dataFolder = fullfile(tempdir，“forestData”, filesep);dataFile = dataFolder +“forestData.laz”；检查文件夹和数据文件是否已经存在folderExists = exists (dataFolder，“dir”）;fileExists = exists (dataFile，“文件”）;创建一个新的文件夹，如果它不存在。如果~ folderExists mkdir (dataFolder);结束%如果航空数据文件不存在，则提取它如果~ fileExists解压(“forestData.zip”, dataFolder);结束从文件中读取LAZ数据lasReader = lasFileReader(dataFile);读取点云以及相应的扫描角度信息[ptCloud,pointAttributes] = readPointCloud(lasReader，“属性”，“ScanAngle”）;可视化输入点云图pcshow(ptCloud.Location)“输入点云”）

分割接地点，提取非接地点和接地点groundPtsIdx = segmentGroundSMRF(ptCloud);nonGroundPtCloud = select(ptCloud，~groundPtsIdx);groundPtCloud = select(ptCloud,groundPtsIdx);分别用洋红色和绿色可视化非接地点和接地点。图pcshowpair(nonGroundPtCloud,groundPtCloud)分割的非接地点和接地点）

groundPoints = groundPtCloud.Location;消除沿x轴和y轴的重复点[uniqueZ,uniqueXY] = groupsummary(groundPoints(:，3)，groundPoints(:，1:2)，@mean);uniqueXY = [uniqueXY{:}];创建插值，并使用它来估计每一点的地面高程F = scatteredInterpolant(double(uniqueXY)，double(uniqueZ)，“自然”）;estElevation = F(double(ptCloud.Location(:，1))，double(ptCloud.Location(:，2)));用地面归一化标高normalizedPoints = ptCloud.Location;normalizedPoints(:，3) = normalizedPoints(:，3) - estElevation;可视化归一化点图pcshow(normalizedPoints) title(“归一化高程点云”）

设置网格大小为每像素10米，cutOffHeight为2米gridSize = 10;cutOffHeight = 2;leafAngDistribution = 0.5;%提取森林指标[canopyCover,gapFraction,leafAreaIndex] = helperExtractForestMetrics(normalizedPoints，.．.pointAttributes.ScanAngle、gridSize cutOffHeight leafAngDistribution);可视化森林度量hForestMetrics = figure;axCC = subplot(2,2,1,Parent=hForestMetrics);axCC。位置= [0.05 0.51 0.4 0.4];显示亮度图像(canopyCover、家长= axCC)标题(axCC,“树冠覆盖”)轴从colormap(灰色)axGF = subplot(2,2,2,Parent=hForestMetrics);axGF。位置= [0.55 0.51 0.4 0.4];显示亮度图像(gapFraction“父”axGF)标题(axGF“差距分数”)轴从colormap(gray) axLAI = subplot(2,2，[3 4]，Parent=hForestMetrics);axLAI。位置= [0.3 0.02 0.4 0.4];显示亮度图像(leafAreaIndex、家长= axLAI)标题(axLAI,“叶面积指数”)轴从colormap(灰色)

设置网格大小为每像素0.5米gridRes = 0.5;%生成CHMcanopyModel = pc2dem(pointCloud(normalizedPoints)，gridRes，拐角fillmethod =“马克斯”）;将无效和负的CHM值剪辑为零华冠模型(isnan(华冠模型)|华冠模型<0)= 0;执行高斯平滑去除噪声效果H = fspecial(“高斯”, 5 [5], 1);华冠模型= imfilter(华冠模型，H，“复制”，“相同”）;可视化CHM图imagesc(canopyModel)“树冠高度模型”)轴从colormap(灰色)

将minTreeHeight设置为5米minTreeHeight = 5;%检测树顶[treeTopRowId,treeTopColId] = helperDetectTreeTops(canopyModel,gridRes,minTreeHeight);形象化树梢图imagesc(canopyModel)保持在情节(treeTopColId treeTopRowId,“处方”MarkerSize = 3)标题(“检测到树顶的CHM”)轴从colormap (“灰色”）

%分割单个树label2D = helperSegmentTrees(canopyModel,treeTopRowId,treeTopColId,minTreeHeight);标识label2D中每个点的行id和列id，并转移标签每点%rowId = ceil((ptCloud.Location(:，2) - ptCloud.YLimits(1))/gridRes) + 1;colId = ceil((ptCloud.Location(:，1) - ptCloud.XLimits(1))/gridRes) + 1;ind = sub2ind(size(label2D)，rowId,colId);标签3d =标签2d (ind);提取有效的标签和对应的点validSegIds = label3D ~= 0;ptVeg = select(ptCloud,validSegIds);veglabel3D = label3D(validSegIds);为每个标签指定颜色numColors = max(veglabel3D);colorMap = randi([0 255]，numColors,3)/255;labelColors = label2rgb(veglabel3D,colorMap,OutputFormat=“三胞胎”）;%可视化树段图pcshow(ptvegf . location,labelColors)“单个树段”)视图(2)

%提取树属性treeMetrics = helperExtractTreeMetrics(normalizedPoints,label3D);%显示前5个树段指标disp(头(treeMetrics, 5));