Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (10): 1129-1150.DOI: 10.17521/cjpe.2022.0077
Special Issue: 生态学研究的方法和技术; 生态遥感及应用
• Reviews • Previous Articles Next Articles
TIAN Jia-Yu1,2, WANG Bin1,2, ZHANG Zhi-Ming1,*(), LIN Lu-Xiang2,3,*()
Received:
2022-03-03
Accepted:
2022-10-11
Online:
2022-10-20
Published:
2022-10-21
Contact:
*(LIN Lu-Xiang, linluxa@xtbg.ac.cn; ZHANG Zhi-Ming, zzming76@ynu.edu.cn)
Supported by:
TIAN Jia-Yu, WANG Bin, ZHANG Zhi-Ming, LIN Lu-Xiang. Application of spectral diversity in plant diversity monitoring and assessment[J]. Chin J Plant Ecol, 2022, 46(10): 1129-1150.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0077
传感器(遥感平台) Sensor (remote sensing platform) | 发射时间 Launch date | 国家/地区 Nation/region | 空间分辨率 Spatial resolution (m) | 波段数 Bands | 光谱范围 Spectral range (nm) | 光谱分辨率 Spectral resolution (nm) |
---|---|---|---|---|---|---|
HIS (SIMASA) | - | 美国 USA | 25 | 220 | 430-2 400 | 20 |
FTHSI (Mighty Sat-2.1) | 2000-07 | 美国 USA | 30 | 256 | 450-1 050 | 10-50 |
Hyperion (EO-1) | 2000-11 | 美国 USA | 30 | 220 | 400-2 500 | 10 |
CHRIS (PROBA-1) | 2001-10 | 欧盟 European Union | 25 | 19 | 450-1 050 | 1.25-11.0 |
COIS (NEMO) | - | 美国 USA | 30 | 210 | 400-2 500 | 10 |
VENuS | - | 以色列和法国 Israel and France | 5.3 | 12 | 415-910 | 16-40 |
GLI (ADEOS-2) | 2002-12 | 日本 Japan | 250-1 000 | 36 | 380-1 195 | 10-100 |
HIS (HJ-1A) | 2008-09 | 中国 China | 100 | 110-128 | 450-950 | - |
HIS (Tiangong-1) | 2011-09 | 中国 China | 10 | 64 | 400-1 000 | 10 |
20 | 64 | 1 000-2 500 | 23 | |||
CMOS (OHS) | 2018-04 | 中国 China | 10 | 32 | 400-1 000 | 2.5 |
AHSI (GF-5) | 2018-05 | 中国 China | 30 | 330 | 400-2 500 | 5-10 |
DESIS (ISS) | 2018-07 | 德国 Germany | 30 | 235 | 400-1 000 | - |
HysIS (HysIS) | 2018-11 | 印度 India | 30 | 326 | 400-2 500 | - |
PRISMA (PRISMA) | 2019-03 | 意大利 Italy | 30 | 239 | 400-2 500 | <12 |
HIS (EnMap) | 2022-04 | 德国 Germany | 30 | 92 | 420-1 030 | 5-10 |
108 | 950-2 450 | 10-25 | ||||
HISUI (ALOS-3)* | - | 日本 Japan | 30 | 185 | 400-2 500 | - |
Table 1 Global partial hyperspectral satellite parameters
传感器(遥感平台) Sensor (remote sensing platform) | 发射时间 Launch date | 国家/地区 Nation/region | 空间分辨率 Spatial resolution (m) | 波段数 Bands | 光谱范围 Spectral range (nm) | 光谱分辨率 Spectral resolution (nm) |
---|---|---|---|---|---|---|
HIS (SIMASA) | - | 美国 USA | 25 | 220 | 430-2 400 | 20 |
FTHSI (Mighty Sat-2.1) | 2000-07 | 美国 USA | 30 | 256 | 450-1 050 | 10-50 |
Hyperion (EO-1) | 2000-11 | 美国 USA | 30 | 220 | 400-2 500 | 10 |
CHRIS (PROBA-1) | 2001-10 | 欧盟 European Union | 25 | 19 | 450-1 050 | 1.25-11.0 |
COIS (NEMO) | - | 美国 USA | 30 | 210 | 400-2 500 | 10 |
VENuS | - | 以色列和法国 Israel and France | 5.3 | 12 | 415-910 | 16-40 |
GLI (ADEOS-2) | 2002-12 | 日本 Japan | 250-1 000 | 36 | 380-1 195 | 10-100 |
HIS (HJ-1A) | 2008-09 | 中国 China | 100 | 110-128 | 450-950 | - |
HIS (Tiangong-1) | 2011-09 | 中国 China | 10 | 64 | 400-1 000 | 10 |
20 | 64 | 1 000-2 500 | 23 | |||
CMOS (OHS) | 2018-04 | 中国 China | 10 | 32 | 400-1 000 | 2.5 |
AHSI (GF-5) | 2018-05 | 中国 China | 30 | 330 | 400-2 500 | 5-10 |
DESIS (ISS) | 2018-07 | 德国 Germany | 30 | 235 | 400-1 000 | - |
HysIS (HysIS) | 2018-11 | 印度 India | 30 | 326 | 400-2 500 | - |
PRISMA (PRISMA) | 2019-03 | 意大利 Italy | 30 | 239 | 400-2 500 | <12 |
HIS (EnMap) | 2022-04 | 德国 Germany | 30 | 92 | 420-1 030 | 5-10 |
108 | 950-2 450 | 10-25 | ||||
HISUI (ALOS-3)* | - | 日本 Japan | 30 | 185 | 400-2 500 | - |
航空成像光谱仪 Airborne imaging spectrometer | 国家/地区 Nation/region | 波段 Bands | 光谱范围 Spectral range (nm) | 光谱分辨率 Spectral resolution (nm) |
---|---|---|---|---|
AVIRIS | 美国 USA | 224 | 400-2 500 | 10 |
CASI-3 | 加拿大 Canada | - | 400-1 000 | - |
HyMap | 澳大利亚 Australia | 100-200 | 450-2 500 | 10-20 |
OMIS | 中国 China | - | 460-12 500 | 5-6.25 |
PHI-2 | 中国 China | 128 | 400-950 | 5 |
256 | 950-2 500 | 10 | ||
WPHI | 中国 China | - | 400-2 500 | 5-6.25 |
APEX | 欧盟 European Union | 313 | 380-2 500 | - |
Table 2 Parameters of global aerial imaging spectrometers
航空成像光谱仪 Airborne imaging spectrometer | 国家/地区 Nation/region | 波段 Bands | 光谱范围 Spectral range (nm) | 光谱分辨率 Spectral resolution (nm) |
---|---|---|---|---|
AVIRIS | 美国 USA | 224 | 400-2 500 | 10 |
CASI-3 | 加拿大 Canada | - | 400-1 000 | - |
HyMap | 澳大利亚 Australia | 100-200 | 450-2 500 | 10-20 |
OMIS | 中国 China | - | 460-12 500 | 5-6.25 |
PHI-2 | 中国 China | 128 | 400-950 | 5 |
256 | 950-2 500 | 10 | ||
WPHI | 中国 China | - | 400-2 500 | 5-6.25 |
APEX | 欧盟 European Union | 313 | 380-2 500 | - |
国家 Nation | 研制单位 Development organization | 代表产品 Representative products |
---|---|---|
中国 China | 中国科学院长春光学精密机械与物理研究所 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | 基于Offner凸光栅分光谱方式的无人机载高光谱成像仪 Unmanned airborne hyperspectral imager based on Offner convex grating spectroscopy |
中国科学院上海技术物理研究所 Shanghai Institute of Technical Physics, Chinese Academy of Sciences | 小型航空成像光谱系统 Small aerial imaging spectral system | |
江苏双利合谱科技有限公司 Jiangsu Dualix Spectral Imaging Technology Co., Ltd. | GaiaSky-mini | |
德国 Germany | Cubert GmbH | S185, S485 |
美国 USA | BaySpec Inc. | BaySpec OCI-F, OCI-U-1000 |
Resonon Inc. | Resonon Pika XC2, Pika L, Pika NIR | |
Surface Optics Corporation | SOC710GX | |
Headwall Photonics Inc. | Hyperspec系列 Hyperspec series | |
加拿大 Canada | ITRES Inc. | ITRES CASI-1500H |
芬兰 Finland | SPECIM, Spectral Imaging LTD. | SPECIM AFX10, AisaFENIX |
挪威 Norway | Neo-Neon Holdings LTD. | HySpex系列 HySpex series |
Table 3 Common unmanned airborne hyperspectral imager and its development unit
国家 Nation | 研制单位 Development organization | 代表产品 Representative products |
---|---|---|
中国 China | 中国科学院长春光学精密机械与物理研究所 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | 基于Offner凸光栅分光谱方式的无人机载高光谱成像仪 Unmanned airborne hyperspectral imager based on Offner convex grating spectroscopy |
中国科学院上海技术物理研究所 Shanghai Institute of Technical Physics, Chinese Academy of Sciences | 小型航空成像光谱系统 Small aerial imaging spectral system | |
江苏双利合谱科技有限公司 Jiangsu Dualix Spectral Imaging Technology Co., Ltd. | GaiaSky-mini | |
德国 Germany | Cubert GmbH | S185, S485 |
美国 USA | BaySpec Inc. | BaySpec OCI-F, OCI-U-1000 |
Resonon Inc. | Resonon Pika XC2, Pika L, Pika NIR | |
Surface Optics Corporation | SOC710GX | |
Headwall Photonics Inc. | Hyperspec系列 Hyperspec series | |
加拿大 Canada | ITRES Inc. | ITRES CASI-1500H |
芬兰 Finland | SPECIM, Spectral Imaging LTD. | SPECIM AFX10, AisaFENIX |
挪威 Norway | Neo-Neon Holdings LTD. | HySpex系列 HySpex series |
生物多样性层次 Biodiversity level | 生境类型或地点 Ecosystem or location | 数据来源 Data source | 实现方法 Method | 参考文献 Reference |
---|---|---|---|---|
物种多样性 Species diversity | 热带干旱森林(美国佛罗里达州) Tropical dry forest (California, USA) | 卫星遥感 Satellite remote sensing | 归一化植被指数的平均值和标准差 Mean and standard deviation of NDVI | Gillespie, |
热带森林(巴拿马) Tropical forest (Panama) | 航空遥感 Airborne remote sensing | 反射率变异系数 Coefficient Variation of reflectance | Somers et al., | |
