An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites

Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Hobart, Geordie W.

doi:10.1016/j.rse.2014.11.005

Cited by 266 publications

(153 citation statements)

References 48 publications

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“…Some clearcut plots were erroneously identified as roads, which also are characterized by a steep drop in the spectral response through time. In areas with harvest activity, however, roads are sometimes temporary and can have various surface materials (e.g., dirt, gravel) rendering them difficult to attribute due to lack of uniformity and a spectral similarity to clearcuts [31]. Specific spatial rules to distinguish between roads and other features based on geometric properties, such as linearity, are possible [39].…”

Section: Discussionmentioning

confidence: 99%

“…This represents the potential ability to further distinguish between harvesting practices in the disturbance classification hierarchy presented in Hermosilla et al [31]. Due to the opening of the Landsat archive, the analysis of spectral time series of Landsat data covering large jurisdictions is an increasingly operational approach for verifying harvest activity at a reduced cost compared to field visits or photo digitization approaches for traditional strategic forest inventories.…”

Section: Discussionmentioning

confidence: 99%

“…These functions rank every pixel based on the presence and distance to clouds and their shadows, the atmospheric quality, and the acquisition sensor. These image composites are further refined by removing noisy observations and infilling of data gaps using spectral trend analysis of pixel time series [31], which results in seamless annual surface-reflectance composites for all of Canada from 1984 to 2012, as well as the detection and characterization of forest change events greater than 0.5 ha (minimum map unit). Following the object-based image analysis approach presented in Hermosilla et al [11], detected changes are attributed to a change type (i.e., fire, harvesting, road, or non-stand-replacing), based on their spectral, temporal, and geometrical characteristics using a Random Forests classifier.…”

Section: Landsat Time-series Datamentioning

confidence: 99%

“…To do so, six spectral metrics, obtained from the time series, were calculated and extracted for each of the analyzed harvest polygons [31]. The six spectral metrics were change persistence (duration), average change magnitude variation, average spectral value postchange, average spectral value pre-change, range of pixel series values, and standard deviation of pixel series values (see full description in Hermosilla et al, [31]). Persistence represents the length in years following the harvest event that spectral values remain at post-change levels.…”

Section: Spectral Characterization Of Harvesting Practicesmentioning

confidence: 99%

See 3 more Smart Citations

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

Jarron

Hermosilla

Coops

et al. 2016

Forests

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Sustainable forest management practices allow for a range of harvest prescriptions, including clearcut, clearcut with residual, and partial or selective cutting, which are largely distinguished by the amount of canopy cover removed. The different prescriptions are aimed to emulate natural disturbance, encourage regeneration (seed trees), or offer other ecosystem services, such as the maintenance of local biodiversity or habitat features. Using remotely sensed data, stand-replacing disturbance associated with clearcutting is commonly accurately detected. Novel time series-based change detection products offer an opportunity to determine the capacity to detect and label a wider range of harvest practices. In this research, we demonstrate the capacity of time series imagery, spectral metrics, and related attributed change products, to distinguish between different harvesting practices over a study area in central British Columbia, Canada. Producer's accuracy of harvest attribution was 79%, with 93% of harvest blocks >5 ha accurately identified. In relation to the amount of canopy cover removed, clearcut harvesting was the most accurately classified (84%), followed by clearcut with residual (79%), and partial cut (64%). Applying detailed spectral metrics derived from Landsat data revealed clearcut and partial cuts to be spectrally distinct. The annual nature of the Landsat time series also offers spatial harvest information within typical, often decadal, forest inventory update cycles. The statistically significant (p < 0.05) relationship between harvest practices and Landsat spectral information indicates a capacity to add increased attribution richness to remote sensing depictions of forest harvest.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Landsat Time-series Datamentioning

confidence: 99%

Section: Spectral Characterization Of Harvesting Practicesmentioning

confidence: 99%

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Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

