The arctic and boreal biomes are changing as temperatures increase, including changes in the type, frequency, intensity, and seasonality of disturbances. However, our understanding of the frequency, ...extent, and causes of disturbance events remains incomplete. Disturbances such as fire, forest harvest, drought, wind, flooding, and insects and pathogens occur at different frequencies and severities, posing challenges to characterize and assess them under a single framework. We used the Continuous Change Detection and Classification (CCDC) algorithm on all available Landsat observations from 1984 to 2014 to detect land cover and land condition change. We mapped the following causes of disturbances annually across the study domain of NASA's Arctic Boreal Vulnerability Experiment (ABoVE): fire, logging, and pest damage. Differences between Landsat Tasseled Cap (TC) values pre- and post-disturbance were used in a random forest classifier to map causal agents. For forested ecosystems, we mapped causal agents including fire, insect, and logging. In areas that were not forest before disturbance, only the fire class was mapped. The result shows that multidimensional spectral-temporal change information is useful for mapping the causes of disturbance in arctic and boreal biomes. We employed two rounds of post-processing and used the information obtained from the comparison between the map and reference data to improve the final map. The user's and producer's accuracies of an aggregated disturbance map were 94.6% (± 2.37%) and 89.3% (± 21.78%) (95% confidence intervals in parenthesis). When evaluating the causal agents, insect damage was found the most challenging to map and validate. We estimated that 10.8% of the ABoVE core domain was disturbed between 1987 and 2012, with a margin of error of 0.5% at the 95% confidence level. Rates of disturbance due to logging remained constant over time, while fires were more episodic, and insect damage was highest between 2005 and 2010. Overall, fires affected 8.8% of the study area, while logging was 1.4% and insect damage 0.6%. Our maps indicate that pest damage became a significant issue after 2000, but it was more severe for forest ecosystems in Western Canada than in Alaska.
•New methods using delta-TC metrics and CCDC to map causal agents of disturbance.•7.2% of the ABoVE core domain experienced disturbance between 1987 and 2012.•Pest damage caused 5.4% of the disturbance; fire was 81.8%; logging was 12.8%.•Improved final maps by learning from reference data stratified on a preliminary map.
A new change detection algorithm for continuous monitoring of forest disturbance at high temporal frequency is developed. Using all available Landsat 7 images in two years, time series models ...consisting of sines and cosines are estimated for each pixel for each spectral band. Dropping the coefficients that capture inter-annual change, time series models can predict surface reflectance for pixels at any location and any date assuming persistence of land cover. The Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) flags forest disturbance by differencing the predicted and observed Landsat images. Two algorithms (single-date and multi-date differencing) were tested for detecting forest disturbance at a Savannah River site. The map derived from the multi-date differencing algorithm was chosen as the final CMFDA result, due to its higher spatial and temporal accuracies. It determines a disturbance pixel by the number of times “change” is observed consecutively. Pixels showing “change” for one or two times are flagged as “probable change”. If the pixel is flagged for the third time, the pixel is determined to have changed. The accuracy assessment shows that CMFDA results were accurate for detecting forest disturbance, with both producer's and user's accuracies higher than 95% in the spatial domain and temporal accuracy of approximately 94%.
► A new continuous monitoring of forest disturbance algorithm (CMFDA). ► Pixels showing “change” for one or two times are flagged as “probable change”. ► Pixels showing “change” for the third times are determined to have changed. ► Both producer's and user's accuracies of CMFDA are higher than 95%. ► Temporal accuracy of CMFDA is approximately 94%.
Although shifting cultivation is the major land use type in Laos, the spatial-temporal patterns and the associated carbon emissions of shifting cultivation in Laos are largely unknown. This study ...provides a nationwide analysis of the spatial-temporal patterns of shifting cultivation and estimations of the associated carbon emissions in Laos over the last three decades. This study found that shifting cultivation has been expanding and intensifying in Laos, especially in the last five years. The newly cultivated land from 2016-2020 accounted for 4.5% (±1.2%) of the total land area of Laos. Furthermore, the length of fallow periods has been continuously declining, indicating that shifting cultivation is becoming increasingly intensive. Combining biomass derived from GEDI (Global Ecosystem Dynamics Investigation) and shifting cultivation maps and area estimates, we found that the net carbon emissions from shifting cultivation declined in 2001-2015 but increased in 2016-2020. The largest carbon source is conversion from intact forests to shifting cultivation, which contributed to 89% of the total emissions from 2001 to 2020. In addition, there were increased emissions from intensified use of fallow land. This research provides useful information for policymakers in Laos to understand the changes in shifting cultivation and improve land use management. This study not only supports REDD+ (Reducing Emissions from Deforestation and forest Degradation) reporting for Laos but also provides a methodology for tracking carbon emissions and removals of shifting cultivation.
