scholarly journals Mapping Land Cover Change over a 25-Year Period (1993–2018) in Sri Lanka Using Landsat Time-Series

Land ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 27 ◽  
Author(s):  
Chithrangani WM Rathnayake ◽  
Simon Jones ◽  
Mariela Soto-Berelov
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Detection Algorithm ◽  
Economic Stability ◽  
Change Analysis ◽  
Forest Reserves ◽  
Normalised Difference Vegetation Index

Land use and land cover change (LULCC) are dynamic over time and space due to human and biophysical factors. Accurate and up-to-date LULCC information is a mandatory part of environmental change analysis and natural resource management. In Sri Lanka, there is a significant temporal gap in the existing LULCC information due to the civil war that took place from 1983 to 2009. In order to fill this gap, this study presents a whole-country LULCC map for Sri Lanka over a 25-year period using Landsat time-series imagery from 1993 to 2018. The LandTrendr change detection algorithm, utilising the normalised burn ratio (NBR) and normalised difference vegetation index (NDVI), was used to develop spectral trajectories over this time period. A land cover change and disturbance map was created with random forest, using 2117 manually interpreted reference pixels, of which 75% were used for training and 25% for validation. The model achieved an overall accuracy of 94.14%. The study found that 890,003.52 hectares (ha) (13.5%) of the land has changed, while 72,266.31 ha (1%) was disturbed (but not permanently changed) over the last 25 years. LULCC was found to concentrate on two distinct periods (2000 to 2004 and 2010 to 2018) when social and economic stability allowed greater land clearing and investment opportunities. In addition, LULCC was found to impact forest reserves and protected areas. This new set of Sri Lanka-wide land cover information describing change and disturbance may provide a reference point for policy makers and other stakeholders to aid in decision making and for planning purposes.

scholarly journals Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover

2021 ◽  
Vol 13 (16) ◽  
pp. 3339
Author(s):  
Matthew Nigel Lawton ◽  
Belén Martí-Cardona ◽  
Alex Hagen-Zanker
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Urban Growth ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Ground Truth ◽  
Data Availability ◽  
Change Analysis ◽  
Normalised Difference Vegetation Index

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period.

scholarly journals Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

2021 ◽  
Vol 13 (19) ◽  
pp. 3951
Author(s):  
Kim André Vanselow ◽  
Harald Zandler ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Demographic Data ◽  
Mountainous Areas ◽  
Generalized Additive Mixed Models ◽  
Additive Mixed Models ◽  
Irregular Data

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

Trend analysis of medium- and coarse-resolution time series image data for burned area mapping in a Mediterranean ecosystem

2014 ◽  
Vol 23 (5) ◽  
pp. 668 ◽  
Author(s):  
Thomas Katagis ◽  
Ioannis Z. Gitas ◽  
Pericles Toukiloglou ◽  
Sander Veraverbeke ◽  
Rudi Goossens
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Probability Of Detection ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Normalised Difference Vegetation Index ◽  
Induced Changes

In this study, the Breaks for Additive Seasonal and Trend (BFAST), a recently introduced trend analysis technique, was employed for the detection of fire-induced changes in a Mediterranean ecosystem. BFAST enables the decomposition of time series into trend, seasonal and noise components, resulting in the detection of gradual and rapid land cover changes. Normalised Difference Vegetation Index (NDVI) time series derived from the MODIS and VEGETATION (VGT) standard products were analysed. The time series decomposition resulted in the mapping of the burned area and the demonstration of the post-fire vegetation recovery trend. The observed gradual changes revealed an increase of NDVI values over time, indicating post-fire vegetation recovery. Spatial validation of the generated burned area maps with a higher resolution reference map was performed and probability statistics were derived. Both maps achieved a high probability of detection – 0.90 for MODIS and 0.87 for VGT – and a low probability of false alarms, 0.01 for MODIS and 0.02 for VGT. In addition, the Pareto boundary theory was implemented to account for the low-resolution bias of the maps. BFAST facilitated detection of fire-induced changes using image time series, without having to set thresholds, select specific seasons or adjust to certain land cover types. Further evaluation of the approach should focus on a more comprehensive assessment across regions and time.

scholarly journals BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

2021 ◽  
Vol 13 (16) ◽  
pp. 3308 ◽  
Author(s):  
Dainius Masiliūnas ◽  
Nandin-Erdene Tsendbazar ◽  
Martin Herold ◽  
Jan Verbesselt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Missing Values ◽  
Confusion Matrix ◽  
Detection Algorithm ◽  
Information Criteria ◽  
Modis Imagery ◽  
Similar Accuracy

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

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