scholarly journals Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland

2020 ◽  
Vol 12 (2) ◽  
pp. 341 ◽  
Author(s):  
Ling Wu ◽  
Zhaoliang Li ◽  
Xiangnan Liu ◽  
Lihong Zhu ◽  
Yibo Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Dongting Lake ◽  
Spatiotemporal Dynamics ◽  
Data Availability ◽  
Rapid Expansion ◽  
Core Zone ◽  
The Core ◽  
Subtropical Wetland ◽  
Ndvi Time Series

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an ‘anytime’ forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability.

scholarly journals Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves

2018 ◽  
Vol 10 (8) ◽  
pp. 1251 ◽  
Author(s):  
Boyu Liu ◽  
Jun Chen ◽  
Jiage Chen ◽  
Weiwei Zhang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Shape Features ◽  
Shape Parameters ◽  
Shape Information ◽  
Zero Crossing ◽  
Ndvi Time Series ◽  
Land Cover Change Detection

Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 116 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Carina Sobe ◽  
Janik Deutscher ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Series Data ◽  
Forest Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

scholarly journals Detecting Different Types of Directional Land Cover Changes Using MODIS NDVI Time Series Dataset

2016 ◽  
Vol 8 (6) ◽  
pp. 495 ◽  
Author(s):  
Lili Xu ◽  
Baolin Li ◽  
Yecheng Yuan ◽  
Xizhang Gao ◽  
Tao Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Cover Changes ◽  
Modis Ndvi ◽  
Time Series Dataset ◽  
Different Types ◽  
Ndvi Time Series

scholarly journals Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

2016 ◽  
Vol 8 (9) ◽  
pp. 741 ◽  
Author(s):  
Fanjie Kong ◽  
Xiaobing Li ◽  
Hong Wang ◽  
Dengfeng Xie ◽  
Xiang Li ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Land Cover Classification ◽  
Modis Ndvi ◽  
Ndvi Time Series
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