稀树草原(纳米比亚中部) Savannah (Central Namibia) | 航空遥感 Airborne remote sensing | 主成分的光谱质心距离 Distance from the spectral centroid in principal component space | Oldeland et al., | |
天然林和人工管理林(以色列) Managed and unmanaged groves (Israel) | 航空遥感 Airborne remote sensing | 监督分类(支持向量机) Supervised classification (support vector machine) | Paz-Kagan et al., | |
寒带森林(芬兰北部) Boreal forests (southern Finland) | 近地面遥感 Near-ground remote sensing | 归一化植被指数, 绿色归一化植被指数 NDVI, GNDVI | Saarinen et al., | |
功能性状多样性 Functional diversity | 热带森林(亚马孙到安第斯) Tropical forest (Amazon to Andes) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Asner et al., |
草地(瑞士) Grassland (Swiss) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Schweiger et al., | |
阔叶林和针叶林(美国东北部) Broad-leaved forest and coniferous forest (Northeast USA) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Singh et al., | |
遗传(系统发育)多样性 Genetic (phylogenetic) diversity | 温带森林(瑞士) Temperate forest (Swiss) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Czy? et al., |
洪都拉斯, 美国北部和中部 Honduras, North and Central America | 全量程光谱辐射计 Full-range spectroradiometer | 主坐标分析、偏最小二乘判别分析 PCO, PLS-DA | Cavender-Bares et al., |
Table 4 Case studies on the application of spectral diversity in plant diversity monitoring and assessment
生物多样性层次 Biodiversity level | 生境类型或地点 Ecosystem or location | 数据来源 Data source | 实现方法 Method | 参考文献 Reference |
---|---|---|---|---|
物种多样性 Species diversity | 热带干旱森林(美国佛罗里达州) Tropical dry forest (California, USA) | 卫星遥感 Satellite remote sensing | 归一化植被指数的平均值和标准差 Mean and standard deviation of NDVI | Gillespie, |
热带森林(巴拿马) Tropical forest (Panama) | 航空遥感 Airborne remote sensing | 反射率变异系数 Coefficient Variation of reflectance | Somers et al., | |
稀树草原(纳米比亚中部) Savannah (Central Namibia) | 航空遥感 Airborne remote sensing | 主成分的光谱质心距离 Distance from the spectral centroid in principal component space | Oldeland et al., | |
天然林和人工管理林(以色列) Managed and unmanaged groves (Israel) | 航空遥感 Airborne remote sensing | 监督分类(支持向量机) Supervised classification (support vector machine) | Paz-Kagan et al., | |
寒带森林(芬兰北部) Boreal forests (southern Finland) | 近地面遥感 Near-ground remote sensing | 归一化植被指数, 绿色归一化植被指数 NDVI, GNDVI | Saarinen et al., | |
功能性状多样性 Functional diversity | 热带森林(亚马孙到安第斯) Tropical forest (Amazon to Andes) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Asner et al., |
草地(瑞士) Grassland (Swiss) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Schweiger et al., | |
阔叶林和针叶林(美国东北部) Broad-leaved forest and coniferous forest (Northeast USA) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Singh et al., | |
遗传(系统发育)多样性 Genetic (phylogenetic) diversity | 温带森林(瑞士) Temperate forest (Swiss) | 航空遥感 Airborne remote sensing | 偏最小二乘回归 PLSR | Czy? et al., |
洪都拉斯, 美国北部和中部 Honduras, North and Central America | 全量程光谱辐射计 Full-range spectroradiometer | 主坐标分析、偏最小二乘判别分析 PCO, PLS-DA | Cavender-Bares et al., |
光谱多样性指标 Spectral diversity index | 计算方法 Method of calculation |
---|---|
变异系数 Coefficient of variation | 波段或光谱特征参数的变异系数 Coefficient of variation of band or spectral characteristic parameters |
光谱角 Spectral angle mapper | 光谱空间内光谱矢量的夹角 Included angle of spectral vector in spectral space |
信息离散度 Spectral information divergence | 光谱空间内光谱矢量的离散度 Dispersion of spectral vector in spectral space |
凸包面积或体积 Convex hull area or volume | 光谱主成分空间内凸包体积或波段与像元二维空间的投影面积 Projection area between convex hull volume or band and pixel two-dimensional space in spectral principal component space |
光谱物种 Spectral species | 光谱特征聚类 Spectral feature clustering |
光谱异质性 Spectral heterogeneity | 光谱主成分方差 Spectral principal component variance |
光谱质心距离 Spectral centroid distance | 各像元到光谱质心的平均距离 Average distance from each pixel to spectral centroid |
Rao’s Q | 像元丰度和距离的异质性 Heterogeneity of pixel abundance and distance |
Table 5 Common spectral diversity indices
光谱多样性指标 Spectral diversity index | 计算方法 Method of calculation |
---|---|
变异系数 Coefficient of variation | 波段或光谱特征参数的变异系数 Coefficient of variation of band or spectral characteristic parameters |
光谱角 Spectral angle mapper | 光谱空间内光谱矢量的夹角 Included angle of spectral vector in spectral space |
信息离散度 Spectral information divergence | 光谱空间内光谱矢量的离散度 Dispersion of spectral vector in spectral space |
凸包面积或体积 Convex hull area or volume | 光谱主成分空间内凸包体积或波段与像元二维空间的投影面积 Projection area between convex hull volume or band and pixel two-dimensional space in spectral principal component space |
光谱物种 Spectral species | 光谱特征聚类 Spectral feature clustering |
光谱异质性 Spectral heterogeneity | 光谱主成分方差 Spectral principal component variance |
光谱质心距离 Spectral centroid distance | 各像元到光谱质心的平均距离 Average distance from each pixel to spectral centroid |
Rao’s Q | 像元丰度和距离的异质性 Heterogeneity of pixel abundance and distance |
植被类型 Vegetation type | 遥感平台 Remote sensing platform | 空间分辨率 Spatial resolution | 反演特征 Inversion characteristics | 反演模型精度(R2) Inversion accuracy (R2) | 参考文献 Reference |
---|---|---|---|---|---|
亚热带森林 Subtropical forest | 卫星 Satellite | 10 m | α多样性 α-diversity | 0.65 | Yang et al., |
红树林 Mangrove | 卫星 Satellite | 2 m/10 m | α多样性 α-diversity | 0.42 | Wang et al., |
地中海落叶林 Mediterranean deciduous forest | 卫星 Satellite | 30 m | α多样性 α-diversity | 0.65 | Ceballos et al., |
稀树草原 Savanna | 卫星 Satellite | 30 m | α多样性 α-diversity | 0.42 | Madonsela et al., |
温带森林 Temperate forest | 卫星 Satellite | 10 m | α多样性 α-diversity | 0.25-0.52 | Camathias et al., |
高草草原 Prairie | 航空 Airborne | 1.1 m | α多样性 α-diversity | 0.44 | Wang et al., |
热带森林 Tropical forest | 航空 Airborne | 2 m | β多样性 β-diversity | 0.47 | Somers et al., |
松林和硬木林 Pine and hardwood forest | 航空 Airborne | 2 m | α多样性 α-diversity | 0.15-0.7 | Hakkenberg et al., |
热带山地森林 Tropical mountain forest | 航空 Airborne | 1 m | α多样性 α-diversity | 0.50-0.73 | Sch?fer et al., |
高草草原 Prairie | 近地面 Near-ground | 1 mm/0.75 m | α多样性 α-diversity | 0.51/0.32 | Gholizadeh et al., |
亚热带森林 Subtropical forest | 近地面 Near-ground | 1 m | α多样性 α-diversity | 0.83 | Zhao et al., |
草地 Grassland | 近地面 Near-ground | 0.3 m | α多样性 α-diversity | 0.73 | Zhao et al., |
草地 Grassland | 近地面 Near-ground | - | γ/β多样性 γ/β-diversity | 0.59/0.68 | Polley et al., |
高寒草甸 Alpine meadow | 近地面 Near-ground | 0.2 m | α多样性 α-diversity | 0.61 | Xu et al., |
Table 6 Cases of retrieving species diversity based on spectral diversity
植被类型 Vegetation type | 遥感平台 Remote sensing platform | 空间分辨率 Spatial resolution | 反演特征 Inversion characteristics | 反演模型精度(R2) Inversion accuracy (R2) | 参考文献 Reference |
---|---|---|---|---|---|
亚热带森林 Subtropical forest | 卫星 Satellite | 10 m | α多样性 α-diversity | 0.65 | Yang et al., |
红树林 Mangrove | 卫星 Satellite | 2 m/10 m | α多样性 α-diversity | 0.42 | Wang et al., |
地中海落叶林 Mediterranean deciduous forest | 卫星 Satellite | 30 m | α多样性 α-diversity | 0.65 | Ceballos et al., |
稀树草原 Savanna | 卫星 Satellite | 30 m | α多样性 α-diversity | 0.42 | Madonsela et al., |
