Landsat Time Series Stacks Research Articles

Estimation of annual harvested wood products based on remote sensing and TPO survey data

ABSTRACT Accurate estimation of Timber Product Output (TPO) is important for carbon budget accounting, since wood products can act as delayed release carbon pools. However, the existing timber harvest data in the US relies on Forest Service’s TPO survey, and the survey does not happen every year. In this study, we proposed a methodological framework to produce annual TPO volume estimates for seven southeastern states (North Carolina, South Carolina, Alabama, Florida, Georgia, Mississippi, and Tennessee) of the US by integrating TPO survey data, Landsat Time Series Stacks (LTSS), and National Land Cover Database (NLCD). First, a forest disturbance product was derived based on Vegetation Change Tracker (VCT) algorithm using LTSS from 1985 to 2016. Then, by linking the predictor variables derived from the disturbance data and the TPO survey data, two regression algorithms were tested and compared, and Random Forest was selected to create TPO estimation models for different wood types. The results show that from 1986 to 2015, the region produced more than 5 × 109 m3 wood products, including 3.7 × 109 m3 softwood products and 1.6 × 109 m3 hardwood products. The derived TPO estimates had large spatial variations among the counties within each state as well as large temporal variations across the study period. The TPO data derived through this study can provide an observational basis for calculating the amount of C transferred from the standing biomass to the wood products through logging and for partitioning of harvested C among different wood product pools.

Forest Age Mapping Using Landsat Time-Series Stacks Data Based on Forest Disturbance and Empirical Relationships between Age and Height

Forest age is a critical parameter for the status and potential of carbon sequestration in forest ecosystems and reflects major forest disturbance information. However, reliable forest age data with high spatial resolution are lacking to date. In this study, we proposed a forest age mapping method with a 30 m resolution that considers forest disturbance. Here, we used the Landsat time-series stacks (LTSS) data from 1986 to 2021 and implemented the LandTrendr algorithm on the Google Earth Engine (GEE) platform to detect the age of disturbed forests. The age of non-disturbed forests was extracted based on forest canopy height data and the empirical relationship between age and height. High-resolution Google images combined with the forest management archive data of forestry departments and national forest inventory (NFI) data were used for the validation of disturbed and non-disturbed forest age, respectively. The results showed that the LandTrendr algorithm detected disturbance years with producer and user accuracies of approximately 94% and 95%, respectively; and the age of non-disturbed forests obtained using the empirical age–height relationship showed an R2 of 0.8875 and a root mean squared error (RMSE) value of 5.776 with NFI-based results. This confirms the reliability of the proposed 30 m resolution forest age mapping method considering forest disturbance. Overall, the method can be used to produce spatially explicit forest age data with high resolution, which can contribute to the sustainable use of forest resources and enhance the understanding of carbon budget studies in forest ecosystems.

Continuous Change Detection and Classification—Spectral Trajectory Breakpoint Recognition for Forest Monitoring

Forest is one of the most important surface coverage types. Monitoring its dynamics is of great significance in global ecological environment monitoring and global carbon circulation research. Forest monitoring based on Landsat time-series stacks is a research hotspot, and continuous change detection is a novel approach to real-time change detection. Here, we present an approach, continuous change detection and classification-spectral trajectory breakpoint recognition, running on Google Earth Engine (GEE) for monitoring forest disturbance and forest long-term trends. We used this approach to monitor forest disturbance and the change in forest cover rate from 1987 to 2020 in Nanning City, China. The high-resolution Google Earth images are collected for the validation of forest disturbance. The classification accuracy of forest, non-forest, and water maps by using the optima classification features was 95.16%. For disturbance detection, the accuracy of our map was 86.4%, significantly higher than 60% of the global forest change product. Our approach can successfully generate high-accuracy classification maps at any time and detect the forest disturbance time on a monthly scale, accurately capturing the thinning cycle of plantations, which earlier studies failed to estimate. All the research work is integrated into GEE to promote the use of the approach on a global scale.

