scholarly journals Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

2015 ◽  
Vol 7 (12) ◽  
pp. 16091-16107 ◽  
Author(s):  
Qingting Li ◽  
Cuizhen Wang ◽  
Bing Zhang ◽  
Linlin Lu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Object Based ◽  
Crop Classification

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Automatic Crop Classification in Northeastern China by Improved Nonlinear Dimensionality Reduction for Satellite Image Time Series

2020 ◽  
Vol 12 (17) ◽  
pp. 2726 ◽  
Author(s):  
Yongguang Zhai ◽  
Nan Wang ◽  
Lifu Zhang ◽  
Lei Hao ◽  
Caihong Hao
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Time Series Data ◽  
Satellite Image ◽  
Series Data ◽  
Nonlinear Dimensionality Reduction ◽  
Distribution Map ◽  
Dimensionality Reduction Technique ◽  
Classification Tasks ◽  
Crop Classification

Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract phenological features to identify crops. It is still a challenge to automatically obtain meaningful features from time-series data for crop classification. In this study, we developed an automated method based on satellite image time series to map the spatial distribution of three major crops including maize, rice, and soybean in northeastern China. The core method used is the nonlinear dimensionality reduction technique. However, the existing nonlinear dimensionality reduction technique cannot handle missing data, and it is not designed for subsequent classification tasks. Therefore, the nonlinear dimensionality reduction algorithm Landmark–Isometric feature mapping (L–ISOMAP) is improved. The advantage of the improved L–ISOMAP is that it does not need to reconstruct time series for missing data, and it can automatically obtain meaningful featured metrics for classification. The improved L–ISOMAP was applied to Landsat 8 full-band time-series data during the crop-growing season in the three northeastern provinces of China; then, the dimensionality reduction bands were inputted into a random forest classifier to complete a crop distribution map. The results show that the area of crops mapped is consistent with official statistics. The 2015 crop distribution map was evaluated through the collected reference dataset, and the overall classification accuracy and Kappa index were 83.68% and 0.7519, respectively. The geographical characteristics of major crops in three provinces in northeast China were analyzed. This study demonstrated that the improved L–ISOMAP method can be used to automatically extract features for crop classification. For future work, there is great potential for applying automatic mapping algorithms to other data or classification tasks.

scholarly journals Mapping Annual Land Use and Land Cover Changes in the Yangtze Estuary Region Using an Object-Based Classification Framework and Landsat Time Series Data

2020 ◽  
Vol 12 (2) ◽  
pp. 659
Author(s):  
Jinquan Ai ◽  
Chao Zhang ◽  
Lijuan Chen ◽  
Dajun Li
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Regional Scale ◽  
Series Data ◽  
Yangtze River Estuary ◽  
Classification Framework ◽  
Object Based

A system understanding of the patterns, causes, and trends of long-term land use and land cover (LULC) change at the regional scale is essential for policy makers to address the growing challenges of local sustainability and global climate change. However, it still remains a challenge for estuarine and coastal regions due to the lack of appropriate approaches to consistently generate accurate and long-term LULC maps. In this work, an object-based classification framework was designed to mapping annual LULC changes in the Yangtze River estuary region from 1985–2016 using Landsat time series data. Characteristics of the inter-annual changes of LULC was then analyzed. The results showed that the object-based classification framework could accurately produce annual time series of LULC maps with overall accuracies over 86% for all single-year classifications. Results also indicated that the annual LULC maps enabled the clear depiction of the long-term variability of LULC and could be used to monitor the gradual changes that would not be observed using bi-temporal or sparse time series maps. Specifically, the impervious area rapidly increased from 6.42% to 22.55% of the total land area from 1985 to 2016, whereas the cropland area dramatically decreased from 80.61% to 55.44%. In contrast to the area of forest and grassland, which almost tripled, the area of inland water remained consistent from 1985 to 2008 and slightly increased from 2008 to 2016. However, the area of coastal marshes and barren tidal flats varied with large fluctuations.

scholarly journals Monitoring the Impacts of Drought on Land Use/Cover: A Developed Object-based Algorithm for NOAA AVHRR Time Series Data