温带森林 Temperate forest | 卫星 Satellite | 10 m | α多样性 α-diversity | 0.25-0.52 | Camathias et al., |
高草草原 Prairie | 航空 Airborne | 1.1 m | α多样性 α-diversity | 0.44 | Wang et al., |
热带森林 Tropical forest | 航空 Airborne | 2 m | β多样性 β-diversity | 0.47 | Somers et al., |
松林和硬木林 Pine and hardwood forest | 航空 Airborne | 2 m | α多样性 α-diversity | 0.15-0.7 | Hakkenberg et al., |
热带山地森林 Tropical mountain forest | 航空 Airborne | 1 m | α多样性 α-diversity | 0.50-0.73 | Sch?fer et al., |
高草草原 Prairie | 近地面 Near-ground | 1 mm/0.75 m | α多样性 α-diversity | 0.51/0.32 | Gholizadeh et al., |
亚热带森林 Subtropical forest | 近地面 Near-ground | 1 m | α多样性 α-diversity | 0.83 | Zhao et al., |
草地 Grassland | 近地面 Near-ground | 0.3 m | α多样性 α-diversity | 0.73 | Zhao et al., |
草地 Grassland | 近地面 Near-ground | - | γ/β多样性 γ/β-diversity | 0.59/0.68 | Polley et al., |
高寒草甸 Alpine meadow | 近地面 Near-ground | 0.2 m | α多样性 α-diversity | 0.61 | Xu et al., |
植物功能性状 Plant functional trait | 反演精度 Inversion accuracy (R2) | 参考文献 Reference |
---|---|---|
叶绿素含量 Chlorophyll content | 0.70-0.91 | Asner et al., |
胡萝卜含量 Carotenoids content | 0.63-0.87 | Asner et al., |
氮含量 Nitrogen content | 0.56-0.86 | Balzotti et al., |
磷含量 Phosphorus content | 0.46-0.83 | Pandey et al., |
钙含量 Calcium content | 0.48-0.79 | Asner et al., |
镁含量 Magnesium content | 0.33-0.70 | Asner et al., |
钾含量 Potassium content | 0.42-0.65 | Martin et al., |
硫含量 Sulfur content | 0.53-0.83 | Pandey et al., |
硼含量 Boron content | 0.32-0.53 | Asner et al., |
铜含量 Copper content | 0.51-0.86 | Pandey et al., |
锰含量 Manganese content | 0.32-0.64 | Pandey et al., |
铁含量 Iron content | 0.26-0.74 | Asner et al., |
锌含量 Zinc content | 0.26-0.73 | Asner et al., |
木质素含量 Lignin content | 0.47-0.76 | Asner et al., |
纤维素含量 Cellulose content | 0.61-0.84 | Asner et al., |
酚类含量 Phenols content | 0.44-0.73 | Asner et al., |
含水量 Water content | 0.49-0.77 | Asner et al., |
鞣酸类含量 Tannins content | 0.25-0.59 | McManus et al., |
糖、淀粉类含量 Sugar, starch content | 0.60-0.64 | Wang et al., |
比叶面积 Specific leaf area | 0.66-0.89 | Ali et al., |
比叶质量 Leaf mass per area | 0.61-0.88 | Singh et al., |
叶干物质含量 Leaf dry matter content | 0.23-0.83 | Ali et al., |
叶面积指数 Leaf area index | 0.71-0.83 | Darvishzadeh et al., |
Table 7 Detectable plant functional traits based on hyperspectral remote sensing
植物功能性状 Plant functional trait | 反演精度 Inversion accuracy (R2) | 参考文献 Reference |
---|---|---|
叶绿素含量 Chlorophyll content | 0.70-0.91 | Asner et al., |
胡萝卜含量 Carotenoids content | 0.63-0.87 | Asner et al., |
氮含量 Nitrogen content | 0.56-0.86 | Balzotti et al., |
磷含量 Phosphorus content | 0.46-0.83 | Pandey et al., |
钙含量 Calcium content | 0.48-0.79 | Asner et al., |
镁含量 Magnesium content | 0.33-0.70 | Asner et al., |
钾含量 Potassium content | 0.42-0.65 | Martin et al., |
硫含量 Sulfur content | 0.53-0.83 | Pandey et al., |
硼含量 Boron content | 0.32-0.53 | Asner et al., |
铜含量 Copper content | 0.51-0.86 | Pandey et al., |
锰含量 Manganese content | 0.32-0.64 | Pandey et al., |
铁含量 Iron content | 0.26-0.74 | Asner et al., |
锌含量 Zinc content | 0.26-0.73 | Asner et al., |
木质素含量 Lignin content | 0.47-0.76 | Asner et al., |
纤维素含量 Cellulose content | 0.61-0.84 | Asner et al., |
酚类含量 Phenols content | 0.44-0.73 | Asner et al., |
含水量 Water content | 0.49-0.77 | Asner et al., |
鞣酸类含量 Tannins content | 0.25-0.59 | McManus et al., |
糖、淀粉类含量 Sugar, starch content | 0.60-0.64 | Wang et al., |
比叶面积 Specific leaf area | 0.66-0.89 | Ali et al., |
比叶质量 Leaf mass per area | 0.61-0.88 | Singh et al., |
叶干物质含量 Leaf dry matter content | 0.23-0.83 | Ali et al., |
叶面积指数 Leaf area index | 0.71-0.83 | Darvishzadeh et al., |
[1] |
Ackerly DD, Cornwell WK. (2007). A trait-based approach to community assembly: partitioning of species trait values into within- and among-community components. Ecology Letters, 10, 135-145.
PMID |
[2] |
Ali AM, Darvishzadeh R, Skidmore AK, van Duren I, Heiden U, Heurich M. (2016). Estimating leaf functional traits by inversion of PROSPECT: assessing leaf dry matter content and specific leaf area in mixed mountainous forest. International Journal of Applied Earth Observation and Geoinformation, 45, 66-76.
DOI URL |
[3] |
Anderson CB. (2018). Biodiversity monitoring, earth observations and the ecology of scale. Ecology Letters, 21, 1572-1585.
DOI PMID |
[4] |
Asner GP, Anderson CB, Martin RE, Tupayachi R, Knapp DE, Sinca F. (2015a). Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy. Nature Geoscience, 8, 567-573.
DOI URL |
[5] | Asner GP. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64, 234-253. |
[6] |
Asner GP, Knapp DE, Kennedy-Bowdoin T, Jones MO, Martin RE, Boardman JW, Field CB. (2007). Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems. Journal of Applied Remote Sensing, 1, 013536. DOI: 10.1111/ele.13106.
DOI |
[7] |
Asner GP, Martin RE, Anderson CB, Knapp DE. (2015b). Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sensing of Environment, 158, 15-27.
DOI URL |
[8] |
Asner GP, Martin RE, Carranza-Jimenez L, Sinca F, Tupayachi R, Anderson CB, Martinez P. (2014). Functional and biological diversity of foliar spectra in tree canopies throughout the Andes to Amazon region. New Phytologist, 204, 127-139.
DOI PMID |
[9] |
Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson CB, Sinca F, Vaughn NR, Llactayo W. (2017). Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science, 355, 385-388.
DOI PMID |
[10] |
Bailey S, Horner-Devine C, Luck G, Moore L, Carney K, Anderson S, Betrus C, Fleishman E. (2004). Primary productivity and species richness: relationships among functional guilds, residency groups and vagility classes at multiple spatial scales. Ecography, 27, 207-217.
DOI URL |
[11] | Balzotti CS, Asner GP, Taylor PG, Cleveland CC, Cole R, Martin RE, Nasto M, Osborne BB, Porder S, Townsend AR. (2016). Environmental controls on canopy foliar nitrogen distributions in a Neotropical lowland forest. Ecological Applications, 26, 2449-2462. |
[12] |
Blonder B, Graae BJ, Greer B, Haagsma M, Helsen K, Kapás RE, Pai H, Rieksta J, Sapena D, Still CJ, Strimbeck R. (2020). Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.). Journal of Ecology, 108, 175-188.
DOI URL |
[13] |
Bongalov B, Burslem DFRP, Jucker T, Thompson SED, Rosindell J, Swinfield T, Nilus R, Clewley D, Phillips OL, Coomes DA. (2019). Reconciling the contribution of environmental and stochastic structuring of tropical forest diversity through the lens of imaging spectroscopy. Ecology Letters, 22, 1608-1619.
DOI PMID |
[14] |
Cabacinha CD, de Castro SS (2009). Relationships between floristic diversity and vegetation indices, forest structure and landscape metrics of fragments in Brazilian Cerrado. Forest Ecology and Management, 257, 2157-2165.
DOI URL |
[15] |
Camathias L, Bergamini A, Küchler M, Stofer S, Baltensweiler A. (2013). High-resolution remote sensing data improves models of species richness. Applied Vegetation Science, 16, 539-551.