Carbon fluxes from contemporary forest disturbances in North Carolina evaluated using a grid-based carbon accounting model and fine resolution remote sensing products

Land use/land cover change is a key component in terrestrial carbon cycle, yet there are still large uncertainties in the terrestrial carbon budget. To reduce such uncertainties and refine the spatial distribution of carbon flux, a 30-m Grid-based Carbon Accounting (GCA) model was proposed. We adapted a well-established bookkeeping model into a spatial-explicit model to utilize Landsat time series stacks and to calculate the carbon fluxes resulting from three types of forest disturbances including forest harvesting, forest-to-urban conversion, and fire. Our model results provide spatial details at sub-ha scale that are crucial for carbon management at individual landowner levels. Sensitivity analysis revealed that both pre-disturbance forest carbon and disturbance intensity had large impact on carbon flux estimates arising from forest disturbances that occurred between 1986 and 2010 in North Carolina. At the state level, forest harvesting and fire from 1986 to 2010 released 88.5 MT and 1.6 MT carbon respectively. During the same period, regrowing trees over the logged area absorbed 142.7 MT carbon while those over burned area absorbed 1.6 MT more. The net flux from harvesting, fire, and post-disturbance growth was −52.5 MT. Conversion of forest to urban resulted in a net source of 5.3 MT. Overall, the areas subject to the three types of disturbances and post-disturbance growth was a net sink of 47.2 MT carbon over the entire study period. While our modeling framework was tested at the 30 m spatial resolution in this study, it can be adapted for use with finer spatial and/or temporal resolution remote sensing products that will become more readily available in the coming years, thus further improve the carbon flux estimates.

Monitoring Spatial and Temporal Patterns of Rubber Plantation Dynamics Using Time-Series Landsat Images and Google Earth Engine

Rubber plantation is an important strategic material related to the national economy and people's livelihoods. Up-to-date and accurate rubber plantation maps are critical for monitoring the area and spatial distribution of rubber plantations and assessing their impacts on society, the economy, and the environment. However, existing optical images are greatly limited by frequent cloud cover, which seriously affects the accuracy of rubber plantation area extraction. To overcome this issue, we used dense Landsat time series stacks based on Google Earth Engine, combined phenological features, and applied random forest algorithms to monitor rubber plantations in Xishuangbanna from 1987 to 2020. The results showed that 1 ) the phenological characteristics of rubber plantations in Xishuangbanna indicated that the leaf-off period lasts from late December to mid-February of the following year, while the leaf-on of rubber plantations occurred in other months; 2 ) the overall accuracy and kappa coefficient values ranged from 0.82 to 0.96 and 0.76 to 0.95, respectively, showing that the extraction accuracy of rubber plantation information can meet the accuracy requirement; 3 ) the rubber plantation area in Xishuangbanna increased between 1987 and 2020 from 7.05×104 to 47.78×104 hm2. The peak rubber plantation area occurred in 2015 (49.60×104 hm2) followed by a downward trend; 4 ) spatially, the rubber plantation is mainly distributed in Jinghong City and Mengla County, while less abundant in Menghai County. Overall, this article is expected to contribute to the rapid and accurate mapping of rubber plantations in large-scale applications and analysis.

Mapping thins to identify active forest management in southern pine plantations using Landsat time series stacks

The southeastern United States is unique in terms of both the intensity and scale of forest management, which includes substantial thinning and other forms of harvesting. Because thinning is not a land use transition, and the disturbance signal is relatively subtle compared to a clear cut, there is a dearth of studies that attempt to detect thinning over large areas. Our goal was to detect pine thins as an indicator of active forest management using Landsat data. Areas which undergo thinning are indicative of active forest management in the region.Our approach uses a machine learning method which combines first-order harmonics and metrics from 3-year Fourier regression of Landsat time series stacks, layers from the Global Forest Change product, and other vetted national products into a random forests model to classify forest thins in the southeastern US. Forest Harvest Inspection Records for Virginia were used for training and validation. Models were successful separating thins from clear cuts and non-harvested pines (overall accuracy 86%, clear cut accuracy 90%, thin accuracy 83% for a simplified 10-predictor variable model).Examination of variable importance illustrates the physical meaning behind the models. The curve fit statistics (R2 or RMSE) of the NDVI, Pan, and SWIR1 harmonic curve fits, which are an indication of a departure from typical vegetation phenology caused by thinning or other disturbances, were consistently among the top predictors. The harmonic regression constant, sine and cosine from the Landsat 8 panchromatic band were also important. These describe the visible reflectance (500–680 nm) phenology over the time period at a high spatial resolution (15 m). The Loss Year from the Global Forest Change product, which is an indication of stand replacing disturbance, was also consistently among the most important variables in the classifiers.High performance computing, such as Google Earth Engine, and analysis-ready data are important for this approach. This work has importance for quantification of actively managed forests in a region of the world where production forestry is the dominant land disturbance signal and a significant economic engine.

