Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Fletcher, Reginald S.

doi:10.4236/ajps.2016.715193

Cited by 22 publications

(23 citation statements)

References 39 publications

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“…The data on the assessment of the spectral predictors used in the MY models indicates a paramount contribution of VIs in the prediction of crops as compared to the individual bands. A similar conclusion was drawn by Fletcher [59] in the discrimination of soybean and three weed species. The highest contribution of VIs in the prediction of the crops was expected since their calculation involved two or more bands and as a result, used the unique spectral characteristics of the individual bands to produce a single layer which captured the different phenological dynamics among the crops.…”

Section: Discussionsupporting

confidence: 85%

“…The highest contribution of VIs in the prediction of the crops was expected since their calculation involved two or more bands and as a result, used the unique spectral characteristics of the individual bands to produce a single layer which captured the different phenological dynamics among the crops. Vegetation indices, which are based on SWIR and NIR spectral bands, are known for their contributions to plant separation [59]. Therefore, despite the contributions of the other predictor variables, the late summer NDMI is ranked as the topmost dominant predictor variables for almost all years, followed by early summer NDVI.…”

Section: Discussionmentioning

confidence: 99%

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Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Kyere¹,

Astor²,

Graß³

et al. 2019

Agronomy

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The spatial distribution and location of crops are necessary information for agricultural planning. The free availability of optical satellites such as Landsat offers an opportunity to obtain this key information. Crop type mapping using satellite data is challenged by its reliance on ground truth data. The Integrated Administration and Control System (IACS) data, submitted by farmers in Europe for subsidy payments, provide a solution to the issue of periodic field data collection. The present study tested the performance of the IACS data in the development of a generalized predictive crop type model, which is independent of the calibration year. Using the IACS polygons as objects, the mean spectral information based on four different vegetation indices and six Landsat bands were extracted for each crop type and used as predictors in a random forest model. Two modelling methods called single-year (SY) and multiple-year (MY) calibration were tested to find out their performance in the prediction of grassland, maize, summer, and winter crops. The independent validation of SY and MY resulted in a mean overall accuracy of 71.5% and 77.3%, respectively. The field-based approach of calibration used in this study dealt with the ‘salt and pepper’ effects of the pixel-based approach.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Kyere¹,

Astor²,

Graß³

et al. 2019

Agronomy

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“…RF classifier has been given increasing attention with regards to crop mapping [37][38][39]. The RF classifier has been proven to be stable and relatively efficient to yield overall accuracy levels that are either comparable to or better than other classifiers such as decision trees, neural networks and SVM [40].…”

Section: Introductionmentioning

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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“…However, the VI information was not used in their study. Fletcher [23] introduced vegetation indices as new features in the random forest for the classification of soybeans and weeds and confirmed the positive effect of vegetation indices, and the overall accuracy of 90.8% and the kappa coefficient of 0.878 were obtained using the image (30 June 2014). Zheng et al [14] classified various types of crops by using a support vector machine classifier using the NDVI time series data that was extracted from Landsat images.…”

Section: Introductionmentioning

confidence: 71%

“…The effect of these factors also depends on the choice of the classifier [21]. Some studies have been done to address some of these issues [22][23][24][25]. For example, the optimal number and dates of images for land cover classification are identified with feature importance obtained by RF classifier on remote sensing time-series [26].…”

Section: Introductionmentioning

confidence: 99%

Effect of the Temporal Gradient of Vegetation Indices on Early-Season Wheat Classification Using the Random Forest Classifier

2018

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Early-season area estimation of the winter wheat crop as a strategic product is important for decision-makers. Multi-temporal images are the best tool to measure early-season winter wheat crops, but there are issues with classification. Classification of multi-temporal images is affected by factors such as training sample size, temporal resolution, vegetation index (VI) type, temporal gradient of spectral bands and VIs, classifiers, and values missed under cloudy conditions. This study addresses the effect of the temporal resolution and VIs, along with the spectral and VIs gradient on the random forest (RF) classifier when missing data occurs in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, a study area is selected on an overlapping area between two Landsat Data Continuity Mission (LDCM) paths. In the proposed method, the missing data from cloudy pixels are retrieved using the average of the k-nearest cloudless pixels in the feature space. Next, multi-temporal image analysis is performed by considering different scenarios provided by decision-makers for the desired crop types, which should be extracted early in the season in the study areas. The classification results obtained by RF improved by 2.2% when the temporally-missing data were retrieved using the proposed method. Moreover, the experimental results demonstrated that when the temporal resolution of Landsat-8 is increased to one week, the classification task can be conducted earlier with slightly better overall accuracy (OA) and kappa values. The effect of incorporating VIs along with the temporal gradients of spectral bands and VIs into the RF classifier improved the OA by 3.1% and the kappa value by 6.6%, on average. The results show that if only three optimum images from seasonal changes in crops are available, the temporal gradient of the VIs and spectral bands becomes the primary tool available for discriminating wheat from barley. The results also showed that if wheat and barley are considered as single class versus other classes, with the use of images associated with 162 and 163 paths, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and kappa coefficient of 93.5%.

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Using Vegetation Indices as Input into Random Forest for Soybean and Weed Classification

Cited by 22 publications

References 39 publications

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data

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

Effect of the Temporal Gradient of Vegetation Indices on Early-Season Wheat Classification Using the Random Forest Classifier

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