DOI URL |
[16] | Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S. (2012). Biodiversity loss and its impact on humanity. Nature, 486, 59-67. |
[17] |
Carlson KM, Asner GP, Hughes RF, Ostertag R, Martin RE. (2007). Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems, 10, 536-549.
DOI URL |
[18] |
Caughlin TT, Graves SJ, Asner GP, van Breugel M, Hall JS, Martin RE, Ashton MS, Bohlman SA. (2016). A hyperspectral image can predict tropical tree growth rates in single-species stands. Ecological Applications, 26, 2369-2375.
DOI URL |
[19] |
Cavender-Bares J, Meireles JE, Couture JJ, Kaproth MA, Kingdon CC, Singh A, Serbin SP, Center A, Zuniga E, Pilz G, Townsend PA. (2016). Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity. Remote Sensing, 8, 221. DOI: 10.3390/rs8030221.
DOI |
[20] |
Cayuela L, Benayas JMR, Justel A, Salas-Rey J. (2006). Modelling tree diversity in a highly fragmented tropical montane landscape. Global Ecology and Biogeography, 15, 602-613.
DOI URL |
[21] |
Ceballos A, Hernández J, Corvalán P, Galleguillos M. (2015). Comparison of airborne LiDAR and satellite hyperspectral remote sensing to estimate vascular plant richness in deciduous mediterranean forests of central Chile. Remote Sensing, 7, 2692-2714.
DOI URL |
[22] |
Chang CI. (2000). An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis. IEEE Transactions on Information Theory, 46, 1927-1932.
DOI URL |
[23] |
Chapin III FS, Zavaleta ES, Eviner VT, Naylor RL, Vitousek PM, Reynolds HL, Hooper DU, Lavorel S, Sala OE, Hobbie SE, Mack MC, Díaz S. (2000). Consequences of changing biodiversity. Nature, 405, 234-242.
DOI URL |
[24] |
Chavana-Bryant C, Malhi Y, Wu J, Asner GP, Anastasiou A, Enquist BJ, Cosio Caravasi EG, Doughty CE, Saleska SR, Martin RE, Gerard FF. (2017). Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytologist, 214, 1049-1063.
DOI PMID |
[25] |
Chen LT, Zhang Y, Nunes MH, Stoddart J, Khoury S, Chan AHY, Coomes DA. (2022). Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: Can a general model be applied across a growing season? Remote Sensing of Environment, 269, 112767. DOI: 10.1016/j.rse.2021.112767.
DOI |
[26] |
Cho MA, Mathieu R, Asner GP, Naidoo L, van Aardt J, Ramoelo A, Debba P, Wessels K, Main R, Smit IPJ, Erasmus B. (2012). Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sensing of Environment, 125, 214-226.
DOI URL |
[27] |
Chrysafis I, Korakis G, Kyriazopoulos AP, Mallinis G. (2020). Predicting tree species diversity using geodiversity and sentinel-2 multi-seasonal spectral information. Sustainability, 12, 9250. DOI: 10.3390/su12219250.
DOI |
[28] |
Coops NC, Tompaski P, Nijland W, Rickbeil GJM, Nielsen SE, Bater CW, Stadt JJ. (2016). A forest structure habitat index based on airborne laser scanning data. Ecological Indicators, 67, 346-357.
DOI URL |
[29] |
Couture JJ, Singh A, Rubert-Nason KF, Serbin SP, Lindroth RL, Townsend PA. (2016). Spectroscopic determination of ecologically relevant plant secondary metabolites. Methods in Ecology and Evolution, 7, 1402-1412.
DOI URL |
[30] |
Czyż EA, Guillén Escribà C, Wulf H, Tedder A, Schuman MC, Schneider FD, Schaepman ME. (2020). Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series. Ecology and Evolution, 10, 7419-7430.
DOI PMID |
[31] |
Dahlin KM. (2016). Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix. Ecological Applications, 26, 2758-2768.
DOI URL |
[32] |
Danner M, Berger K, Wocher M, Mauser W Hank T (2017). Retrieval of biophysical crop variables from multi-angular canopy spectroscopy. Remote Sensing, 9, 21. DOI: 10.3390/rs9070726.
DOI |
[33] |
Darvishzadeh R, Atzberger C, Skidmore A, Schlerf M. (2011). Mapping grassland leaf area index with airborne hyperspectral imagery: a comparison study of statistical approaches and inversion of radiative transfer models. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 894-906.
DOI URL |
[34] |
Degerickx J, Okujeni A, Iordache MD, Hermy M, van der Linden S, Somers B. (2017). A novel spectral library pruning technique for spectral unmixing of urban land cover. Remote Sensing, 9, 565. DOI: 10.3390/rs9060565.
DOI |
[35] |
Durán SM, Martin RE, Díaz S, Maitner BS, Malhi Y, Salinas N, Shenkin A, Silman MR, Wieczynski DJ, Asner GP, Bentley LP, Savage VM, Enquist BJ. (2019). Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient. Science Advances, 5, eaaw8114. DOI: 10.1126/ sciadv.aaw8114.
DOI |
[36] |
Fallon B, Yang A, Nguyen C, Armour I, Juzwik J, Montgomery R, Cavender-Bares J. (2020). Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes. Tree Physiology, 40, 377-390.
DOI PMID |
[37] |
Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A. (2016). Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186, 64-87.
DOI URL |
[38] |
Feilhauer H, Schmidtlein S. (2009). Mapping continuous fields of forest alpha and beta diversity. Applied Vegetation Science, 12, 429-439.
DOI URL |
[39] |
Feilhauer H, Schmidtlein S. (2011). On variable relations between vegetation patterns and canopy reflectance. Ecological Informatics, 6, 83-92.
DOI URL |
[40] |
Féret JB, Asner GP. (2014). Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecological Applications, 24, 1289-1296.
DOI URL |
[41] |
Féret JB, Berger K, de Boissieu F, Malenovský Z. (2021). PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents. Remote Sensing of Environment, 252, 112173. DOI: 10.1016/j.rse.2020.112173.
DOI |
[42] |
Féret JB, Gitelson AA, Noble SD, Jacquemoud S. (2017). PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sensing of Environment, 193, 204-215.
DOI URL |
[43] |
Foody GM, Cutler MEJ. (2003). Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography, 30, 1053-1066.
DOI URL |
[44] |
Frye HA, Aiello-Lammens ME, Euston-Brown D, Jones CS, Kilroy Mollmann H, Merow C, Slingsby JA, van der Merwe H, Wilson AM, Silander Jr JA. (2021). Plant spectral diversity as a surrogate for species, functional and phylogenetic diversity across a hyper-diverse biogeographic region. Global Ecology and Biogeography, 30, 1403-1417.
DOI URL |
[45] |
Fu P, Meacham-Hensold K, Guan KY, Bernacchi CJ. (2019). Hyperspectral leaf reflectance as proxy for photosynthetic capacities: an ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730-730.
DOI PMID |
[46] | Fu YY, Yang G, Guan SH. (2020). Research status and development trend of hyperspectral imagers onboard airborne and spaceborne platforms. Infrared, 41(8), 1-8. |
[付严宇, 杨桄, 关世豪 (2020). 航空航天高光谱成像仪研究现状及发展趋势. 红外, 41(8), 1-8.] | |
[47] |
Funk JL, Larson JE, Ames GM, Butterfield BJ, Cavender-Bares J, Firn J, Laughlin DC, Sutton-Grier AE, Williams L, Wright J. (2017). Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biological Reviews, 92, 1156-1173.
DOI URL |
[48] |
Gamon JA, Serrano L, Surfus JS. (1997). The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia, 112, 492-501.
DOI PMID |
[49] | Gamon JA, Wang R, Gholizadeh H, Zutta B, Townsend PA, Cavender-Bares J. (2020). Consideration of scale in remote sensing of biodiversity//Cavender-Bares J, Gamon JA, Townsend PA. Remote Sensing of Plant Biodiversity. Springer, Gewerbestrasse, Switzerland. 425-447. |
[50] |
Gholizadeh H, Gamon JA, Helzer CJ, Cavender-Bares J. (2020). Multi-temporal assessment of grassland α- and β-diversity using hyperspectral imaging. Ecological Applications, 30, e02145. DOI: 10.1002/eap.2145.
DOI |
[51] |
Gholizadeh H, Gamon JA, Zygielbaum AI, Wang R, Schweiger AK, Cavender-Bares J. (2018). Remote sensing of biodiversity: soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems. Remote Sensing of Environment, 206, 240-253.
DOI URL |
[52] |
Gillespie TW. (2005). Predicting woody-plant species richness in tropical dry forests: a case study from south florida, USA. Ecological Applications, 15, 27-37.
DOI URL |
[53] |
Gonzalez A, Germain RM, Srivastava DS, Filotas E, Dee LE, Gravel D, Thompson PL, Isbell F, Wang SP, Kéfi S, Montoya J, Zelnik YR, Loreau M. (2020). Scaling-up biodiversity-ecosystem functioning research. Ecology Letter, 23, 757-776.
DOI URL |
[54] |
Gould W. (2000). Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots. Ecological Applications, 10, 1861-1870.
DOI URL |
[55] |
Guo QH, Hu TY, Ma Q, Xu KX, Yang QL, Sun QH, Li YM, Su YJ. (2020). Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology, 44, 418-435.