Specific Bamboo Forest Extraction and Long-Term Dynamics as Revealed by Landsat Time Series Stacks and Google Earth Engine

Understanding the spatiotemporal dynamics of bamboo forests is of critical importance as it characterizes the interaction between forest and agricultural ecosystems and provides essential information for sustainable ecosystem management and decision-making. Thus far, the specific dynamics of moso bamboo (Phyllostachys edulis) and other bamboo are still unknown. In this study, we used temporal information extracted from Landsat time series stacks with Google Earth Engine (GEE) to characterize the spatiotemporal dynamics of bamboo forests, including moso bamboo and other bamboo, in Lin’an County, China, from 2000 to 2019. The bamboo forests were mapped in four periods: the early 2000s (2000–2004), the late 2000s (2005–2009), the early 2010s (2010–2014), and the late 2010s (2015–2019). The overall accuracy of these maps ranged from 97% to 99%. We then analyzed the spatiotemporal dynamics of the bamboo forests at the county and subdistrict/township scales, and probed the bamboo forest gain and loss with respect to the terrain features. Our findings show that bamboo forests increased by 4% from 2000 to 2014, followed by a sharp decrease of 13% in the late 2010s. The decrease was mainly caused by the loss of other bamboo. Approximately 69% of the bamboo forest gain occurred in non-bamboo forest areas, and the rest occupied non-forest areas. Bamboo forest loss was mainly due to conversion into orchard (59%) and forest plantation (22%). Compared to bamboo forest gain, bamboo forest loss was typically observed in areas with lower elevations and steeper slopes. Our study offers spatially explicit and timely insight into bamboo forest changes at the regional scale. The derived maps can be applied to study the drivers, consequences, and future trends of bamboo forest dynamics, which will contribute to sustainable ecosystem management.

Characterizing 40 years of natural Pinus sylvestris var. mongolica carbon stocks in Northeast China using stand age from remote sensing time series

ABSTRACTRemote sensing (RS)-assisted estimates of forest carbon stocks are essential for modelling carbon budgets at large scales. Research on the dynamic variations in forest biomass and carbon stocks is important to improve the sustainable use of forest resources and understand forest carbon budgets in China. The Honghuaerji region of the Inner Mongolia Autonomous Region is where Pinus sylvestris var. mongolica Litv. (MP) originated. MP forests contained the most important coniferous silvicultural tree species and were later introduced to the desert regions of northern China. However, there has been very little research on carbon stocks (vegetation and soil carbon) over time in the original MP forests. Our goal was to estimate the changes in the carbon stocks of the natural MP forests in the Honghuaerji region using a 40-year RS time series, forest survey data and laboratory data. First, we mapped MP forests and estimated forest ages based on Key Hole 9 and Landsat time series stacks from 1975 to 2015, and the estimated forest ages were shown to be consistent with the surveyed tree ages with a coefficient of determination (R2) of 0.96. Second, fifteen vegetation indices were evaluated as indicators of diameter at breast height (DBH). Of these, tasselled cap greenness provided the best relationships, and an RS-based DBH equation was constructed with an R2 of 0.65. Third, the carbon densities in the different ages of MP forests were obtained at regional scales. Finally, the dynamics of the carbon stocks in the natural MP forest ecosystem (including stems, branches, leaves, roots and soils) over 40 years were obtained. The results of this study indicated that the total MP forest ecosystem carbon stocks increased significantly from 7.36 ± 1.92 Tg C in 1975 to 11.52 ± 2.51 Tg C in 2015 at a rate of 1.42% year–1, among which vegetation carbon stocks increased 5.54% year–1 and soil carbon stocks increased 0.45% year–1. The results reveal a notable carbon increase in the MP forests in the Honghuaerji region over the past four decades and provide information on the role of natural forest conservation and management in China in mitigating global climate change.