Jarron

Hermosilla

Coops

et al. 2016

Forests

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“…Furthermore, pre-processing approaches and noise-reduction algorithms for Landsat have evolved in recent decades [4], making for significant variation in preparing the data it generates to enhance breakpoint detection [5][6][7]. For this reason, the interpreter's experience plays an important role in selecting, calibrating, and applying an algorithm.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms

2017

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Abstract:The Andean Amazon is an endangered biodiversity hot spot but its forest dynamics are less studied than those of the Amazon lowland and forests from middle or high latitudes. This is because its landscape variability, complex topography and cloudy conditions constitute a challenging environment for any remote-sensing assessment. Breakpoint detection with Landsat time-series data is an established robust approach for monitoring forest dynamics around the globe but has not been properly evaluated for implementation in the Andean Amazon. We analyzed breakpoint detection-generated forest dynamics in order to determine its limitations when applied to three different study areas located along an altitude gradient in the Andean Amazon in Ecuador. Using all available Landsat imagery for the period 1997-2016, we evaluated different pre-processing approaches, noise reduction techniques, and breakpoint detection algorithms. These procedures were integrated into a complex function called the processing chain generator. Calibration was not straightforward since it required us to define values for 24 parameters. To solve this problem, we implemented a novel approach using genetic algorithms. We calibrated the processing chain generator by applying a stratified training sampling and a reference dataset based on high resolution imagery. After the best calibration solution was found and the processing chain generator executed, we assessed accuracy and found that data gaps, inaccurate co-registration, radiometric variability in sensor calibration, unmasked cloud, and shadows can drastically affect the results, compromising the application of breakpoint detection in mountainous areas of the Andean Amazon. Moreover, since breakpoint detection analysis of landscape variability in the Andean Amazon requires a unique calibration of algorithms, the time required to optimize analysis could complicate its proper implementation and undermine its application for large-scale projects. In exceptional cases when data quality and quantity were adequate, we recommend the pre-processing approaches, noise reduction algorithms and breakpoint detection algorithms procedures that can enhance results. Finally, we include recommendations for achieving a faster and more accurate calibration of complex functions applied to remote sensing using genetic algorithms.

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Increasing net ecosystem biomass production of Canada's boreal and temperate forests despite decline in dry climates

Hember

Kurz

Coops

2017

Global Biogeochemical Cycles

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Repeated measurements of tree biomass at field plots describe recovery from disturbances, sampling artifacts, and potential effects of environmental change on forest ecosystems. Challenges in differentiating between intrinsic and extrinsic sources of variation, both in theory and in practice, continue to confound claims of an anthropogenic carbon sink in forest biomass. Here we analyzed observations at 10,307 plots across southern ecozones of Canada to investigate temporal trends in stand-level biomass growth (G), biomass loss due to mortality (M), and net ecosystem biomass production (NEBP) of intact stands. Net extrinsic forcing (F ex ) was expressed by the collective dependence of biomass fluxes on climate anomalies, nitrogen deposition (N), and atmospheric carbon dioxide concentration (C). Inferences drawn directly from linear mixed-effects model coefficients only reflect the static behavior of the model specifically at field plot locations. We, therefore, defined a dynamic landscape-scale net extrinsic forcing (F ex 0 ), which additionally accounted for potential negative feedback responses to anthropogenic growth enhancement. Simulations were performed over 1501-2012 to estimate F ex 0 . Overall, F ex 0 was positive, suggesting that environmental changes drove a 90% increase in NEBP. The increase in NEBP was confined to wet regions, while the biomass sink in dry regions decreased, suggesting that large expanses of northern forests, historically located near the boundary between wet and dry climates, may be at high risk of decline under continued increases in evaporative demand. These results have important implications for the greenhouse gas balance of Canada's forest sector.

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An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites

Cited by 266 publications

References 48 publications

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms

Increasing net ecosystem biomass production of Canada's boreal and temperate forests despite decline in dry climates

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