Reducing terrestrial carbon emissions to the atmosphere requires accurate measuring, reporting and verification of land surface activities that emit or sequester carbon. Many carbon accounting ...practices in use today provide only regionally aggregated estimates and neglect the spatial variation of pre-disturbance forest conditions and post-disturbance land cover dynamics. Here, we present a spatially explicit carbon bookkeeping model that utilizes a high-resolution map of aboveground biomass and land cover dynamics derived from time series analysis of Landsat data. The model produces estimates of carbon emissions/uptake with model uncertainty at Landsat resolution. In a case study of the Colombian Amazon, an area of 47 million ha, the model estimated total emissions of 3.97 ± 0.71 Tg C yr−1 and uptake by regenerating forests of 1.11 ± 0.23 Tg C yr−1 2001–2015, with an additional 45.1 ± 7.99 Tg of carbon remaining in the form of woody products, decomposing slash and charcoal at the end of 2015 (estimates provided with ±95% confidence intervals). Total emissions attributed to the study period (including carbon not yet released) is 6.97 ± 1.24 Tg C yr−1. The presented model is based on recent technological advancements in the field of remote sensing to achieve spatially explicit modeling of carbon emissions and uptake associated with land surface changes and post-disturbance landscapes that is compliant with international reporting criteria.
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•A new spatially explicit carbon bookkeeping model compliant with IPCC tier 3•Activity data based on time series analysis of Landsat observations•Initial aboveground biomass of forests extracted from Landsat-based biomass map•Total emissions and uptake estimated for the Colombian Amazon for 2001–2015•Spatializing the parameters and input data improves estimates of carbon emissions.
Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of ...change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).
•We developed a robust capability for operational monitoring of land surface change.•Landsat ARD and Continuous Change Detection and Classification are foundational.•Landsat's rich time series has substantial variability in observation frequency.•The algorithm was modified reducing variability in results between scene centers and overlap zones.•Classification was modified to improve training data representativeness and reduce artifacts.
A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series ...of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30+ year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series.
•Landsat time series were produced for fifteen Canadian boreal forest sites.•Landsat time series contain biases and variability caused by data artifacts.•Sensor view angle variance affects red and NIR reflectances but not NDVI.•Landsat 7 ETM+ data have lower red reflectance values than Landsat 5 TM data.•Combining ETM+ and TM data introduces artificial long-term trends in NDVI.
The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several ...viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.
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•Stacking can be used to leverage an ensemble of maps to improve accuracy.•Stacking can align a mapping process with project-specific class definitions.•Random Forests was better than logistic regression as an ensemble fusion rule.•Cloud computing lowers barriers to stacking.•Future algorithm development may focus on specialization to fill ensemble gaps.
Mangrove forests exert a strong influence on tropical deltas by trapping sediments discharged by rivers and by stabilizing the substrate with roots. Understanding the dynamics of sediments and ...morphology in and around mangrove forests is critical in order to assess the resilience of coastlines in a period of accelerated sea level rise. In this research, sediment samples, mangrove forest characteristics, and remote sensing data are used to investigate the relationship between mangroves and sediment substrate in the Mekong Delta, Vietnam. Our data show a significant correlation between percent of sand in bottom sediments and density of Sonneratia caseolaris forest. We ascribe this result to higher sediment disturbance in muddy areas that prevents seedling establishment. This correlation potentially allows the determination of substrate characteristics from vegetation attributes detected by remote sensing, despite the impenetrability of the forest canopy. The results presented herein suggest that a supply of sand from the river and hydrodynamic processes moving the sand ashore control the density of the Sonneratia mangrove forests at this location, promoting tidal flat colonization and canopy expansion.
Land cover and land change were monitored continuously between 1985 and 2011 at 30 m resolution across New England in the Northeastern United States in support of modeling the terrestrial carbon ...budget. It was found that the forest area has been decreasing throughout the study period in each state of the region since the 1980s. A total of 386 657 98 137 ha (95% confidence interval) of forest has been converted to other land covers since 1985. Mainly driven by low density residential development, the deforestation accelerated in the mid-1990s until 2007 when it plateaued as a result of declining new residential construction and in turn, the financial crisis of 2007-08. The area of forest harvest, estimated at 226 519 66 682 ha, was mapped separately and excluded from the deforestation estimate, while the area of forest expansion on non-forested lands was found to not be significantly different from zero. New England is often held as a principal example of a forest transition with historical widespread deforestation followed by recovery of forestlands as farming activities diminished, but the results of this study support the notion of a reversal of the forest transition as the region again is experiencing widespread deforestation. All available Landsat imagery acquired after 1985 for the study area were collected and used in the analysis. Areas of land cover and land change were estimated from a random sample of reference observations stratified by a twelve-class land change map encompassing the entire study area and period. The statistical analysis revealed that the net change in forest area and the associated modeled impact on the terrestrial carbon balance would have been considerably different if the results of the map were used without inferring the area of forest change by analysis of a reference sample.
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