Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data

Tatsumi, Kenichi; Yamashiki, Yosuke; Torres, Miguel Angel Canales; Taipe, Cayo Leonidas Ramos

doi:10.1016/j.compag.2015.05.001

Cited by 213 publications

(104 citation statements)

References 45 publications

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“…Recently developed nonparametric machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), provide effective tools to identify different land cover classes, as they are not constrained by the assumption that the input parameters are normally distributed [34][35][36]. RF classifier has been given increasing attention with regards to crop mapping [37][38][39].…”

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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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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“…Once the trees are created, classification results are obtained by majority voting. RF has reached around 85% OA in crop type classification using a multi-spectral time series of RapidEye images [62], and higher than 80% for a time series of Landsat7 images in homogeneous regions [13]. RF has two user-defined parameters: the number of trees and the number of features available to build each decision tree.…”

Section: Vegetation Index (Vi) Formulamentioning

confidence: 99%

“…Remote sensing image classification is a convenient approach for producing these maps due to advantages in terms of cost, revisit time, and spatial coverage [10]. Indeed, remotely sensed image classification has been successfully applied to produce crop maps in homogeneous areas [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Spatial features benefit crop discrimination [19], especially in heterogeneous areas where high local variance is more relevant when very high spatial resolution images are applied [20,21]. Regarding temporal features, multi-temporal spectral indices have been exploited in crop identification because they provide information about the seasonal variation in surface reflectance caused by crop phenology [13,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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“…These studies were conducted using data from multiple sensors across many spatial, spectral, radiometric, and temporal resolutions for both irrigated and rainfed crops (Biggs et al 2006;Friedl et al 2010;Funk and Brown 2009;Gumma et al 2011;Loveland et al 2000;Ozdogan and Woodcock 2006;Pervez, Budde, and Rowland 2014;Pittman et al 2010;Teluguntla et al 2015a;Thenkabail et al 2009aThenkabail et al , 2009bWardlow, Egbert, and Kastens 2007;Xiao et al 2006;Yu et al 2013). These studies consider an ensemble of methods that include: (a) decision tree algorithms (De Fries et al 1998;Friedl and Brodley 1997;Ozdogan and Gutman 2008;Waldner, Canto, and Defourny 2015); (b) the random forest algorithm (Gislason, Benediktsson, and Sveinsson 2006;Tatsumi et al 2015); (c) Tassel cap brightness-greenness-wetness (Cohen and Goward 2004;Crist and Cicone 1984;Gutman et al 2008;Masek et al 2008); (d) space-time spiral curves and change vector analysis (Thenkabail, Schull, and Turral 2005); (e) phenological approaches Gumma et al 2011;Loveland et al 2000;Pan et al 2015;Teluguntla et al 2015a;Xiao et al 2006;Zhou et al 2016); (f) Hierarchical Image Segmentation (HSEG) software or HSeG (Tilton et al 2012); (g) support vector machines (Mountrakis, Im, and Ogole 2011;Shao and Lunetta 2012); (h) spectral matching techniques (Thenkabail et al 2007a); (i) pixel-and object-based methods usin...…”

Section: Introduction and Rationalementioning

confidence: 99%

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

Teluguntla

Thenkabail

Xiong

et al. 2017

International Journal of Digital Earth

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Mapping croplands, including fallow areas, are an important measure to determine the quantity of food that is produced, where they are produced, and when they are produced (e.g. seasonality). Furthermore, croplands are known as water guzzlers by consuming anywhere between 70% and 90% of all human water use globally. Given these facts and the increase in global population to nearly 10 billion by the year 2050, the need for routine, rapid, and automated cropland mapping year-after-year and/or season-after-season is of great importance. The overarching goal of this study was to generate standard and routine cropland products, year-after-year, over very large areas through the use of two novel methods: (a) quantitative spectral matching techniques (QSMTs) applied at continental level and (b) rulebased Automated Cropland Classification Algorithm (ACCA) with the ability to hind-cast, now-cast, and future-cast. Australia was chosen for the study given its extensive croplands, rich history of agriculture, and yet nonexistent routine yearly generated cropland products using multitemporal remote sensing. This research produced three distinct cropland products using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m normalized difference vegetation index 16-day composite time-series data for 16 years: 2000 through 2015. The products consisted of: (1) cropland extent/areas versus cropland fallow areas, (2) irrigated versus rainfed croplands, and (3) cropping intensities: single, double, and continuous cropping. An accurate reference cropland product (RCP) for the year 2014 (RCP2014) produced using QSMT was used as a knowledge base to train and develop the ACCA algorithm that was then applied to the MODIS time-series data for the years 2000-2015. A comparison between the ACCA-derived cropland products

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Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data

Cited by 213 publications

References 45 publications

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

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

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

Spectral matching techniques (SMTs) and automated cropland classification algorithms (ACCAs) for mapping croplands of Australia using MODIS 250-m time-series (2000–2015) data

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