基于多源数据及三层模型的小班林型识别

doi:10.12302/j.issn.1000-2006.202109037

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南京林业大学学报（自然科学版） ›› 2022, Vol. 46 ›› Issue (1): 69-80.doi: 10.12302/j.issn.1000-2006.202109037

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基于多源数据及三层模型的小班林型识别

黄健(), 吴达胜, 方陆明

浙江农林大学数学与计算机科学学院,林业感知技术与智能装备国家林业和草原局重点实验室,浙江杭州 311300

收稿日期:2021-09-19 接受日期:2021-11-02 出版日期:2022-01-30 发布日期:2022-02-09
基金资助:
浙江省科技重点研发计划资助项目(2018C02013)

Identification of sub-compartment forest type based on multi-source data and three-tier models

HUANG Jian(), WU Dasheng, FANG Luming

College of Mathematics and Computer Science,Zhejiang Agriculture and Forestry University, Key Laboratory of National Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment,Hangzhou 311300, China

Received:2021-09-19 Accepted:2021-11-02 Online:2022-01-30 Published:2022-02-09

摘要/Abstract

摘要：

【目的】目前关于林型识别的研究区域主要为小范围森林区域和林场,为了探究较大范围内林型的识别方法,本研究使用Sentinel-2光学遥感影像、森林资源二类调查数据、数字高程模型(DEM)和Sentinel-1雷达遥感影像数据建立林型识别模型。【方法】以淳安县作为研究区域,针对淳安县各个小班中的7种林型:毛竹(Phyllosstachys edulis)林、茶树(Camellia sinensis)林、山核桃(Carya cathayensis)林、杉木(Cunninghamia lanceolata)林、马尾松(Pinus massoniana)林、阔叶混交林、其他硬阔林进行识别。研究采用分层的方法对林型进行识别,整体分为3层。第1层使用RF算法建立林地与非林地识别模型;第2层对林地数据进行树种结构识别,分别使用随机森林(random forest, RF)、极端梯度提升(eXtreme gradient boosting, XGBoost)和轻量级梯度提升机(light gradient boosting machine, LightGBM)方法建立不同模型并对比分析实验结果;第3层将树种结构细分为林型。【结果】第1层RF林地与非林地识别模型总体精度为98.08%;第2层树种结构识别模型中对比了3个模型不同特征组合下的性能,其中LightGBM模型总体精度最高,达到81.43%;第3层模型对林型进行识别,基于所有特征结合雷达遥感因子建模的情况下,LightGBM模型精度为84.51%,经递归特征消除法(recursive feature elimination, RFE)选择特征后,最优精度为83.21%。【结论】通过各个模型的特征重要性图发现,光学遥感中的绿光、红光、近红外波段和红边波段,以及DEM提取的地形因子对研究区域小班林型识别有较好的效果,而Sentinel-1雷达中提取的自变量对林型的识别没有特别明显的帮助。

关键词: 林型识别, 光学遥感, 雷达遥感, 数字高程模型, 模型分层

Abstract:

【Objective】 Currently, forest type identification research is mainly focused on small forest areas and forest farms. To explore the forest type identification method across a large range, this study used Sentinel-2 optical remote sensing imagery, forest resource survey data, a digital elevation model (DEM), and Sentinel-1 radar remote sensing image data to establish tree species identification model.【Method】 The research area was defined as Chun’an County, where seven forest types were modeled separately, including Phyllosstachys edulis forest, Camellia sinensis forest, Carya cathayensis forest, Cunninghamia lanceolata forest, Pinus massoniana forest, broad-leaved mixed forest, and other hard broad-leaved species. Models were divided into three layers. In the first layer, the RF algorithm was used to establish the identification model of forested land and non-forested land. In the second layer, forest structure was identified from forested land. The RF, XGBoost and LightGBM methods were used to build various models and analyze the experimental results. In the third layer, the forest structures were further divided into forest type.【Result】 The overall accuracy of the first-layer model based on the RF algorithm dividing samples into forested and non-forested land samples was 98.08%. In the second layer (i.e., forest structure recognition model), the performance of the three models under various feature combinations were compared. It was found that the LightGBM model had the highest overall accuracy of 81.43%. In the third layer, the performance indicators for seven forest type models were compared; based on the combination of all features and radar factors, the overall accuracy of the LightGBM model was 84.51%, after feature selection by the recursive feature elimination algorithm, the optimal accuracy was 83.21%.【Conclusion】 The green, red, near-infrared and red-edge bands from optical remote sensing imagery, and terrain factors from a DEM are effective in identifying the forest type. However, independent variables extracted from Sentinel-1 radar do not provide significant help to identify forest type.

Key words: forest type identification, optical remote sensing, radar remote sensing, digital elevation model, multi-layer models

中图分类号:

S771.8

黄健,吴达胜,方陆明. 基于多源数据及三层模型的小班林型识别[J]. 南京林业大学学报（自然科学版）, 2022, 46(1): 69-80.

HUANG Jian, WU Dasheng, FANG Luming. Identification of sub-compartment forest type based on multi-source data and three-tier models[J].Journal of Nanjing Forestry University (Natural Science Edition), 2022, 46(1): 69-80.DOI: 10.12302/j.issn.1000-2006.202109037.