DOI URL |
[郭庆华, 胡天宇, 马勤, 徐可心, 杨秋丽, 孙千惠, 李玉美, 苏艳军 (2020). 新一代遥感技术助力生态系统生态学研究. 植物生态学报, 44, 418-435.]
DOI |
|
[56] |
Guo QH, Liu J, Li YM, Zhai QP, Wang YC, Wu FF, Hu TY, Wan HW, Liu HM, Shen WM. (2016). A near-surface remote sensing platform for biodiversity monitoring, perspectives and prospects. Biodiversity Science, 24, 1249-1266.
DOI URL |
[郭庆华, 刘瑾, 李玉美, 翟秋萍, 王永财, 吴芳芳, 胡天宇, 万华伟, 刘慧明, 申文明 (2016). 生物多样性近地面遥感监测, 应用现状与前景展望. 生物多样性, 24, 1249-1266.]
DOI |
|
[57] |
Hakkenberg CR, Zhu K, Peet RK, Song C. (2018). Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing. Ecology, 99, 474-487.
DOI PMID |
[58] |
Helsen K, van Cleemput E, Bassi L, Somers B, Honnay O. (2020). Optical traits perform equally well as directly-measured functional traits in explaining the impact of an invasive plant on litter decomposition. Journal of Ecology, 108, 2000-2011.
DOI URL |
[59] |
Hernández-Stefanoni JL, Gallardo-Cruz JA, Meave JA, Rocchini D, Bello-Pineda J, López-Martínez JO. (2012). Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data. International Journal of Applied Earth Observation and Geoinformation, 19, 359-368.
DOI URL |
[60] |
Homolová L, Malenovský Z, Clevers JGPW, García-Santos G, Schaepman ME. (2013). Review of optical-based remote sensing for plant trait mapping. Ecological Complexity, 15, 1-16.
DOI URL |
[61] | Hu JB, Zhang J. (2018). Unmanned Aerial Vehicle remote sensing in ecology: advances and prospects. Acta Ecologica Sinica, 38, 20-30. |
[胡健波, 张健 (2018). 无人机遥感在生态学中的应用进展. 生态学报, 38, 20-30.] | |
[62] |
Itten KI, Dell’Endice F, Hueni A, Kneubühler M, Schläpfer D, Odermatt D, Seidel F, Huber S, Schopfer J, Kellenberger T, Bühler Y, D’Odorico P, Nieke J, Alberti E, Meuleman K. (2008). APEX-the hyperspectral ESA airborne prism experiment. Sensors (Basel), 8, 6235-6259.
DOI URL |
[63] |
Jacquemoud S, Baret F. (1990). PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75-91.
DOI URL |
[64] | Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, François C, Ustin SL. (2009). PROSPECT+SAIL models: a review of use for vegetation characterization. Remote Sensing of Environment, 113, S56-S66. |
[65] | Jia W, Pang Y, Yue CR, Li ZY. (2015). Mountain forest classification based on AISA eagle II hyperspectral data. Forest Inventory and Planning, 40(1), 9-14. |
[荚文, 庞勇, 岳彩荣, 李增元 (2015). 基于AISA Eagle II机载高光谱数据的普洱市山区森林分类. 林业调查规划, 40(1), 9-14.] | |
[66] |
Kalacska M, Sanchez-Azofeifa GA, Rivard B, Caelli T, White HP, Calvo-Alvarado JC. (2007). Ecological fingerprinting of ecosystem succession: estimating secondary tropical dry forest structure and diversity using imaging spectroscopy. Remote Sensing of Environment, 108, 82-96.
DOI URL |
[67] |
Kayet N, Pathak K, Chakrabarty A, Kumar S, Singh CP, Chowdary VM. (2020). Assessment of mining activities on tree species and diversity in hilltop mining areas using Hyperion and Landsat data. Environmental Science and Pollution Research, 27, 42750-42766.
DOI URL |
[68] | Kerr JT, Southwood TRE, Cihlar J. (2001). Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. Proceedings of the National Academy of Sciences of the United States of America, 98, 11365-11370. |
[69] |
Kim D. (2018). Modeling spatial and temporal dynamics of plant species richness across tidal creeks in a temperate salt marsh. Ecological Indicators, 93, 188-195.
DOI URL |
[70] | Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA. (2009). Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing of Environment, 113, S78-S91. |
[71] |
Kothari S, Schweiger AK. (2022). Plant spectra as integrative measures of plant phenotypes. Journal of Ecology, 110, 2536-2554.
DOI URL |
[72] |
Krause KS, Kuester MA, Johnson BR, McCorkel J, Kampe TU. (2011). Early algorithm development efforts for the National Ecological Observatory Network Airborne Observation Platform imaging spectrometer and waveform LiDAR instruments. Imaging Spectrometry XVI, 81580D. DOI: 10.1117/12.894178.
DOI |
[73] |
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH. (1993). The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44, 145-163.
DOI URL |
[74] |
Laliberté E, Schweiger AK, Legendre P. (2020). Partitioning plant spectral diversity into alpha and beta components. Ecology Letters, 23, 370-380.
DOI PMID |
[75] |
Laughlin DC, Laughlin DE. (2013). Advances in modeling trait-based plant community assembly. Trends in Plant Science, 18, 584-593.
DOI PMID |
[76] |
Lausch A, Bannehr L, Beckmann M, Boehm C, Feilhau H, Hacker JM, Heurich M, Jung A, Klenke R, Neumann C, Pause M, Rocchini D, Schaepman ME, SChmidtlein S, Schulz K, et al. (2016). Linking Earth Observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives. Ecological Indicators, 70, 317-339.
DOI URL |
[77] | Le Bagousse-Pinguet Y, Soliveres S, Gross N, Torices R, Berdugo M, Maestre FT. (2019). Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proceedings of the National Academy of Sciences of the United States of America, 116, 8419-8424. |
[78] | Li JL, Pang Y, Li ZY, Jia W. (2019). Tree species classification by airborne hyperspectral image of forest in cloud shadow area. Forest Research, 32(5), 136-141. |
[李军玲, 庞勇, 李增元, 荚文 (2019). 云阴影区机载高光谱影像森林树种分类. 林业科学研究, 32(5), 136-141.] | |
[79] | Li Y, Yang CK, Zhou CP, Su JJ. (2019). Advance and application of UAV hyperspectral imaging equipment. Bulletin of Surveying and Mapping, (9), 1-6. |
[李月, 杨灿坤, 周春平, 苏俊杰 (2019). 无人机载高光谱成像设备研究及应用进展. 测绘通报, (9), 1-6.] | |
[80] |
Locherer M, Hank T, Danner M, Mauser W. (2015). Retrieval of seasonal leaf area index from simulated enmap data through optimized lut-based inversion of the PROSAIL model. Remote Sensing, 7, 10321-10346.
DOI URL |
[81] |
Lopatin J, Dolos K, Kattenborn T, Fassnacht FE. (2019). How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing. Remote Sensing in Ecology and Conservation, 5, 302-317.
DOI URL |
[82] |
Lopatin J, Fassnacht FE, Kattenborn T, Schmidtlein S. (2017). Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote Sensing of Environment, 201, 12-23.
DOI URL |
[83] |
Lucas R, Bunting P, Paterson M, Chisholm L. (2008). Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment, 112, 2088-2103.
DOI URL |
[84] |
Lucas K, Carter G. (2008). The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island, Mississippi. Remote Sensing of Environment, 112, 3908-3915.
DOI URL |
[85] |
Madonsela S, Cho MA, Ramoelo A, Mutanga O. (2017). Remote sensing of species diversity using Landsat 8 spectral variables. ISPRS Journal of Photogrammetry and Remote Sensing, 133, 116-127.
DOI URL |
[86] |
Madritch MD, Kingdon CC, Singh A, Mock KE, Lindroth RL, Townsend PA. (2014). Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales. Philosophical Transactions of the Royal Society B: Biological Sciences, 369, 20130194. DOI: 10.1098/rstb.2013.0194.
DOI |
[87] |
Marconi S, Graves SJ, Weinstein BG, Bohlman S, White EP. (2021). Estimating individual-level plant traits at scale. Ecological Applications, 31, e02300. DOI: 10.1002/eap.2300.
DOI |
[88] |
Martin R, Chadwick KD, Brodrick P, Carranza-Jimenez L, Vaughn N, Asner G. (2018). An approach for foliar trait retrieval from airborne imaging spectroscopy of tropical forests. Remote Sensing, 10, 199. DOI: 10.3390/rs10020199.
DOI |
[89] |
Martínez-López J, Carreño MF, Palazón-Ferrando JA, Martínez-Fernández J, Esteve MA. (2014). Remote sensing of plant communities as a tool for assessing the condition of semiarid Mediterranean saline wetlands in agricultural catchments. International Journal of Applied Earth Observation and Geoinformation, 26, 193-204.
DOI URL |
[90] |
Maschler J, Atzberger C, Immitzer M. (2018). Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sensing, 10, 1218. DOI: 10.3390/rs10081218.
DOI |
[91] |
McManus K, Asner G, Martin R, Dexter K, Kress W, Field C. (2016). Phylogenetic structure of foliar spectral traits in tropical forest canopies. Remote Sensing, 8, 196. DOI: 10.3390/rs8030196.
