scholarly journals Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1775 ◽  
Author(s):  
Zhihui Wang ◽  
Wenyi Yao ◽  
Qiuhong Tang ◽  
Liangyun Liu ◽  
Peiqing Xiao ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Loess Plateau ◽  
Vegetation Coverage ◽  
Landsat Data ◽  
Vegetation Types ◽  
Continuous Change ◽  
The Loess Plateau ◽  
Ndvi Time Series

Accurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.

Spatio-temporal detection of converting from cropland to forest/grassland on the Loess Plateau using all available Landsat time-series images

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Peiqing Xiao
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Loess Plateau ◽  
Water Conservation ◽  
Series Data ◽  
Vegetation Types ◽  
Continuous Change ◽  
The Loess Plateau ◽  
Ndvi Time Series ◽  
Spatio Temporal

<p><strong>Conversion of cropland to forest/grassland has become a key ecological restoration measure on the Loess Plateau since 1999. Accurate mapping of the spatio-temporal dynamic information of conversion from cropland into forest/grassland is necessary for studying the effects of vegetation change on hydro-ecological process and soil and water conservation on the Loess Plateau, China. Currently, the accuracy of change detection of farmland and forest/grassland at 30-m scale in this area is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, an innovative method for continuous change detection of cropland and forest/grassland using all available Landsat time-series data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation.</strong></p>

scholarly journals Time-Series Model-Adjusted Percentile Features: Improved Percentile Features for Land-Cover Classification Based on Landsat Data

2020 ◽  
Vol 12 (18) ◽  
pp. 3091
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Jiangning Yang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Time Series Model ◽  
Training Data ◽  
Series Data ◽  
Landsat Data ◽  
Data Sampling ◽  
Continuous Change

Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage.

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.

Soil conservation on the Loess Plateau and the regional effect: impact of the ‘Grain for Green' Project

2018 ◽  
Vol 109 (3-4) ◽  
pp. 461-471
Author(s):  
Xiaofeng WANG ◽  
Feiyan XIAO ◽  
Xiaoming FENG ◽  
Bojie FU ◽  
Zixiang ZHOU ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Land Cover ◽  
Loess Plateau ◽  
Soil Conservation ◽  
Yellow River ◽  
Vegetation Coverage ◽  
The Yellow River ◽  
The Loess Plateau ◽  
Regional Effect ◽  
Grain For Green

ABSTRACTSoil conservation on the Loess Plateau is important not only for local residents but also for reducing sediment downstream in the Yellow River. In this paper, we report a decrease in soil erosion from 2000 to 2010 as a result of the ‘Grain for Green' (GFG) Project. By using the Revised Universal Soil Loss Equation and data on land cover, climate and sediment yield, we found that soil erosion decreased from 6579.55tkm–2yr–1 in 2000 to 1986.66tkm–2yr–1 in 2010. During this period, there was a major land cover change from farmland to grassland in response to the GFG. The area of low vegetation coverage with severe erosion decreased dramatically, whereas the area of high vegetation coverage with slight erosion increased. Our study demonstrates that the reduction in soil erosion on the Loess Plateau contributed to the decrease in the sediment concentration in the Yellow River.

scholarly journals An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2779 ◽  
Author(s):  
Katie Awty-Carroll ◽  
Pete Bunting ◽  
Andy Hardy ◽  
Gemma Bell
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Land Cover ◽  
Change Detection ◽  
Seasonal Changes ◽  
Detection Methods ◽  
Continuous Change ◽  
Validation Data ◽  
Simulated Time ◽  
Simulated Time Series

Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.

scholarly journals A LANDSAT TIME-SERIES STACKS MODEL FOR DETECTION OF CROPLAND CHANGE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 693-697
Author(s):  
J. Chen ◽  
J. Chen ◽  
J. Zhang
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Surface ◽  
Spectral Band ◽  
Main Idea ◽  
Landsat 8 ◽  
Continuous Change ◽  
Landsat Images ◽  
Detection Techniques

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the “true change” without overestimating the “false” one, while CVA pointed out “true change” pixels with a large number of “false changes”. The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

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