胞器和细胞的损伤在所难免，而细胞器或细胞的损伤也必然加剧对酶的影响[24]。
目前，对真菌代谢物对线虫抑制或致死作用相关研究主要集中在这些代谢产物的实验室或大田的时间-剂量效应研究上以及代谢物对J2的致死性，对虫卵孵化的抑制力以及抑制线虫侵染寄主的能力[25]；Vos等[26]研究发现丛枝菌根真菌可通过改变其寄主根系分泌物来减少根结线虫的侵染，而且其根系分泌物可在抑制线虫运动方面发挥一定的作用。另有一些则对线虫寄生真菌和食线虫真菌侵染性胞外酶的蛋白分子结构、酶动力学以及其作用于线虫的组织部位进行了相关的分析与研究[27]；现在许多微生物的杀线虫代谢产物的真正的作用机制还不清楚，代谢产物可通过不同的作用靶点破坏线虫的正常生活，已有的对微生物杀线代谢产物作用机制的研究多从代谢物对线虫的神经毒作用、体壁破坏作用、与物质代谢及能量代谢相关的酶的变化以及分泌植物生长激素等方面进行[28-33]，较深入系统的研究还很缺乏。由于这方面的理论问题没有真正搞清楚，因此大大限制了高效生物杀线虫农药的开发应用，这直接影响对它们更好的利用[29]。
近年来随着系统生物学中代谢组学技术的迅猛发展为在更深层次上揭示活性代谢物的杀线虫机理提供了强有力的手段和工具[34]。未来课题组将在前期研究的基础上，以对农林作物造成严重危害的植物寄生线虫为作用对象，将高分辨电镜、酶学及生理生化等研究手段与系统生物学中新兴的代谢组学的新技术方法相结合，充分利用学科交叉的优势，通过体内、外实验系统深入地研究探讨Sr18生物杀线剂防治植物根结线虫病害的作用机理，寻找生物标记物，确定其作用方式，为今后建立植物寄生线虫代谢组学研究平台及开发对黄瓜、番茄、辣椒等根结线虫病害具有防效高、增产作用明显[29]等特点的线虫生防菌株Sr18新型高效生物杀线剂做有益的探索。
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图 3 目视解译与监督分类结果
A-1、B-1、C-1分别为典型样方A、B、C的航拍原始图像；A-2、B-2、C-2分别为典型样方A、B、C的目视解译结果；A-3、B-3、C-3分别为典型样方A、B、C的最大似然监督分类结果。A-1, B-1, C-1 represent original images of quadrat A, B and C, respectively; A-2, B-2, C-2 represent visual interpretation results of quadrat A, B and C, respectively; A-3, B-3, C-3 represent maximum likelihood supervised classification calculating results of quadrat A, B and C, respectively.
Figure 3. Results of visual interpretation and supervised classification
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图 5 Otsu法和双峰直方图法分割结果对比图
Figure 5. Comparison of different indices by Otsu and histogram threshold methods
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图 7 排土场坡面植被分类结果图
Figure 7. Classification results of slope in mine junkyard of the study area
表 1 常见的可见光植被指数
Table 1 Common vegetation indices of visible bands
可见光植被指数
Visible vegetation index全称 Full name 计算公式 Equation 参考文献Reference NGRDI 归一化绿红差异指数
Normalized green-red difference index（G − R）/（G + R） [20] NGBDI 归一化绿蓝差异指数
Normalized green-blue difference index（G − B）/（G + B） [21] EXG 超绿指数
Excess green index2g − r − b [22] EXGR 超绿超红差异指数
Excess green minus excess red indexEXG − 1.4r − g [23] VEG 植被指数
Vegetation indexg/r0.67b0.33 [24] VDVI 可见光波段差异植被指数
Visible-band difference vegetation index（2G − R − B）/（2G + R + B） [11] RGRI 绿红比值指数
Red-green ratio indexR/G [25] BGRI 绿蓝比值指数
Blue-green ratio indexB/G [26] 注：G. 绿光通道；R. 红光通道；B. 蓝光通道；g. 绿光通道标准化结果；r. 红光标准化结果；b. 蓝光标准化结果；Notes: G, green channel; R, red channel; B, blue channel; g, standardization of green channel; r, standardization of red channel; b, standardization of blue channel; g = G/(G + R + B)，r = R/(G + R + B)，b = B/(G + R + B). 
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表 2 地物在红、绿、蓝波段及各可见光植被指数波段的像元值差异表
Table 2 Differences in pixel values in red, green, blue bands and vegetation indices of land cover
波段类型 Band type 植被 Vegetation 裸地 Bare land 均值 Mean 标准差 Standard deviation 均值 Mean 标准差 Standard deviation 红光波段像元值 Pixel value for red band 164.470 26.898 212.602 21.997 绿光波段像元值 Pixel value for green band 177.432 27.016 204.327 19.403 蓝光波段像元值 Pixel value for blue band 159.941 30.192 205.148 17.529 BGRI像元值 Pixel value for BGRI 0.900 0.083 1.006 0.042 EXG像元值 Pixel value for EXG 0.063 0.036 −0.015 0.006 EXGR像元值 Pixel value for EXGR −0.750 0.020 −0.821 0.013 NGBDI像元值 Pixel value for NGBDI 0.055 0.046 −0.003 0.020 NGRDI像元值 Pixel value for NGRDI 0.039 0.016 −0.019 0.015 RGRI像元值 Pixels value for RGRI 0.925 0.029 1.039 0.030 VDVI像元值 Pixel value for VDVI 0.046 0.026 −0.011 0.005 VEG像元值 Pixel value for VEG 1.093 0.045 0.973 0.009
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表 3 4种可见光植被指数分类结果精度评估
Table 3 Accuracy evaluation of four kinds of vegetation indices of visible bands
% 项目 Item EXG EXGR NGRDI VDVI 监督分类
Supervised
classificationOtsu法
Otsu method双峰直方图法
Histogram methodOtsu法
Otsu method双峰直方图法
Histogram methodOtsu法
Otsu method双峰直方图法
Histogram methodOtsu法
Otsu method双峰直方图法
Histogram method样方A Quadrat A 93.53 95.41 91.80 93.55 88.35 90.14 91.12 95.86 98.93 样方B Quadrat B 61.61 75.89 62.82 83.57 64.31 90.33 61.40 90.98 96.39 样方C Quadrat C 91.27 92.86 85.82 85.87 78.81 81.30 92.22 92.89 96.80 均值 Mean 82.14 88.05 80.15 87.66 77.16 87.26 81.58 93.24 97.37
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表 4 样方D精度评估表
Table 4 Accuracy evaluation of quadrat D
分类数据
Classification data双峰直方图法 Histogram method Otsu法 Otsu method 植被Vegetation 非植被
Non-vegetation行总和
Row total用户精度
User accuracy/%植被
Vegetation非植被
Non-vegetation行总和
Row total用户精度
User accuracy/%植被 Vegetation 516 240 25 318 541 558 95.32 385 202 11 971 397 173 96.99 非植被 Non-vegetation 84 415 747 313 831 728 89.85 215 453 760 660 976 113 77.93 列总和 Column total 600 655 772 631 1 373 286 600 655 772 631 1 373 286 生产者精度
Producer accuracy/%85.95 96.72 64.13 98.45
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