DOI |
[92] |
Meireles JE, Cavender-Bares J, Townsend PA, Ustin S, Gamon JA, Schweiger AK, Schaepman ME, Asner GP, Martin RE, Singh A, Schrodt F, Chlus A, O’Meara B. (2020a). Leaf reflectance spectra capture the evolutionary history of seed plant. New Phytologist, 228, 485-493.
DOI URL |
[93] | Meireles JE, O’Meara B, Cavender-Bares J. (2020b). Linking leaf spectra to the plant tree of life//Cavender-Bares J, Gamon JA, Townsend PA. Remote Sensing of Plant Biodiversity. Springer, Gewerbestrasse, Switzerland. 155-172. |
[94] |
Möckel T, Dalmayne J, Schmid BC, Prentice HC, Hall K. (2016). Airborne hyperspectral data predict fine-scale plant species diversity in grazed dry grasslands. Remote Sensing, 8, 133. DOI: 10.3390/rs8020133.
DOI |
[95] |
Naeem S, Duffy JE, Zavaleta E. (2012). The functions of biological diversity in an age of extinction. Science, 336, 1401-1406.
DOI PMID |
[96] |
Nagendra H, Rocchini D. (2008). High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail. Biodiversity and Conservation, 17, 3431-3442.
DOI URL |
[97] |
Nagendra H, Rocchini D, Ghate R, Sharma B, Pareeth S. (2010). Assessing plant diversity in a dry tropical forest: comparing the utility of landsat and ikonos satellite images. Remote Sensing, 2, 478-496.
DOI URL |
[98] |
Naidoo L, Cho MA, Mathieu R, Asner G. (2012). Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 167-179.
DOI URL |
[99] |
Noda HM, Muraoka H, Nasahara KN. (2021). Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest. Journal of Plant Research, 134, 737-751.
DOI PMID |
[100] |
Oldeland J, Wesuls D, Rocchini D, Schmidt M, Jürgens N. (2010). Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity? Ecological Indicators, 10, 390-396.
DOI URL |
[101] |
Ollinger SV. (2011). Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist, 189, 375-394.
DOI PMID |
[102] |
Ollinger SV, Richardson AD, Martin ME, Hollinger DY, Frolking SE, Reich PB, Plourde LC, Katul GG, Munger JW, Oren R, Smith ML, Bolstad PV, Cook BD, Day MC, Martin TA, et al. (2008). Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proceedings of the National Academy of Sciences of the United States of America, 105, 19336-19341.
DOI PMID |
[103] |
Palmer MW, Earls PG, Hoagland BW, White PS, Wohlgemuth T. (2002). Quantitative tools for perfecting species lists. Environmetrics, 13, 121-137.
DOI URL |
[104] |
Pandey P, Ge YF, Stoerger V, Schnable JC. (2017). High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Frontiers in Plant Science, 8, 1348. DOI: 3389/fpls.2017.01348.
DOI |
[105] |
Pang Y, Li ZY, Ju HB, Lu H, Jia W, Si L, Guo Y, Liu QW, Li SM, Liu LX, Xie BB, Tan BX, Dian YY. (2016). LiCHy: the CAF’s LiDAR, CCD and hyperspectral integrated airborne observation system. Remote Sensing, 8, 398. DOI: 10.3390/rs8050398.
DOI |
[106] | Pang Y, Liang XJ, Jia W, Si L, Yan GJ, Shi JC. (2021). The comprehensive airborne remote sensing experiment in Saihanba forest farm. National Remote Sensing Bulletin, 25, 904-917. |
[庞勇, 梁晓军, 荚文, 斯林, 阎广建, 施建成 (2021). 塞罕坝林场机载综合遥感试验. 遥感学报, 25, 904-917.] | |
[107] |
Papp L, van Leeuwen B, Szilassi P, Tobak Z, Szatmári J, Árvai M, Mészáros J, Pásztor L. (2021). Monitoring invasive plant species using hyperspectral remote sensing data. Land, 10, 29-29.
DOI URL |
[108] |
Parviainen M, Luoto M, Heikkinen RK. (2009). The role of local and landscape level measures of greenness in modelling boreal plant species richness. Ecological Modelling, 220, 2690-2701.
DOI URL |
[109] |
Paz-Kagan T, Caras T, Herrmann I, Shachak M, Karnieli A. (2017). Multiscale mapping of species diversity under changed land use using imaging spectroscopy. Ecological Applications, 27, 1466-1484.
DOI URL |
[110] |
Peng J, Li Y, Tian L, Liu YX, Wang YL. (2015). Vegetation dynamics and associated driving forces in eastern China during 1999-2008. Remote Sensing, 7, 13641-13663.
DOI URL |
[111] |
Pereira HM, Ferrier S, Walters M, Geller GN, Jongman RHG, Scholes RJ, Bruford MW, Brummitt N, Butchart SHM, Cardoso AC, Coops NC, Dulloo E, Faith DP, Freyhof J, Gregory RD, et al. (2013). Essential biodiversity variables. Science, 339, 277-278.
DOI PMID |
[112] |
Petchey OL, Gaston KJ. (2006). Functional diversity: back to basics and looking forward. Ecology Letters, 9, 741-758.
DOI PMID |
[113] |
Pettorelli N, Safi K, Turner W. (2014). Satellite remote sensing, biodiversity research and conservation of the future. Philosophical Transactions of the Royal Society B: Biological Sciences, 369, 20130190. DOI: 10.1098/rstb. 2013.0190.
DOI |
[114] |
Polley HW, Yang CH, Wilsey BJ, Fay PA. (2019). Spectral heterogeneity predicts local-scale gamma and beta diversity of mesic grasslands. Remote Sensing, 11, 458. DOI: 10.3390/rs11040458.
DOI |
[115] |
Rebelo AJ, Somers B, Esler KJ, Meire P. (2018). Can wetland plant functional groups be spectrally discriminated? Remote Sensing of Environment, 210, 25-34.
DOI URL |
[116] |
Rocchini D. (2007). Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sensing of Environment, 111, 423-434.
DOI URL |
[117] |
Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M, Nagendra H, Oldeland J, Ricotta C, Southworth J, Neteler M. (2010). Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecological Informatics, 5, 318-329.
DOI URL |
[118] |
Rocchini D, Chiarucci A, Loiselle SA. (2004). Testing the spectral variation hypothesis by using satellite multispectral images. Acta Oecologica, 26, 117-120.
DOI URL |
[119] |
Rocchini D, Luque S, Pettorelli N, Bastin L, Doktor D, Faedi N, Feilhauer H, Féret JB, Foody GM, Gavish Y, Godinho S, Kunin WE, Lausch A, Leitão PJ, Marcantonio M, et al. (2018). Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring. Methods in Ecology and Evolution, 9, 1787-1798.
DOI URL |
[120] |
Rocchini D, Marcantonio M, da Re D, Bacaro G, Feoli E, Foody GM, Furrer R, Harrigan RJ, Kleijn D, Iannacito M, Lenoir J, Lin M, Malavasi MX, Marchetto E, Meyer RS, et al. (2021). From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy. Global Ecology and Biogeography, 30, 1153-1162.
DOI URL |
[121] |
Rossi C, Kneubuhler M, Schutz M, Schaepman ME, Haller RM, Risch AC. (2021). Remote sensing of spectral diversity: a new methodological approach to account for spatio-temporal dissimilarities between plant communities. Ecological Indicators, 130. DOI: 10.1016/j.ecolind.2021.108106.
DOI |
[122] |
Roth KL, Roberts DA, Dennison PE, Alonzo M, Peterson SH, Beland M. (2015). Differentiating plant species within and across diverse ecosystems with imaging spectroscopy. Remote Sensing of Environment, 167, 135-151.
DOI URL |
[123] |
Saarinen N, Vastaranta M, Näsi R, Rosnell T, Hakala T, Honkavaara E, Wulder MA, Luoma V, Tommaselli AMG, Imai NN, Ribeiro EAW, Guimarães RB, Holopainen M, Hyyppä J. (2018). Assessing biodiversity in boreal forests with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sensing, 10, 338. DOI: 10.3390/rs10020338.
DOI |
[124] |
Sankey T, Donager J, McVay J, Sankey JB. (2017). UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sensing of Environment, 195, 30-43.
DOI URL |
[125] |
Sankey T, Hultine K, Blasini D, Koepke D, Bransky N, Grady K, Cooper H, Gehring C, Allan G. (2021). UAV thermal image detects genetic trait differences among populations and genotypes of Fremont cottonwood (Populus fremontii, Salicaceae). Remote Sensing in Ecology and Conservation, 7, 245-258.
DOI URL |
[126] |
Sankey TT, McVay J, Swetnam TL, McClaran MP, Heilman P, Nichols M. (2018). UAV hyperspectral and lidar data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sensing in Ecology and Conservation, 4, 20-33.
DOI URL |
[127] |
Sasaki T, Imanishi J, Ioki K, Morimoto Y, Kitada K. (2012). Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data. Landscape and Ecological Engineering, 8, 157-171.
DOI URL |
[128] |
Schäfer E, Heiskanen J, Heikinheimo V, Pellikka P. (2016). Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data. Ecological Indicators, 64, 49-58.
DOI URL |
[129] |
Schmidtlein S, Fassnacht FE. (2017). The spectral variability hypothesis does not hold across landscapes. Remote Sensing of Environment, 192, 114-125.
DOI URL |
[130] |
Schmidtlein S, Sassin J. (2004). Mapping of continuous floristic gradients in grasslands using hyperspectral imagery. Remote Sensing of Environment, 92, 126-138.
