Forest cover trends from time series Landsat data for the Australian continent

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

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Improving the Accuracy of Fractional Evergreen Forest Cover Estimation at Subpixel Scale in Cloudy and Rainy Areas by Harmonizing Landsat-8 and Sentinel-2 Time-Series Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3064580 ◽

2021 ◽

Vol 14 ◽

pp. 3373-3385

Author(s):

Taixia Wu ◽

Yuting Zhao ◽

Shudong Wang ◽

Hongjun Su ◽

Yingying Yang ◽

...

Keyword(s):

Time Series ◽

Forest Cover ◽

Time Series Data ◽

Evergreen Forest ◽

Series Data ◽

Landsat 8 ◽

Sentinel 2

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Distance metric-based forest cover change detection using MODIS time series

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2014.01.004 ◽

2014 ◽

Vol 29 ◽

pp. 78-92 ◽

Cited By ~ 19

Author(s):

Xiaoman Huang ◽

Mark A. Friedl

Keyword(s):

Time Series ◽

Change Detection ◽

Forest Cover ◽

Distance Metric ◽

Forest Cover Change

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Causes for Deforestation in Indonesia: Corruption and Palm Tree Plantation

Asian Social Science ◽

10.5539/ass.v11n27p120 ◽

2015 ◽

Vol 11 (27) ◽

pp. 120

Author(s):

Osama Eldeeb ◽

Petr Prochazka ◽

Mansoor Maitah

Keyword(s):

Time Series ◽

Palm Oil ◽

Forest Cover ◽

Palm Tree ◽

Test Statistics ◽

Crude Palm Oil ◽

Tree Plantation ◽

Long Run ◽

Corruption Perception ◽

Forest Cover Loss

<p>Indonesian biodiversity is threatened by massive deforestation. In this research paper, claims that deforestation in Indonesia is caused by corruption and supported by crude palm oil production is verified using time series analysis. Using Engel Granger cointegration test, three time series of data, specifically corruption perception index, rate of deforestation and price of crude palm oil are inspected for a long-run relationship. Test statistics suggests that there is no long-run relationship among these variables. Authors provide several explanations for this result. For example, corruption in Indonesia, as measured by CPI is still very high. This may mean that forest cover loss is possible even though there is a positive change in corruption level. According to the results, crude palm oil price has also no effect upon forest cover loss. This is likely due to very low shut-down price of crude palm oil for which production is still economical.</p>

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Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.137409 ◽

2020 ◽

Vol 720 ◽

pp. 137409 ◽

Cited By ~ 3

Author(s):

Xiaojing Tang ◽

Lucy R. Hutyra ◽

Paulo Arévalo ◽

Alessandro Baccini ◽

Curtis E. Woodcock ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Carbon Emissions ◽

Spatially Explicit ◽

Landsat Data ◽

Series Analysis

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The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2613-2013 ◽

2013 ◽

Vol 17 (7) ◽

pp. 2613-2635 ◽

Cited By ~ 58

Author(s):

H. E. Beck ◽

L. A. Bruijnzeel ◽

A. I. J. M. van Dijk ◽

T. R. McVicar ◽

F. N. Scatena ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Forest Regeneration ◽

Forest Cover ◽

Impact Analysis ◽

Forest Area ◽

Macro Scale ◽

Annual Time Series ◽

The Impact ◽

Annual Time

Abstract. Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of Â were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of Â were insignificant (p &geq; 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

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DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1385-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1385-1388

Author(s):

H. Miyazaki ◽

M. Nagai ◽

R. Shibasaki

Keyword(s):

Public Health ◽

Time Series ◽

Risk Management ◽

Satellite Data ◽

Disaster Risk ◽

Disaster Risk Management ◽

Training Data ◽

Series Data ◽

Landsat Data ◽

Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1385-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1385-1388 ◽

Cited By ~ 1

Author(s):

H. Miyazaki ◽

M. Nagai ◽

R. Shibasaki

Keyword(s):

Public Health ◽

Time Series ◽

Risk Management ◽

Satellite Data ◽

Disaster Risk ◽

Disaster Risk Management ◽

Training Data ◽

Series Data ◽

Landsat Data ◽

Human Settlement

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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