Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

doi:10.1016/j.jag.2017.11.006

Cited by 40 publications

(38 citation statements)

References 24 publications

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“…Different machine learning classification methods, such as support vector machines (SVM), artificial neural networks (ANNs), linear discriminant analysis (LDA), and random forest (RF) [16,25,29,[32][33][34][35], have been used for early detection of plant diseases based on remote sensing data. Random forest is a flexible and powerful machine learning classifier [36] that has been utilized in the classification of remote-sensing-based information [29,[37][38][39][40][41]. The random forest classifier can handle huge, multidimensional datasets and performs both classification and regression functions without over-fitting the model [36,42].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

et al. 2020

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Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.The pathogen starts infecting roots during early soybean growth stages [9,10] and causes root rot and poor root development [3]. Root infections are favored by cool, wet soil environments [11,12]. Foliar symptoms of interveinal chlorosis (yellowing between leaf veins) and necrosis (tissue browning following cell death) typically appear during reproductive stages [13] and cause premature defoliation and senescence under severe disease pressure [14]. The initial foliar symptoms show only as yellow traces on lower leaves, which makes the disease difficult to detect at early stages. Abundant soil moisture favors SDS foliar symptom expression [12,15], whereas infected plants may not develop foliar symptoms under dry field conditions. Disease distribution within a field is limited by the spatial distribution of the pathogen at the beginning of the growing season.Scouting for SDS foliar symptoms in the field is made difficult by the relatively late onset of visible foliar symptom expression, which often occurs after the soybean canopy has closed, and by the patchy distribution of SDS in soybean fields. Scouting for symptomatic plants is time-consuming, and confirmation of Fv infection requires destructive sampling [16]. Therefore, a more effective method for monitoring and quantifying the distribution of SDS in the field is needed.Early detection of plant diseases through remote sensing can be di...

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Section: Introductionmentioning

confidence: 99%

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

et al. 2020

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“…In contrast, object-based information extraction can make full use of the spatial information, geometric structure, and texture information of the image, and gradually highlight its advantages in the process of information extraction of high spatial resolution remote sensing images. The object-based approach firstly divides the land into several parts in terms of influence, and then takes the divided land as the smallest classification unit on the basis of obtaining relatively homogeneous land blocks [27]. For example, in prior research, blocks after multi-scale segmentation were taken as basic classification units, and spectral, texture, shape, and other variables were constructed [86].…”

Section: Prospects Of Object-based Approaches Compared To Pixel-basedmentioning

confidence: 99%

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Chen

Jia

et al. 2019

Remote Sensing

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Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.

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“…The soil classification has been done by the researchers using different approaches like i) environmental conditions i.e. temperature, moisture, humidity and pH, were considered, ii) fertility of soil [2], [8], [9] was used as the basic measure for the soil classification, iii) mode of formation of soil iv) structure/texture of soil [5], [10], [11] v) regional or land cover basis [12]- [15], [14], [10], [16], [17] or based on vi) existence of chemicals in the soil [8], [9] were used for the soil classification.…”

Section: B Classification Of Soilmentioning

confidence: 99%

Knowledge based Soil Classification Towards Relevant Crop Production

Haider¹,

Durrani²,

Rehman³

et al. 2019

IJACSA

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Pakistan's economy is strongly associated with agriculture sector. For a country having 25 % of GDP contributed through agriculture, there is a need to modernize the agriculture by acclimatizing contemporary approaches. Unfortunately, it has become a common trend among farmers to cultivate crops, being used in food items or which can easily be sold out in the market without using knowledge about the suitability or relevancy of crops to the soil environment. Consequently, the farmers face financial losses. Many researchers have proposed soil classification methods for various soils related researches, but they have very little contribution towards guidance of the farmers to select most suitable crops for cultivation at a particular soil type. Without the use of technology and computer-assisted approaches, the process of classifying soil environments could not help the farmers in taking decisions regarding appropriate crop selection in their respective fields. In this paper, an effective knowledge-oriented approach for soil classification in Pakistan has been presented using crowd sourced data obtained from 1557 users regarding 103 agricultural zones. The data were also obtained from AIMS (Govt. of Punjab) and Ministry of National Food Security & Research. In this work, random forest classifier has been used for processing and predicting complex tiered relationship among soil types belonging to agricultural zones and major suitable crops for improving yield production. The proposed model helps in computing the degree of relevancy of crop to agricultural region that help former selecting suitable crops for their cultivated lands.489 | P a g e www.ijacsa.thesai.org cultivation of crops on the soil types. Experimental setup is given in Section IV, which is interlinked with Sections V and VI. These sections cover the implementation of the algorithm and discusses the results of different experiments respectively.

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Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

Cited by 40 publications

References 24 publications

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Knowledge based Soil Classification Towards Relevant Crop Production

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