scholarly journals Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms

2015 ◽  
Vol 160 ◽  
pp. 99-113 ◽  
Author(s):  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Weili Kou ◽  
Yuanwei Qin ◽  
Geli Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Paddy Rice ◽  
Landsat Images

scholarly journals Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

2019 ◽  
Vol 11 (14) ◽  
pp. 1699 ◽  
Author(s):  
Qi Yin ◽  
Maolin Liu ◽  
Junyi Cheng ◽  
Yinghai Ke ◽  
Xiuwan Chen
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Paddy Rice ◽  
Northeastern China ◽  
Rice Fields ◽  
Fusion Algorithm ◽  
Landsat Images ◽  
Modis Data ◽  
Temporal Sampling ◽  
New Strategy

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

Tracking the dynamics of paddy rice cultivation practice through MODIS time series and PhenoRice algorithm

2021 ◽  
Vol 307 ◽  
pp. 108538
Author(s):  
Nirajan Luintel ◽  
Weiqiang Ma ◽  
Yaoming Ma ◽  
Binbin Wang ◽  
Jie Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Paddy Rice ◽  
Rice Cultivation ◽  
Cultivation Practice

A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images

2021 ◽  
Vol 259 ◽  
pp. 112394
Author(s):  
Huijin Yang ◽  
Bin Pan ◽  
Ning Li ◽  
Wei Wang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Paddy Rice ◽  
Systematic Method ◽  
Spatio Temporal

scholarly journals Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984–2010 through PALSAR and time series Landsat images

2017 ◽  
Vol 190 ◽  
pp. 233-246 ◽  
Author(s):  
Jie Wang ◽  
Xiangming Xiao ◽  
Yuanwei Qin ◽  
Jinwei Dong ◽  
George Geissler ◽  
...  
Keyword(s):  
Time Series ◽  
Eastern Redcedar ◽  
Landsat Images

scholarly journals Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria

2021 ◽  
Vol 25 (8) ◽  
pp. 1449-1452
Author(s):  
P.A. Ukoha ◽  
S.J. Okonkwo ◽  
A.R. Adewoye
Keyword(s):  
Time Series ◽  
Change Point ◽  
Vegetation Index ◽  
Google Earth ◽  
Change Point Detection ◽  
Mcmc Methods ◽  
Landsat Images ◽  
Southwestern Nigeria ◽  
Forest Reserve ◽  
Point Detection

This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian Change Point (BCP) to monitor the abrupt changes in vegetation dynamics associated with deforestation. The BCP shows the magnitude of changes over the years, from the posterior data obtained. BCP focuses on changes in the long‐range using Markov Chain Monte Carlo (MCMC) methods, this returns posterior probability at > 0.5% of a change point occurring at each time index in the time series. Three decades of Landsat data were classified using the random forest algorithm to assess the rate of deforestation within the study area. The results shows forest in 2000 (97.7%), 2010 (89.4%), 2020 (84.7%) and non-forest increase 2000 (2.0%), 2010 (10.6%), 2020 (15.3%). Kappa coefficient was also used to determine the accuracy of the classification.

scholarly journals Paddy Rice Mapping in Thailand Using Time-Series Sentinel-1 Data and Deep Learning Model

2021 ◽  
Vol 13 (19) ◽  
pp. 3994
Author(s):  
Lu Xu ◽  
Hong Zhang ◽  
Chao Wang ◽  
Sisi Wei ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Development ◽  
Large Scale ◽  
Management Practices ◽  
Conditional Random Field ◽  
Paddy Rice ◽  
Growth Patterns ◽  
Selection Strategy ◽  
Svm Classifier ◽  
Humid Climate

The elimination of hunger is the top concern for developing countries and is the key to maintain national stability and security. Paddy rice occupies an essential status in food supply, whose accurate monitoring is of great importance for human sustainable development. As one of the most important paddy rice production countries in the world, Thailand has a favorable hot and humid climate for paddy rice growing, but the growth patterns of paddy rice are too complicated to construct promising growth models for paddy rice discrimination. To solve this problem, this study proposes a large-scale paddy rice mapping scheme, which uses time-series Sentinel-1 data to generate a convincing annual paddy rice map of Thailand. The proposed method extracts temporal statistical features of the time-series SAR images to overcome the intra-class variability due to different management practices and modifies the U-Net model with the fully connected Conditional Random Field (CRF) to maintain the edge of the fields. In this study, 758 Sentinel-1 images that covered the whole country from the end of 2018 to 2019 were acquired to generate the annual paddy rice map. The accuracy, precision, and recall of the resultant paddy rice map reached 91%, 87%, and 95%, respectively. Compared to SVM classifier and the U-Net model based on feature selection strategy (FS-U-Net), the proposed scheme achieved the best overall performance, which demonstrated the capability of overcoming the complex cultivation conditions and accurately identifying the fragmented paddy rice fields in Thailand. This study provides a promising tool for large-scale paddy rice monitoring in tropical production regions and has great potential in the global sustainable development of food and environment management.

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