DOI URL |
[131] |
Schneider FD, Morsdorf F, Schmid B, Petchey OL, Hueni A, Schimel DS, Schaepman ME. (2017). Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nature Communitions, 8, 1441. DOI: 10.1038/s41467-017-01530-3.
DOI |
[132] |
Schweiger AK, Cavender-Bares J, Kothari S, Townsend PA, Madritch MD, Grossman JJ, Gholizadeh H, Wang R, Gamon JA. (2021). Coupling spectral and resource-use complementarity in experimental grassland and forest communities. Proceedings of the Royal Society B: Biological Sciences, 288, 20211290. DOI: 10.1098/rspb.2021.1290.
DOI |
[133] | Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D, Gamon JA. (2018). Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nature Ecology & Evolution, 2, 976-982. |
[134] |
Schweiger AK, Risch AC, Damm A, Kneubühler M, Haller R, Schaepman ME, Schütz M. (2015). Using imaging spectroscopy to predict above-ground plant biomass in alpine grasslands grazed by large ungulates. Journal of Vegetation Science, 26, 175-190.
DOI URL |
[135] |
Schweiger AK, Schütz M, Risch AC, Kneubühler M, Haller R, Schaepman ME. (2017). How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response. Methods in Ecology and Evolution, 8, 86-95.
DOI URL |
[136] |
Serbin SP, Singh A, Desai AR, Dubois SG, Jablonski AD, Kingdon CC, Kruger EL, Townsend PA. (2015). Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sensing of Environment, 167, 78-87.
DOI URL |
[137] |
Serbin SP, Singh A, McNeil BE, Kingdon CC, Townsend PA. (2014). Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecological Applications, 24, 1651-1669.
DOI URL |
[138] |
Signoroni A, Savardi M, Baronio A, Benini S. (2019). Deep learning meets hyperspectral image analysis: a multidisciplinary review. Journal of Imaging, 5, 52. DOI: 10.3390/jimaging5050052.
DOI |
[139] |
Simonson WD, Allen HD, Coomes DA. (2012). Use of an airborne lidar system to model plant species composition and diversity of Mediterranean oak forests. Conservation Biology, 26, 840-850.
DOI PMID |
[140] |
Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA. (2015). Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecological Applications, 25, 2180-2197.
DOI URL |
[141] |
Skidmore AK, Pettorelli N, Coops NC, Geller GN, Hansen M, Lucas R, Müecher CA, O’Connor B, Paganini M, Pereira HM, Schaepman ME, Turner W, Wang TJ, Wegmann M. (2015). Agree on biodiversity metrics to track from space. Nature, 523, 403-405.
DOI URL |
[142] |
Somers B, Asner GP. (2014). Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: wavelength adaptive spectral mixture analysis. International Journal of Applied Earth Observation and Geoinformation, 31, 57-66.
DOI URL |
[143] |
Somers B, Asner GP, Martin RE, Anderson CB, Knapp DE, Wright SJ, van de Kerchove R. (2015). Mesoscale assessment of changes in tropical tree species richness across a bioclimatic gradient in Panama using airborne imaging spectroscopy. Remote Sensing of Environment, 167, 111-120.
DOI URL |
[144] | Sun ZY, Chen YQ, Yang L, Tang GL, Yuan SX, Lin ZW. (2017). Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology. Chinese Journal of Applied Ecology, 28, 528-536. |
[孙中宇, 陈燕乔, 杨龙, 唐光良, 袁少雄, 林志文 (2017). 轻小型无人机低空遥感及其在生态学中的应用进展. 应用生态学报, 28, 528-536.]
DOI |
|
[145] |
Szantoi Z, Escobedo F, Abd-Elrahman A, Smith S, Pearlstine L. (2013). Analyzing fine-scale wetland composition using high resolution imagery and texture features. International Journal of Applied Earth Observation and Geoinformation, 23, 204-212.
DOI URL |
[146] |
Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, et al. (2004). Extinction risk from climate change. Nature, 427, 145-148.
DOI URL |
[147] |
Thompson PL, Isbell F, Loreau M, O’Connor MI, Gonzalez A. (2018). The strength of the biodiversity: ecosystem function relationship depends on spatial scale. Proceedings of the Royal Society B: Biological Sciences, 285, 20180038. DOI: 10.1098/rspb.2018.0038.
DOI |
[148] |
Tilman D. (2000). Causes, consequences and ethics of biodiversity. Nature, 405, 208-211.
DOI URL |
[149] |
Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Marcantonio M, Ricotta C, Tonon G. (2019). Estimating tree species diversity from space in an alpine conifer forest: the Rao’s Q diversity index meets the spectral variation hypothesis. Ecological Informatics, 52, 26-34.
DOI |
[150] |
Townsend PA, Serbin SP, Kruger EL, Gamon JA. (2013). Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data. Proceedings of the National Academy of Sciences of the United States of America, 110, E1074. DOI: 10.1073/pnas.1300952110.
DOI |
[151] |
Turner W, Spector S, Gardiner N, Fladeland M, Sterling E, Steininger M. (2003). Remote sensing for biodiversity science and conservation. Trends in Ecology & Evolution, 18, 306-314.
DOI URL |
[152] |
Ustin SL, Gamon JA. (2010). Remote sensing of plant functional types. New Phytologist, 186, 795-816.
DOI PMID |
[153] | Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, Gamon JA, Zarco-Tejada P. (2009). Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113, S67-S77. |
[154] |
Vaglio Laurin G, Chan JCW, Chen Q, Lindsell JA, Coomes DA, Guerriero L, Del Frate F, Miglietta F, Valentini R. (2014). Biodiversity mapping in a tropical west african forest with airborne hyperspectral data. PLOS ONE, 9 e97910. DOI: 10.1371/journal.pone.0105032.
DOI |
[155] |
Villoslada M, Bergamo TF, Ward RD, Burnside NG, Joyce CB, Bunce RGH, Sepp K. (2020). Fine scale plant community assessment in coastal meadows using UAV based multispectral data. Ecological Indicators, 111, 105979. DOI: 10.1016/j.ecolind.2019.105979.
DOI |
[156] |
Waite CE, van der Heijden GMF, Field R, Boyd DS. (2019). A view from above: unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies. Journal of Applied Ecology, 56, 902-912.
DOI URL |
[157] | Wake DB, Vredenburg VT. (2008). Are we in the midst of the sixth mass extinction? A view from the world of amphibians. Proceedings of the National Academy of Sciences of the United States of America, 105, 11466-11473. |
[158] |
Wallis CIB, Homeier J, Peña J, Brandl R, Farwig N, Bendix J. (2019). Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sensing of Environment, 225, 77-92.
DOI URL |
[159] |
Wang DZ, Qiu PH, Wan B, Cao ZX, Zhang QF. (2022). Mapping α- and β-diversity of mangrove forests with multispectral and hyperspectral images. Remote Sensing of Environment, 275, 113021. DOI: 10.1016/j.rse.2022.113021.
DOI |
[160] |
Wang L, Dronova I, Gong P, Yang WB, Li YR, Liu Q. (2012). A new time series vegetation-water index of phenological-hydrological trait across species and functional types for Poyang Lake wetland ecosystem. Remote Sensing of Environment, 125, 49-63.
DOI URL |
[161] |
Wang R, Gamon JA. (2019). Remote sensing of terrestrial plant biodiversity. Remote Sensing of Environment, 231, 111218. DOI: 10.1016/j.rse.2019.111218.
DOI |
[162] |
Wang R, Gamon JA, Emmerton CA, Li H, Nestola E, Pastorello GZ, Menzer O. (2016a). Integrated analysis of productivity and biodiversity in a southern Alberta prairie. Remote Sensing, 8, 214. DOI: 10.3390/rs8030214.
DOI |
[163] |
Wang R, Gamon JA, Montgomery RA, Townsend PA, Zygielbaum AI, Bitan K, Tilman D, Cavender-Bares J. (2016b). Seasonal variation in the NDVI-species richness relationship in a prairie grassland experiment (cedar creek). Remote Sensing, 8, 128. DOI: 10.3390/rs8020128.
DOI |
[164] |
Wang R, Gamon JA, Schweiger AK, Cavender-Bares J, Townsend PA, Zygielbaum AI, Kothari S. (2018). Influence of species richness, evenness, and composition on optical diversity: a simulation study. Remote Sensing of Environment, 211, 218-228.
DOI URL |
[165] |
Wang Z, Chlus A, Geygan R, Ye Z, Zheng T, Singh A, Couture JJ, Cavender-Bares J, Kruger EL, Townsend PA. (2020). Foliar functional traits from imaging spectroscopy across biomes in eastern North America. New Phytologist, 228, 494-511.
DOI URL |
[166] | Williams LJ, Cavender-Bares J, Townsend PA, Couture JJ, Wang ZH, Stefanski A, Messier C, Reich PB. (2021). Remote spectral detection of biodiversity effects on forest biomass. Nature Ecology & Evolution, 5, 46-54. |
[167] |
Wu YH, Hu BL, Gao XH, Zhou AA. (2018). Adaptive hyperspectral image classification using region-growing techniques. Optics and Precision Engineering, 26, 426-434.
