Mapping Spatial Distribution of Larch Plantations from  Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

Gao, Tian; Zhu, Jiaojun; Zheng, Xunhua; Shang, Guiduo; Huang, Liyan; Wu, Shangrong

doi:10.3390/rs70201702

Cited by 45 publications

(34 citation statements)

References 48 publications

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“…We used the tool called imageRF [81] that can be implemented in an IDL/ENVI environment for the classification of remote sensing images with RF. We fixed the number of decision trees at 1000 to minimize the generalization error [36,82], and just enough not to increase the computational time [34].…”

Section: Random Forest Classifiermentioning

confidence: 99%

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

2016

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Abstract:We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar's test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.

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Section: Random Forest Classifiermentioning

confidence: 99%

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

2016

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“…The mtry parameter was set to the square root of the total number of input features within different feature scenarios (see Table 3), and the ntree parameter was set to a relatively high number (set as 1000) for each feature scenarios to allow for convergence of the Out-Of-Bag (OOB) error statistic since values larger than the default (500) are known to have little influence on the overall classification accuracy [34,51,54]. The RF model was constructed using a set of field data that was randomly divided 70%/30% for training and testing, and examined by using 10-fold cross-validation to optimize classification performance.…”

Section: Random Forest Classificationmentioning

confidence: 99%

“…Mean blue, green, red and near-infrared spectral features were computed from the values of all pixels forming an object, showing information related to leaf pigment and vegetation status [48,49]. The textural features related to crop structure, soil background and planting patterns, including gray-level co-occurrence matrix (GLCM) correlation, dissimilarity and entropy, were calculated from GF-1 WFV bands (blue band to near infrared band) [50,51]. The texture features were calculated within the object.…”

Section: Feature Extractionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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“…The NN and RF classifications were completed using the Neural Network Toolbox in Matlab R2015b [65] and R 3.2.3 software, respectively. The detailed procedure of RF classifications using R software is listed in our previous studies [43,66]. Notes: a for detailed information refer to [64]; b for detailed information refer to [65]; c for detailed information refer to [57].…”

Section: Ravgmentioning

confidence: 99%

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

Deng

Katoh

et al. 2016

Remote Sensing

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Individual tree delineation using remotely sensed data plays a very important role in precision forestry because it can provide detailed forest information on a large scale, which is required by forest managers. This study aimed to evaluate the utility of airborne laser scanning (ALS) data for individual tree detection and species classification in Japanese coniferous forests with a high canopy density. Tree crowns in the study area were first delineated by the individual tree detection approach using a canopy height model (CHM) derived from the ALS data. Then, the detected tree crowns were classified into four classes-Pinus densiflora, Chamaecyparis obtusa, Larix kaempferi, and broadleaved trees-using a tree crown-based classification approach with different combinations of 23 features derived from the ALS data and true-color (red-green-blue-RGB) orthoimages. To determine the best combination of features for species classification, several loops were performed using a forward iteration method. Additionally, several classification algorithms were compared in the present study. The results of this study indicate that the combination of the RGB images with laser intensity, convex hull area, convex hull point volume, shape index, crown area, and crown height features produced the highest classification accuracy of 90.8% with the use of the quadratic support vector machines (QSVM) classifier. Compared to only using the spectral characteristics of the orthophotos, the overall accuracy was improved by 14.1%, 9.4%, and 8.8% with the best combination of features when using the QSVM, neural network (NN), and random forest (RF) approaches, respectively. In terms of different classification algorithms, the findings of our study recommend the QSVM approach rather than NNs and RFs to classify the tree species in the study area. However, these classification approaches should be further tested in other forests using different data. This study demonstrates that the synergy of the ALS data and RGB images could be a promising approach to improve species classifications.

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Mapping Spatial Distribution of Larch Plantations from Multi-Seasonal Landsat-8 OLI Imagery and Multi-Scale Textures Using Random Forests

Cited by 45 publications

References 48 publications

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Comparison of Tree Species Classifications at the Individual Tree Level by Combining ALS Data and RGB Images Using Different Algorithms

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