Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data

Franklin, Steven E.; Ahmed, Oumer S.; Wulder, Michael A.; White, Joanne C.; Hermosilla, Txomin; Coops, Nicholas C.

doi:10.1080/07038992.2015.1089401

Cited by 73 publications

(42 citation statements)

References 34 publications

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“…These findings are consistent with other studies in Sri Lanka that have detected LULCC at local scales [17][18][19][69][70][71][72][73]. Many comparable time-series studies have been carried out using Landsat data, but they have been mainly restricted to vegetation change analysis in mid-latitude countries [2,6,12,23,28,29,38,64]. The few studies that have attempted time-series remote sensing in tropical regions concede it is a challenge given the high incidence of clouds [26,41,[74][75][76][77][78].…”

Section: Discussionsupporting

confidence: 90%

“…On the other hand, Landsat 8 (Operational Land Imager (OLI)) SR products are generated using the Landsat 8 Surface Reflectance Code (LaSRC) algorithm [35]. These developments have collectively increased the number of time-series applications in change detection studies from local to regional and global scales [2,30,[36][37][38][39]. Multiple pixel-based time-series approaches have also been designed and tested in the last decade to detect LULCC at a high temporal scale [13,23,37,38,[40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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

Rathnayake

Jones

Soto‐Berelov

2020

Land

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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.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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

Rathnayake

Jones

Soto‐Berelov

2020

Land

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“…In the global land cover map produced by [22] the highest overall classification accuracy for eight land cover classes was 71.5%. Application of time series of images and combination of land cover classification with change detection can improve land cover classification results for a selected date in a time series and provide accuracies close to or even above 90%, even for detailed class division [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

et al. 2018

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The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

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“…For example, the global Forest Cover database describes forest gain and loss at 30 m resolution, for the entire globe on an annual basis during 2000-2014 [8]. During the last years, several time series of land use and land cover maps have been elaborated at global scale (e.g., MODIS MCD12 product, Climate Change Initiative (CCI) Land Cover Annual Global Land Cover Maps) or for different regions of the world [9][10][11][12][13][14][15][16]. These increasingly available LUCC data with high temporal and thematic resolutions pose the challenge of developing novel methods able to process high volumes of data which describe the succession of land use/cover types over time [15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of High Temporal Resolution Land Use/Land Cover Trajectories

Mas

Vasconcelos

Franca-Rocha

2019

Land

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In this study, methods, originally developed to assess life course trajectories, are explored in order to evaluate land change through the analysis of sequences of land use/cover. Annual land cover maps which describe land use/land cover change for the 1985–2017 period for a large region in Northeast Brazil were analyzed. The most frequent sequences, the entropy and the turbulence of the land trajectories, and the average time of permanence were computed. Clusters of similar sequences were determined using different dissimilarity measures. The effect of some covariates such as slope and distance from roads on land trajectories was also evaluated. The obtained results show the potential of these techniques to analyze land cover sequences since the availability of multidate land cover data with both, high temporal and thematic resolutions, is continuously increasing and poses significant challenges to data analysis.

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Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data

Cited by 73 publications

References 34 publications

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

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

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Analysis of High Temporal Resolution Land Use/Land Cover Trajectories

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