Random Forest and Objected-Based Classification for Forest Pest Extraction From Uav Aerial Imagery

Hu, Xiangyun

doi:10.5194/isprsarchives-xli-b1-1093-2016

Cited by 18 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unmanned aerial vehicle (UAV) imaging is a rapidly growing technology in recent years and has been widely applied in crop monitoring due to its high efficiency, high spatial and temporal resolution, low cost and easy customization [10]. Since 2010, high-throughput phenotyping by UAV imaging has been introduced to precision agriculture [11] in a range of applications, such as the detection of abiotic stress [12], nutrient deficiency [13] and biotic stress [14, 15], weed management [16], plant growth monitoring [17–19] and yield prediction [20, 21]. In previous studies, low-altitude UAV equipped with a RGB camera was shown to be an effective method for counting rapeseed seedlings [22] and estimating canopy cover of cotton, sorghum and sugarcane [9, 23].…”

Section: Introductionmentioning

confidence: 99%

The estimation of crop emergence in potatoes by UAV RGB imagery

Xu²,

Han

et al. 2019

Plant Methods

View full text Add to dashboard Cite

BackgroundCrop emergence and canopy cover are important physiological traits for potato (Solanum tuberosum L.) cultivar evaluation and nutrients management. They play important roles in variety screening, field management and yield prediction. Traditional manual assessment of these traits is not only laborious but often subjective. ResultsIn this study, semi-automated image analysis software was developed to estimate crop emergence from high-resolution RGB ortho-images captured from an unmanned aerial vehicle (UAV). Potato plant objects were extracted from bare soil using Excess Green Index and Otsu thresholding methods. Six morphological features were calculated from the images to be variables of a Random Forest classifier for estimating the number of potato plants at emergence stage. The outputs were then used to estimate crop emergence in three field experiments that were designed to investigate the effects of cultivars, levels of potassium (K) fertiliser input, and new compound fertilisers on potato growth. The results indicated that RGB UAV image analysis can accurately estimate potato crop emergence rate in comparison to manual assessment, with correlation coefficient () of 0.96 and provide an efficient tool to evaluate emergence uniformity.ConclusionsThe proposed UAV image analysis method is a promising tool for use as a high throughput phenotyping method for assessing potato crop development at emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency.

show abstract

Section: Introductionmentioning

confidence: 99%

The estimation of crop emergence in potatoes by UAV RGB imagery

Xu²,

Han

et al. 2019

Plant Methods

View full text Add to dashboard Cite

show abstract

“…The multiple decision trees of the RF are trained on a bootstrap sample of the original training data. At each node of every decision tree, one of a randomly selected subset of input parameters is chosen as the best split and used for node splitting [66]. Variable selection is significant for interpretation and prediction, especially for multidimensional datasets.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach

Fallatah¹,

Jones²,

Mitchell³

2018

Preprint

View full text Add to dashboard Cite

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

show abstract

“…Various sensors can be mounted on UAVs, such as thermal infrared cameras [11], hyperspectral cameras [12], and LiDAR [13]. UAVs have been used for forest biomass estimation [14], tree height measurement [15], pest and disease monitoring [16,17], disaster assessment [18][19][20], fire surveillance and prevention [21], etc. UAV remote sensing data have high image and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry

Zhang

Gao

Jiang

et al. 2022

Drones

View full text Add to dashboard Cite

Traditional forest monitoring has been mainly performed with images or orthoimages from aircraft or satellites. In recent years, the availability of high-resolution 3D data has made it possible to obtain accurate information on canopy size, which has made the topic of canopy 3D growth monitoring timely. In this paper, forest growth pattern was studied based on a canopy point cloud (PC) reconstructed from UAV aerial photogrammetry at a daily interval for a year. Growth curves were acquired based on the canopy 3D area (3DA) calculated from a triangulated 3D mesh. Methods for canopy coverage area (CA), forest coverage rate, and leaf area index (LAI) were proposed and tested. Three spectral vegetation indices, excess green index (ExG), a combination of green indices (COM), and an excess red union excess green index (ExGUExR) were used for the segmentation of trees. The results showed that (1) vegetation areas extracted by ExGUExR were more complete than those extracted by the other two indices; (2) logistic fitting of 3DA and CA yielded S-shaped growth curves, all with correlation R2 > 0.92; (3) 3DA curves represented the growth pattern more accurately than CA curves. Measurement errors and applicability are discussed. In summary, the UAV aerial photogrammetry method was successfully used for daily monitoring and annual growth trend description.

show abstract

Random Forest and Objected-Based Classification for Forest Pest Extraction From Uav Aerial Imagery

Cited by 18 publications

References 16 publications

The estimation of crop emergence in potatoes by UAV RGB imagery

The estimation of crop emergence in potatoes by UAV RGB imagery

Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach

High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry

Contact Info

Product

Resources

About