Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

被引:28
作者
Buters, Todd [1 ]
Belton, David [2 ]
Cross, Adam [1 ]
机构
[1] Curtin Univ, Sch Mol & Life Sci, ARC Ctr Mine Site Restorat, Kent St, Bentley, WA 6102, Australia
[2] Curtin Univ, Sch Earth & Planetary Sci, Spatial Sci, Bentley, WA 6102, Australia
基金
澳大利亚研究理事会;
关键词
ecological restoration; object-based image analysis; rehabilitation; remote sensing; monitoring; INFRARED IMAGERY; RESTORATION; MICROSITE; RECRUITMENT; PERFORMANCE; UAV;
D O I
10.3390/drones3030053
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 45 条
[1]  
[Anonymous], 2004, SER INT PRIM EC REST
[2]   Identifying species from the air: UAVs and the very high resolution challenge for plant conservation [J].
Baena, Susana ;
Moat, Justin ;
Whaley, Oliver ;
Boyd, Doreen S. .
PLOS ONE, 2017, 12 (11)
[3]   Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery [J].
Berni, J. A. J. ;
Zarco-Tejada, P. J. ;
Sepulcre-Canto, G. ;
Fereres, E. ;
Villalobos, F. .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (11) :2380-2388
[4]   Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle [J].
Berni, Jose A. J. ;
Zarco-Tejada, Pablo J. ;
Suarez, Lola ;
Fereres, Elias .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (03) :722-738
[5]   Methodological Ambiguity and Inconsistency Constrain Unmanned Aerial Vehicles as A Silver Bullet for Monitoring Ecological Restoration [J].
Buters, Todd M. ;
Bateman, Philip W. ;
Robinson, Todd ;
Belton, David ;
Dixon, Kingsley W. ;
Cross, Adam T. .
REMOTE SENSING, 2019, 11 (10)
[6]   High-resolution airborne hyperspectral and thermal imagery for early, detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices [J].
Calderon, R. ;
Navas-Cortes, J. A. ;
Lucena, C. ;
Zarco-Tejada, P. J. .
REMOTE SENSING OF ENVIRONMENT, 2013, 139 :231-245
[7]   Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images [J].
Candiago, Sebastian ;
Remondino, Fabio ;
De Giglio, Michaela ;
Dubbini, Marco ;
Gattelli, Mario .
REMOTE SENSING, 2015, 7 (04) :4026-4047
[8]   Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models [J].
Cao, Jingjing ;
Leng, Wanchun ;
Liu, Kai ;
Liu, Lin ;
He, Zhi ;
Zhu, Yuanhui .
REMOTE SENSING, 2018, 10 (01)
[9]   Nitrogen limitation and calcifuge plant strategies constrain the establishment of native vegetation on magnetite mine tailings [J].
Cross, Adam T. ;
Ivanov, Dmitry ;
Stevens, Jason C. ;
Sadler, Rohan ;
Zhong, Hongtao ;
Lambers, Hans ;
Dixon, Kingsley W. .
PLANT AND SOIL, 2021, 461 (1-2) :181-201
[10]   Compromised root development constrains the establishment potential of native plants in unamended alkaline post-mining substrates [J].
Cross, Adam T. ;
Stevens, Jason C. ;
Sadler, Rohan ;
Moreira-Grez, Benjamin ;
Ivanov, Dmitry ;
Zhong, Hongtao ;
Dixon, Kingsley W. ;
Lambers, Hans .
PLANT AND SOIL, 2021, 461 (1-2) :163-179