FOREST DISTURBANCE DETECTION BY LANDSAT-BASED NDVI TIME SERIES FOR AYUQUILA RIVER BASIN, JALISCO, MEXICO

Abstract. Time series data have been applied for forest disturbance detection. The validation of detected changes is challenging partially because the validation data are often not readily available. Unlike multi-temporal change analysis, time series analysis not only detects areas of change but also reports time of change. Both spatial and temporal accuracy are therefore important for the accuracy assessment. Ayuquila River Basin (ARB) is one of the early action areas in Mexico for the implementation of REDD+ initiatives under UNFCCC. In ARB, shifting cultivation and cattle grazing often take place, resulting in degraded forestland. Sub-annual forest disturbance detection and estimation contribution to the improved local forest management and REDD+ implementation. Landsat-based NDVI time series data covering 1999–2018 were analysed using linear regression and the breakpoints of change and the magnitude of change were detected. Breakpoints with magnitude of change ranging from (−0.05) to (−0.2) were verified during a field campaign in October 2018. Here the magnitude of change is related with NDVI differences. Areas with magnitude of change higher than (−0.2) were identified as false changes. Verification data were generated by visually interpreting time series Landsat images of 2016–2018. In this way, areas with forest loss were identified. By stratified random sampling, 683 points were applied for the verification including 511 points of forests and 172 points of forest loss. It yields 75.84% for the overall accuracy of the change detection; for the detected forest loss as a category, the user’s accuracy is 88.89% and the producer’s accuracy is 0.46%. A possible reason for the very low producer’s accuracy is that the selected magnitude value (−0.2) is too low and some of the detected changes were filtered out.

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An autoencoder-based model for forest disturbance detection using Landsat time series data

International Journal of Digital Earth ◽

10.1080/17538947.2021.1949399 ◽

2021 ◽

pp. 1-16

Author(s):

Gaoxiang Zhou ◽

Ming Liu ◽

Xiangnan Liu

Keyword(s):

Time Series ◽

Time Series Data ◽

Forest Disturbance ◽

Series Data ◽

Disturbance Detection

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Forest Disturbance Detection By Landsat-Based Ndvi Time Series For Ayuquila River Basin, Jalisco, Mexico

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) ◽

10.1109/lagirs48042.2020.9165583 ◽

2020 ◽

Author(s):

Y. Gao ◽

A. Quevedo ◽

J. Loya

Keyword(s):

Time Series ◽

River Basin ◽

Forest Disturbance ◽

Ndvi Time Series ◽

Disturbance Detection

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Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Sustainability ◽

10.3390/su13020542 ◽

2021 ◽

Vol 13 (2) ◽

pp. 542

Author(s):

Tarate Suryakant Bajirao ◽

Pravendra Kumar ◽

Manish Kumar ◽

Ahmed Elbeltagi ◽

Alban Kuriqi

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Suspended Sediment ◽

River Basin ◽

Time Series Data ◽

Series Data ◽

Discrete Wavelet ◽

Original Time Series ◽

Inference System ◽

Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

Remote Sensing ◽

10.3390/rs11141683 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1683 ◽

Cited By ~ 6

Author(s):

Yangchengsi Zhang ◽

Long Guo ◽

Yiyun Chen ◽

Tiezhu Shi ◽

Mei Luo ◽

...

Keyword(s):

Time Series ◽

Organic Carbon ◽

Time Series Data ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Series Data ◽

Landsat 8 ◽

Auxiliary Variables ◽

Jianghan Plain ◽

Ndvi Time Series

High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSEP) and coefficient of determination of prediction (R2P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSEP of 3.718 and highest R2P of 0.391, followed by SVM (RMSEP = 3.753, R2P = 0.361) and PLSR (RMSEP = 4.087, R2P = 0.283). The SLR model was the worst model, with the lowest R2P of 0.281 and highest RMSEP of 3.930. ANN and SVM were better than OK (RMSEP = 3.727, R2P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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