2011 ◽  
Vol 11 (17) ◽  
pp. 3089-3103 ◽  
Author(s):  
A. Mokhtari ◽  
S.B. Mansor ◽  
A.R. Mahmud ◽  
Z.M. Helmi
Keyword(s):  
Land Use ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Noaa Avhrr ◽  
Object Based

scholarly journals COMPARATIVE STUDY ON DEEP NEURAL NETWORK MODELS FOR CROP CLASSIFICATION USING TIME SERIES POLSAR AND OPTICAL DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 675-681 ◽  
Author(s):  
G. S. Phartiyal ◽  
D. Singh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Classification Task ◽  
Optical Data ◽  
Series Data ◽  
Study Region ◽  
Crop Classification

<p><strong>Abstract.</strong> Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.</p>

scholarly journals Land Cover and Crop Classification Based on Red Edge Indices Features of GF-6 WFV Time Series Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4522
Author(s):  
Yupeng Kang ◽  
Xinli Hu ◽  
Qingyan Meng ◽  
Youfeng Zou ◽  
Linlin Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Red Edge ◽  
Ndvi Time Series ◽  
Crop Classification

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

scholarly journals Land cover analysis based on descriptive statistics of Sentinel-2 time series data

2018 ◽  
Vol 12 (2) ◽  
pp. 1-9 ◽  
Author(s):  
Orsolya Varga ◽  
Ildikó Gombosné Nagy ◽  
Péter Burai ◽  
Tamás Tomor ◽  
Csaba Lénárt ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Descriptive Statistics ◽  
Series Data ◽  
Corine Land Cover ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Time Series Dataset ◽  
Sentinel 2

In our paper we examined the opportunities of a classification based on descriptive statistics of NDVIthroughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 imagesin 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, inaccordance with Corine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overallaccuracy. We described the reasons for the disagreement in case of the most remarkable errors. .

scholarly journals Crop Classification Based on Temporal Information Using Sentinel-1 SAR Time-Series Data

2018 ◽  
Vol 11 (1) ◽  
pp. 53 ◽  
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Bo Zhang ◽  
Meng Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Hyperspectral Image ◽  
Temporal Information ◽  
Series Data ◽  
Model Matching ◽  
Hyperspectral Image Processing ◽  
Temporal Models ◽  
Temporal Model ◽  
Crop Classification

With the increasing temporal resolution of space-borne SAR, large amounts of intensity data are now available for continues land observations. Previous researches proved the effectiveness of multitemporal SAR in land classification, but the characterizations of temporal information were still inadequate. In this paper, we proposed a crop classification scheme, which made full use of multitemporal SAR backscattering responses. In this method, the temporal intensity models were established by the K-means clustering method. The intensity vectors were treated as input features, and the mean intensity vectors of cluster centers were regarded as the temporal models. The temporal models summarized the backscatter evolutions of crops and were utilized as the criterion for crop discrimination. The spectral similarity value (SSV) measure was introduced from hyperspectral image processing for temporal model matching. The unlabeled pixel was assigned to the class to which the temporal model with the highest similarity belonged. Two sets of Sentinel-1 SAR time-series data were used to illustrate the effectiveness of the proposed method. The comparison between SSV and other measures demonstrated the superiority of SSV in temporal model matching. Compared with the decision tree (DT) and naive Bayes (NB) classifiers, the proposed method achieved the best overall accuracies in both VH and VV bands. For most crops, it either obtained the best accuracies or achieved comparable accuracies to the best ones, which illustrated the effectiveness of the proposed method.

scholarly journals Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

2021 ◽  
Vol 13 (7) ◽  
pp. 1394
Author(s):  
Qinghua Xie ◽  
Kunyu Lai ◽  
Jinfei Wang ◽  
Juan M. Lopez-Sanchez ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Growing Season ◽  
Optimal Combination ◽  
Series Data ◽  
Future Research ◽  
Sar Images ◽  
Crop Monitoring ◽  
Crop Classification

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channelρ ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.

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