Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

2017 ◽  
Vol 11 (8) ◽  
pp. 783-802 ◽  
Author(s):  
Jiantao Liu ◽  
Quanlong Feng ◽  
Jianhua Gong ◽  
Jieping Zhou ◽  
Jianming Liang ◽  
...  
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
Random Forest Classifier ◽  
Sensor Data ◽  
Multi Temporal

scholarly journals Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm

2020 ◽  
Vol 12 (23) ◽  
pp. 3933
Author(s):  
Anggun Tridawati ◽  
Ketut Wikantika ◽  
Tri Muji Susantoro ◽  
Agung Budi Harto ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Random Forest ◽  
Remote Sensing Data ◽  
Agroforestry System ◽  
Classification Model ◽  
Sensor Data ◽  
Kappa Statistics ◽  
Random Forest Algorithm ◽  
Coffee Plantations ◽  
Multi Temporal ◽  
Sentinel 2

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.

scholarly journals Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

2020 ◽  
Vol 12 (12) ◽  
pp. 2065 ◽  
Author(s):  
Feng Xu ◽  
Zhaofu Li ◽  
Shuyu Zhang ◽  
Naitao Huang ◽  
Zongyao Quan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Google Earth ◽  
Shandong Province ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Wheat Development ◽  
Development Phases ◽  
Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

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

2018 ◽  
Vol 8 (8) ◽  
pp. 1216 ◽  
Author(s):  
Mousa Abad ◽  
Ali Abkar ◽  
Barat Mojaradi
Keyword(s):  
Missing Data ◽  
Random Forest ◽  
Winter Wheat ◽  
Temporal Resolution ◽  
Decision Makers ◽  
Wheat Crop ◽  
Early Season ◽  
Temporal Gradient ◽  
Spectral Bands ◽  
Multi Temporal

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%.

scholarly journals On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

2019 ◽  
Vol 11 (7) ◽  
pp. 776 ◽  
Author(s):  
Qinghua Xie ◽  
Jinfei Wang ◽  
Chunhua Liao ◽  
Jiali Shang ◽  
Juan Lopez-Sanchez ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Supervised Classification ◽  
Alpha Angle ◽  
Morphological Characteristics ◽  
Random Forest Classifier ◽  
Decomposition Approach ◽  
Multi Temporal ◽  
Crop Classification ◽  
Two Parameters

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

scholarly journals Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

2022 ◽  
Vol 8 (2) ◽  
pp. 75-84
Author(s):  
Nurwita Mustika Sari ◽  
R. Rokhmatuloh ◽  
Masita Dwi Mandini Manessa
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Activities ◽  
Research Area ◽  
Random Forest Classifier ◽  
Sustainable Environment ◽  
Multi Temporal ◽  
Sentinel 2 ◽  
Dynamics Of Vegetation

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier

2017 ◽  
Vol 33 (10) ◽  
pp. 1017-1035 ◽  
Author(s):  
Huanxue Zhang ◽  
Qiangzi Li ◽  
Jiangui Liu ◽  
Xin Du ◽  
Taifeng Dong ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Textural Features ◽  
Object Based ◽  
Multi Temporal ◽  
Crop Classification ◽  
Spot 5

scholarly journals Random Forest Classifier for Extracting Water bodies from Pansharpened Image to Detect Surface Water Changes

2019 ◽  
Vol 9 (1) ◽  
Keyword(s):  
Surface Water ◽  
Random Forest ◽  
Landsat Imagery ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Commission Error ◽  
Landsat Images ◽  
Classification Techniques ◽  
Multi Temporal ◽  
Active Research

Change detection from time series multispectral Landsat imagery has been an active research in remote sensing for several years to monitor the ecosystem, environment, climate and so on. This study is focused on detecting the changes in surface water by the integration of fusion and image classification techniques in multi-temporal multispectral Landsat images. The panchromatic band and the multispectral band of Landsat OLI and TM images respectively, were fused using undecimated wavelet transform to get the pan-sharpened image. Then classification techniques like Maximum Likelihood, Support Vector Machine, Artificial Neural Network and Random Forest were employed for extracting the water pixels and changed pixels. The performances of these classification techniques were analyzed based on metrics such as overall error, commission error, precision, recall, overall accuracy, kappa coefficients and the results show that the application of random forest classifier on pansharpened image outperforms in extracting the water pixels and also in highlighting the changes with maximum accuracy

scholarly journals From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

2021 ◽  
Vol 13 (9) ◽  
pp. 1700
Author(s):  
Dang Hung Bui ◽  
László Mucsi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Decision Rules ◽  
Random Forest Classifier ◽  
Landsat Images ◽  
Object Based ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Land Cover Maps

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier
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