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树种结构 forest structure	样本信息数量 sample size	林型 forest type	样本信息数量 sample size
阔叶林 (含阔叶混交林) broad-leaved forest (including broad- leaved mixed forest)	16 414	山核桃林 Carya cathayensis forest	6 602
		阔叶混交林 broad-leaved mixed forest	4 644
		其他硬阔林 other hard broad- leaved forest	5 168
针叶林 coniferous forest	15 550	马尾松林 Pinus massoniana forest	5 965
针叶林 coniferous forest	15 550	杉木林 Cunninghamia lancelata forest	9 585
经济林 no-woods forest	18 601	毛竹林 Phyllosstachys edulis forest	9 830
经济林 no-woods forest	18 601	茶树林 Camellia sinensis forest	8 771

自变量因子类别 category of independent variable factors	自变量因子名称 name of independent variable factors	量符号或公式 variable or the formula	中文名称 Chinese name
遥感影像波段信息 band information of remote sensing image	B2	B₂	遥感影像像元值
	B3	B₃
	B4	B₄
	B5	B₅
	B6	B₆
	B7	B₇
	B8	B₈
	B8A	B₈_A
	B11	B₁₁
	B12	B₁₂
植被指数 vegetation index	RVI	B₅/B₄	比值植被指数
	EVI	2.5×(B₈-B₄)/(B₈+ 6×B₄-7.5×B₂+1)	增强型植被指数
	DVI	B₈/B₄	差值环境植被指数
	NDVI	(B₈-B₄)/(B₈+B₄)	归一化植被指数
光学波段组合因子 optical band combination factor	B8A_B4	(B_8A-B₄)/(B_8A+B₄)
	B8A_B7	(B_8A-B₇)/(B_8A+B₇)
	B7_B6	(B₇-B₆)/(B₇+B₆)
	B8A_B5	(B_8A-B₅)/(B_8A+B₅)
	B6_B5	(B₆-B₅)/(B₆+B₅)
	B8A_B6	(B_8A-B₆)/(B_8A+B₆)

过采样算法 oversampling algorithm	用户精度/% UA		总体精度/% OA
过采样算法 oversampling algorithm	林地 forest	非林地 non-forest	总体精度/% OA
SMOTE	99.06	93.10	98.70
Borderline-SMOTE	98.28	92.94	97.96
SVM-SMOTE	98.35	92.62	98.00
ADASYN	98.48	94.95	98.27

模型 model	方案 plan	用户精度/% UA			总体精度/% OA	模型训练时间/s model training time
模型 model	方案 plan	阔叶林 (含阔叶混交林) broad-leaved forest (including broad- leaved mixed forest)	针叶林 coniferous forest	经济林 no-woods forest	总体精度/% OA	模型训练时间/s model training time
RF	1	66.34	78.11	80.40	75.13	16.16
	2	66.84	79.16	80.70	75.73	15.15
	3	67.86	79.38	81.21	76.31	18.90
XGBoost	1	70.94	79.74	83.84	78.44	49.45
	2	71.75	80.22	84.02	78.92	52.89
	3	72.19	81.06	85.04	79.70	59.76
LightGBM	1	72.42	80.03	84.07	79.05	1.29
	2	72.80	80.58	85.27	79.79	1.35
	3	73.96	81.71	85.93	80.76	1.45

窗口大小 sliding window size	用户精度/% UA			总体精度/% OA
窗口大小 sliding window size	阔叶林 (含阔叶混交林) broad-leaved mixed forest	针叶林 coniferous forest	经济林 no-woods forest	总体精度/% OA
3×3	73.69	81.25	87.15	80.99
7×7	73.79	82.52	87.22	81.43
11×11	74.04	81.82	86.87	81.17
15×15	73.79	81.71	86.95	81.09
19×19	73.81	81.73	86.57	80.95
23×23	73.90	81.45	86.71	80.95
27×27	73.77	81.21	86.51	80.76
31×31	73.57	80.99	86.19	80.51
35×35	74.00	80.95	86.05	80.59
39×39	74.10	80.80	86.35	80.68
43×43	73.98	81.08	86.32	80.71
47×47	73.94	81.06	86.23	80.66
51×51	74.06	80.64	86.32	80.61

基于多源数据及三层模型的小班林型识别

Identification of sub-compartment forest type based on multi-source data and three-tier models

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林型 forest type	雷达遥感因子及特征选择方案 radar remote sensing factor and feature selection scheme
	方案A plan A		方案B plan B		方案C plan C
	用户精度/% UA	生产者精度/% PA	用户精度/% UA	生产者精度/% PA	用户精度/% UA	生产者精度/% PA
山核桃林 Carya cathayensis forest	91.87	92.93	92.32	93.24	92.32	93.05
阔叶混交林 broad-leaved mixed forest	63.36	59.96	62.93	59.79	63.22	58.97
其他硬阔林 other hard broad-leaved forest	60.43	63.00	59.97	63.34	57.97	62.27
杉木林 Cunninghamia lanceolata forest	85.93	81.79	86.28	81.82	86.42	81.47
毛竹林 Phyllosstachys edulis forest	94.86	94.32	94.46	94.19	95.03	93.83
茶树林 Camellia sinensis forest	92.27	92.27	92.76	93.03	92.15	93.31
马尾松林 Pinus massoniana forest	69.77	75.51	69.94	75.79	69.14	75.86
总体精度/% OA	83.08		83.21		82.91
Kappa系数	0.80		0.80		0.80

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[2]	徐茂松,李坤,谢酬,朱松,罗洪章,张风丽,王雪军,夏忠胜,党永峰. 极化雷达与光学遥感森林雪灾破坏协同监测[J]. 南京林业大学学报（自然科学版）, 2014, 57(04): 1-6.
[3]	胡刚,宋慧,张明礼. 栅格DEM中隔断线约束算法应用评价[J]. 南京林业大学学报（自然科学版）, 2014, 57(01): 140-144.