An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks

Impervious surface dynamics have far-reaching consequences on both the environment and human well-being. The expansion of impervious surface is often spontaneous and conscious, particularly in fast developing regions. Thus, monitoring impervious surface dynamics with high temporal frequency in a both accurate and efficient manner is highly needed. Here, we propose an approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense time series stacks of Landsat data. First, a stable area mask based on image classification in the start and the end years is generated to remove pixels that are persistent or spatially irrelevant. The Continuous Change Detection (CCD) algorithm is then employed to determine the change points when non-impervious cover converts to impervious surface based on the property of temporal irreversibility. Finally, the CCD time series models are calibrated for pixels with no change or multiple changes. We apply and assess the proposed approach in Nanchang (China), which has been experiencing rapid impervious surface expansion during the past decade. According to the validation results, overall accuracies of image classification in the start and the end years are 97.2% and 96.7%, respectively. Our approach generates convincing results for impervious surface change detection, with overall accuracy of 85.5% at the annual scale, which is higher than three commonly used approaches in previous studies. At the continuous scale, the mean biases of the detected time of imperviousness emergence are +0.17 (backward) and −3.42 (forward) Landsat revisit periods (16 days) for pixels with one single change and multiple changes, respectively. The derived impervious surface extent maps exhibit comparable performances with five widely used products. The present approach offers a new perspective for providing timely and accurate impervious surface dynamics with dense temporal frequency and high classification accuracy.

Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico

The Sierra Madre Occidental (SMO) is the longest continuous mountain complex in Mexico and is characterised by high species diversity and a high proportion of endemism. The rate of deforestation is high in Mexico, as in other megadiverse countries, and protection of the country’s biodiversity is a top priority. Quantification of changes in vegetation cover is essential for this purpose. Temporal information is required to enable classification of vegetation cover and change processes. In this study, the disturbances that occurred in the temperate forest of the SMO in the State of Durango (Mexico) during the period 1986–2012 were quantified using Landsat Time Series Stacks (LTSS) and the Vegetation Change Tracker (VCT) algorithm. The results obtained confirmed that land cover changes were detected with high overall accuracy (97.6%). In order to analyze the forest losses corresponding to the only official data available in Mexico, we retrieved land use and vegetation mapping (USV) data from the Mexican National Institute of Statistics and Geography (INEGI). The aridity index was established and fragmentation analysis was carried out in the study area, showing that forest pests and forest fires were the principal disturbance events in the SMO of Durango, and that the climate greatly influenced the occurrence of disturbances. The LTSS-VCT analysis revealed that for the period 1986–2012, about 34% of the temperate forest cover in the SMO in Durango was lost due to different types of disturbance, representing an annual rate of loss of forest cover of 1.3% and affecting 32,840 ha of land per year. The trend analysis of USV data showed very similar changes to those indicated by the LTSS-VCT analysis in terms of loss of temperate forest. However, differences were observed in regards to the absolute values of forest cover and vegetation loss, with analysis of the USV data indicating forest losses of 28% due to disturbances and an annual disturbance rate of 1%, affecting 49,940 ha of land per year. The LTSS-VCT approach proved efficient for mapping data on forest disturbance acquired by a medium spatial resolution (Landsat) sensor in the SMO in the State of Durango, providing satisfactory results and at low cost.

Identifying Forest Impacted by Development in the Commonwealth of Virginia through the Use of Landsat and Known Change Indicators

This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding low density development within the Commonwealth of Virginia’s forests. Individual NDVI images were stacked by year for the years 1995–2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from development disturbances and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed forest from undisturbed forest. Disturbances, once identified, could be separated into Development Disturbances and Non-Development Disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Low density development disturbances of previous forest land cover had an F-measure, combining precision and recall into a single class-specific accuracy (β = 1), of 0.663. We compared our results to the NLCD 2001–2011 land cover changes from any forest (classes 41, 42, 43, and 90) to any developed (classes 21, 22, 23, and 24), resulting in an F-measure of 0.00 for the same validation points. Landsat time series stacks thus show promise for identifying even the small changes associated with low density development that have been historically overlooked/underestimated by prior mapping efforts. However, further research is needed to ensure that (1) the approach will work in other forest biomes and (2) enabling detection of these important, but spatially and spectrally subtle, disturbances still ensures accurate detection of other forest disturbances.

Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data

Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984–2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area—Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions.

A LANDSAT TIME-SERIES STACKS MODEL FOR DETECTION OF CROPLAND CHANGE

Abstract. Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the “true change” without overestimating the “false” one, while CVA pointed out “true change” pixels with a large number of “false changes”. The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

The effects of topographic correction and gap filling in imagery on the detection of tropical forest disturbances using a Landsat time series in Myanmar