DOI URL |
[168] | Xi XF, Zhou GD. (2016). A survey on deep learning for natural language processing. Acta Automatica Sinica, 42, 1445-1465. |
[奚雪峰, 周国栋 (2016). 面向自然语言处理的深度学习研究. 自动化学报, 42, 1445-1465.] | |
[169] |
Xi YB, Ren CY, Wang ZM, Wei SQ, Bai JL, Zhang B, Xiang HX, Chen L. (2019). Mapping tree species composition using OHS-1 hyperspectral data and deep learning algorithms in Changbai Mountains, Northeast China. Forests, 10, 818. DOI: 10.3390/f10090818.
DOI |
[170] | Xie WJ, Huang K, Li RP, Sun H, Hu JJ, Huang HG. (2015). Applying high-resolution satellite images to estimate tree diversity of mixed broadleaf-Korean pine forest. Journal of Beijing Forestry University, 37(3), 20-26. |
[解潍嘉, 黄侃, 李瑞平, 孙浩, 扈晶晶, 黄华国 (2015). 应用高分辨率卫星数据估算阔叶红松林乔木多样性. 北京林业大学学报, 37(3), 20-26.] | |
[171] |
Xu C, Zeng Y, Zheng ZJ, Zhao D, Liu WJ, Ma ZH, Wu BF. (2022). Assessing the impact of soil on species diversity estimation based on UAV imaging spectroscopy in a natural alpine steppe. Remote Sensing, 14, 671. DOI: 10.3390/rs14030671.
DOI |
[172] |
Xu Y, Zhang CL, Jiang RJ, Wang ZF, Zhu MC, Shen GC. (2021). UAV-based hyperspectral images and monitoring of canopy tree diversity. Biodiversity Science, 29, 647-660.
DOI |
[徐岩, 张聪伶, 降瑞娇, 王子斐, 朱梦晨, 沈国春 (2021). 无人机高光谱影像与冠层树种多样性监测. 生物多样性, 29, 647-660.]
DOI |
|
[173] |
Yan ZB, Guo ZF, Serbin SP, Song GQ, Zhao YY, Chen Y, Wu SB, Wang J, Wang X, Li J, Wang B, Wu YT, Su YJ, Wang H, Rogers A, et al. (2021). Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types. New Phytologist, 232, 134-147.
DOI URL |
[174] |
Yang GJ, Liu JG, Zhao CJ, Li ZH, Huang YB, Yu HY, Xu B, Yang XD, Zhu DM, Zhang XY, Zhang RY, Feng HK, Zhao XQ, Li ZH, Li HL, Yang H. (2017). Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Frontiers in Plant Science, 8, 1111. DOI: 10.3389/fpls.2017.01111.
DOI |
[175] |
Yang QC, Wang LH, Huang JL, Lu LJ, Li Y, Du Y, Ling F. (2022). Mapping plant diversity based on combined SENTINEL-1/2 data—Opportunities for subtropical mountainous forests. Remote Sensing, 14, 492. DOI: 10.3390/rs14030492.
DOI |
[176] |
Yi LN, Xu X, Zhang GF, Ming X, Guo WJ, Li SC, Sha LY. (2019). Light and small UAV hyperspectral image mosaicking. Spectroscopy and Spectral Analysis, 39, 1885-1891.
DOI |
[易俐娜, 许筱, 张桂峰, 明星, 郭文记, 李少聪, 沙灵玉 (2019). 轻小型无人机高光谱影像拼接研究. 光谱学与光谱分析, 39, 1885-1891.] | |
[177] |
Zhang JC, Wang CD, Yuan L, Liu P, Zhang Y, Wu KH. (2020). Construction of a plant spectral library based on an optimised feature selection method. Biosystems Engineering, 195, 1-16.
DOI URL |
[178] |
Zhang YW, Guo YP, Tang R, Tang ZY. (2022). Progress and trends of application of hyperspectral remote sensing in plant diversity research. National Remote Sensing Bulletin. DOI: 10.11834/jrs.20211120.
DOI |
[张艺伟, 郭焱培, 唐荣, 唐志尧 (2022). 高光谱遥感技术在植物多样性研究的应用:进展与趋势. 遥感学报. DOI: 10.11834/jrs.20211120.]
DOI |
|
[179] |
Zhao YJ, Sun YH, Chen WH, Zhao YP liu XL, Bai YF. (2021a). The potential of mapping grassland plant diversity with the links among spectral diversity, functional trait diversity, and species diversity. Remote Sensing, 13, 3034. DOI: 10.3390/rs13153034.
DOI |
[180] |
Zhao YJ, Sun YH, Lu XM, Zhao XZ, Yang L, Sun ZY, Bai YF. (2021b). Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures. Ecological Indicators, 122, 107267. DOI: 10.1016/j.ecolind.2020.107267.
DOI |
[181] |
Zhao YJ, Zeng Y, Zheng ZJ, Dong WX, Zhao D, Wu BF, Zhao QJ. (2018). Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China. Remote Sensing of Environment, 213, 104-114.
DOI URL |
[182] |
Zheng ZJ, Zeng Y, Schneider FD, Zhao YJ, Zhao D, Schmid B, Schaepman ME, Morsdorf F. (2021). Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest. Remote Sensing of Environment, 252, 112170. DOI: 10.1016/j.rse.2020.112170.
DOI |
[1] | ZHANG Zhong-Yang, SONG Xi-Qiang, REN Ming-Xun, ZHANG Zhe. Ecological functions of vascular epiphytes in habitat construction [J]. Chin J Plant Ecol, 2023, 47(7): 895-911. |
[2] | YANG Jia-Rong, DAI Dong, CHEN Jun-Fang, WU Xian, LIU Xiao-Lin, LIU Yu. Insight into recent studies on the diversity of arbuscular mycorrhizal fungi in shaping plant community assembly and maintaining rare species [J]. Chin J Plant Ecol, 2023, 47(6): 745-755. |
[3] | LI Yao-Qi, WANG Zhi-Heng. Functional biogeography of plants: research progresses and challenges [J]. Chin J Plant Ecol, 2023, 47(2): 145-169. |
[4] | HAO Qing, HUANG Chang. A review of forest aboveground biomass estimation based on remote sensing data [J]. Chin J Plant Ecol, 2023, 47(10): 1356-1374. |
[5] | MA He-Ping, WANG Rui-Hong, QU Xing-Le, YUAN Min, MU Jin-Yong, LI Jin-Hang. Effects of different habitats on the diversity and biomass of ground moss in the southeast Xizang, China [J]. Chin J Plant Ecol, 2022, 46(5): 552-560. |
[6] | LI Xiao-Long, ZHOU Jun, PENG Fei, ZHONG Hong-Tao, Hans LAMBERS. Temporal trends of plant nutrient-acquisition strategies with soil age and their ecological significance [J]. Chin J Plant Ecol, 2021, 45(7): 714-727. |
[7] | SUN Hao-Zhe, WANG Xiang-Ping, ZHANG Shu-Bin, WU Peng, YANG Lei. Abiotic and biotic modulators of litterfall production and its temporal stability during the succession of broad-leaf and Korean pine mixed forest [J]. Chin J Plant Ecol, 2021, 45(6): 594-605. |
[8] | JING Xin, HE Jin-Sheng. Relationship between biodiversity, ecosystem multifunctionality and multiserviceability: literature overview and research advances [J]. Chin J Plant Ecol, 2021, 45(10): 1094-1111. |
[9] | LI Zhou-Yuan, YE Xiao-Zhou, WANG Shao-Peng. Ecosystem stability and its relationship with biodiversity [J]. Chin J Plant Ecol, 2021, 45(10): 1127-1139. |
[10] | LI Song-Song, WANG Ning-Xin, ZHENG Wei, ZHU Ya-Qiong, WANG Xiang, MA Jun, ZHU Jin-Zhong. Comparison of transgressive overyielding effect and plant diversity effects of annual and perennial legume-grass mixtures [J]. Chin J Plant Ecol, 2021, 45(1): 23-37. |
[11] | Hanula TASIKEN, CAI Hui-Ying, JIN Guang-Ze. Effects of canopy structure on productivity in a typical mixed broadleaved-Korean pine forest [J]. Chin J Plant Ecol, 2021, 45(1): 38-50. |
[12] | LIU Ling, FAN Ying-Jie, SONG Xiao-Tong, LI Min, SHAO Xiao-Ming, WANG Xiao-Rui. Bryophyte societies on the fallen logs of Pinus armandii with different decay classes in Sygera Mountains [J]. Chin J Plant Ecol, 2020, 44(8): 842-853. |
[13] | FU Wei, WU Hui, ZHAO Ai-Hua, HAO Zhi-Peng, CHEN Bao-Dong. Ecological impacts of nitrogen deposition on terrestrial ecosystems: research progresses and prospects [J]. Chin J Plant Ecol, 2020, 44(5): 475-493. |
[14] | ZHOU Gui-Yao, ZHOU Ling-Yan, SHAO Jun-Jiong, ZHOU Xu-Hui. Effects of extreme drought on terrestrial ecosystems: review and prospects [J]. Chin J Plant Ecol, 2020, 44(5): 515-525. |
[15] | GUO Qing-Hua, HU Tian-Yu, MA Qin, XU Ke-Xin, YANG Qiu-Li, SUN Qian-Hui, LI Yu-Mei, SU Yan-Jun. Advances for the new remote sensing technology in ecosystem ecology research [J]. Chin J Plant Ecol, 2020, 44(4): 418-435. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
Copyright © 2022 Chinese Journal of Plant Ecology
Tel: 010-62836134, 62836138, E-mail: apes@ibcas.ac.cn, cjpe@ibcas.ac.cn