ABSTRACTIn this study, we evaluated the effects of topographic correction and gap filling of Landsat Enhanced Thematic Mapper Plus (ETM+) images on the accuracy of forest change detection through a trajectory-based approach. Four types of Landsat time series stacks (LTSS) were generated. These stacks resulted from combinations of topographically corrected and uncorrected imagery combined with gap-filled and unfilled stacks. These combinations of stacks were then used as input into a trajectory-based change detection. The results of change detection from trajectory-based analysis using these LTSS were compared in order to assess the effects of both topographic correction and gap-filling procedures on the ability to detect forest disturbances. The results showed that overall accuracies of change detection were improved after gap filling (10.5% and 7.5%), but were only slightly improved after topographic correction (3.6% and 0.6%). Although the gap-filling process introduced some uncertainty that might have caused false change detection, the number of pixels whose detection of disturbance was enhanced after gap filling exceeded those detecting false change. The results also showed that the topographic correction did not contribute much to improve the change detection in this study area. However, topographic correction has a potential to increase the accuracy of change detection in areas of more rugged terrain and steep slopes. This is because a direct relationship between the slope of the topography with topographic correction and an enhanced detection of disturbance in pixels from year to year was observed in this study. For robust change detection, we recommend that a gap-filling process should be included in the trajectory-based analysis procedures such as the one used in this study where a single image per year is used to characterize change. We also recommend that in areas of rugged terrain, a topographic correction in the image pre-processing should be implemented.

Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987–2011)

Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker (VCT) model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks (LTSS). The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM+ scenes with different projections due to distinct data sources (Beijing remote sensing ground station and the USGS EROS Center). The validation results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 (38.3 %) and 1992 (56.2 %) in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis-registration errors of the image pairs. Major forest disturbance types existing in our study area were identified as agricultural expansion (39.8 %), urbanization (24.9 %), forest management practice (19.3 %), and mining (12.8 %). In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China’s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.

Spatial characteristics of early successional habitat across the Upper Great Lakes states

Creation and management of early successional forest (ESF) is needed to halt and reverse declines of bird species dependent on pioneering plant species or young forests. ESF-dependent bird species require specific structural forest classes and are sensitive to forest age (a surrogate for forest structure), patch size, proximity to patch edges, and the juxtaposition of forest age classes. To date, ESF conservation plans have relied on spatially inexplicit data, lacking patch and landscape metrics, to set habitat goals and to track habitat trends. In a previous study, we used Landsat time series stacks and a vegetation-change-tracker algorithm to track forest canopy disturbances and subsequent regrowth from 1990 to 2009 across the Upper Great Lakes Young Forest Initiative region. Based on canopy disturbance histories, we assigned forest age classes to forest classes of the National Land Cover Database of 2011. In the present study, we used this spatial product to assess areas, patch and edge metrics, and land protection statuses of deciduous-mixed forest and woody wetland age classes. We defined ESF using four 5-year-age classes (1–5, 6–10, 11–15, 16–20years old) and their aggregate (1–20years old) whereas forest >20years old was referred to as ‘persisting’. Aggregated across 5-year-age classes, ESF of deciduous-mixed forest covered 3.4% and 0.9% of Bird Conservation Regions (BCR) 12 (Boreal Hardwood Transition) and 23 (Prairie Hardwood Transition), respectively, whereas woody wetland ESF constituted 1.0% and 0.2% of the same BCRs. For both deciduous-mixed forest and woody wetlands, ESF often occurred in patches ⩾1ha, but most ESF also occurred near patch edges created by adjacencies with persisting forest. Most ESF fell on lands with an unprotected or unknown protection status regardless of forest class. Regionally, ESF covered less area, occurred in smaller patches and nearer to edges, and more often fell on lands of unprotected or unknown protection status in BCR 23 than in BCR 12. Our results advance ESF conservation by providing insight into spatial characteristics that influence habitat quality and by establishing a baseline for habitat management planning and monitoring.

Detection of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks

Abstract Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, {ETM} +, and {OLI} data. The methodology was applied to the ~ 29,000 km2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and {NDMI} based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.

Land Covermapping based on Landsat Time-series Stacks in Sanjiang Plain

Land Covermapping based on Landsat Time-series Stacks in Sanjiang Plain

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration’s (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer’s and user’s accuracies of 0.21 ± 0.06 to 0.38 ± 0.05 for insect disturbance, 0.23 ± 0.07 to 1 ± 0 for burned area and 0.74 ± 0.03 to 0.76 ± 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer’s and user’s accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32years and a determination coefficient (R (2)) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R (2) values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8Gkg, from 5.8Gkg in 1986 to 26.6Gkg in 2013, a total increase of 360%. For the persistent forest area since 1974, the forest AGB density increased from 15.72t/ha in 1986 to 44.53t/ha in 2013, with an annual rate of about 0.98t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.