Landsat Archive Research Articles

Human–elephant conflict and land cover change in Sri Lanka

Human–elephant conflict (HEC) is a key environmental issue in number of Asian countries, including Sri Lanka. Incidents of HEC have significantly increased in Sri Lanka between 1991 and 2018, with 1734 human deaths reported in this period (281% increase), 4837 elephant deaths (1172% increase), 1053 human injuries (140% increase) and more than 23,000 property damage reports (1406% increase). In this study we present a Sri Lanka wide analysis to explore the role of land use and land cover change (LULCC) in relation to HEC, using official government data and a land cover change dataset (1993–2018) recently developed by the authors using satellite imagery from the Landsat archive. We investigated rates of HEC over time and compared these to rates of LULCC over the same period. We also present spatial analytics of HEC and LULCC, as well as determining hotspots of HEC and LULCC using a kernel density estimator. Annual HEC incidents were found to broadly increase in line with land use change events (r = 0.43, p < 0.05). Human deaths, elephant deaths, human injuries and property damage hotspots show distinct spatial patterns: human deaths and injuries being more concentrated in the North West, Polonnaruwa and Ampara, wildlife regions; while elephant deaths are spread throughout the HEC region and property damage is high in the Central, Polonnaruwa Anuradhapura, North West, and Southern wildlife regions. We found a strong negative correlation between HEC location and distance to LULCC events. In total, 98% HEC occurred within 1 km of an area that experienced recent LULCC Since 2017, the primary HEC hotspots have shifted to the south and east of the country in concert with LULCC. These countrywide perspectives could help inform HEC mitigation strategies in Sri Lanka and other countries facing similar human-wildlife challenges.

High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: A step toward discharge estimation in ungauged basins

The reliance on ground-based measurements has been a long-standing barrier toward deriving river discharge for ungauged headwaters, which are quite sensitive to climate change due to relatively high contributions of snow and glacier meltwater to river discharge. In addition, information pertaining to discharge is scarce and inconsistent across the globe due to economic, geographic, and political reasons. Therefore, understanding spatiotemporal dynamics of river discharge in ungauged headwaters is extremely challenging, particularly in high-mountain regions such as the Tibetan Plateau (TP). This study presents a methodology of estimating daily continuous discharge using multisource remote sensing and a hydrological model that can simulate cryospheric processes including snow and glacier accumulation and melt. The hydrological model is forced and calibrated purely by satellite-observed information, particularly by river widths derived from high-spatial-resolution images without using in situ river discharge measurements. Poorly gauged or ungauged headwaters with river widths on the order of 100 m, including the Lhasa (the tributary of the upper Brahmaputra River), Salween, Mekong, and Yangtze rivers originating on the TP were chosen to test the methodology. At-a-section river widths were derived from high-resolution satellite images (i.e., IKONOS, QuickBird, RapidEye-1/2/4/5, GeoEye-1, and WorldView-2) and Landsat archives. As exemplified by the Nash–Sutcliffe efficiency coefficient values (NSE) (up to 0.8 and higher than 0.5 overall), simulated daily continuous discharge was reliable in the four testbeds with different river bathymetry, narrow river widths, and complex terrain. Uncertainties associated with the accuracy, number, and variability of calibration references (i.e., remotely sensed widths) and optimization algorithms were fully discussed. This study highlights the potential of deriving daily continuous discharge without a priori information on river flow, paving the way for discharge estimation in ungauged headwaters globally and providing important implications for satellite-based discharge estimation in narrow rivers across high-mountain regions.

The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results

Recent advances in Landsat archive data processing and characterization enhanced our capacity to map land cover and land use globally with higher precision, temporal frequency, and thematic detail. Here, we present the first results from a project aimed at annual multidecadal land monitoring providing critical information for tracking global progress towards sustainable development. The global 30-m spatial resolution dataset quantifies changes in forest extent and height, cropland, built-up lands, surface water, and perennial snow and ice extent from the year 2000 to 2020. Landsat Analysis Ready Data served as an input for land cover and use mapping. Each thematic product was independently derived using locally and regionally calibrated machine learning tools. Thematic maps validation using a statistical sample of reference data confirmed their high accuracy (user’s and producer’s accuracies above 85% for all land cover and land use themes, except for built-up lands). Our results revealed dramatic changes in global land cover and land use over the past 20 years. The bitemporal dataset is publicly available and serves as a first input for the global land monitoring system.

A global map of planting years of plantations

Plantation is an important land use type that differs from natural forests and affects the economy and the environment. Tree age is one of the key factors used to quantify the impact of plantations. However, there is a lack of datasets explicitly documenting the planting years of global plantations. Here we used time-series Landsat archive from 1982 to 2020 and the LandTrendr algorithm to generate global maps of planting years based on the global plantation extent products in Google Earth Engine (GEE) platform. The datasets developed in this study are in a GeoTIFF format with 30-meter spatial resolution by recording gridded specie types and planting years of global plantations. The derived dataset could be used for yield prediction of tree crops and social and ecological cost-benefit analysis of plantations.

The use of Remote Sensing and GIS Methodology in the Analysis of Urualla Gully Erosion site Imo State Nigeria

The study of Urualla gully erosion is currently undermined by the inherent costs associated with consistent field monitoring and the lack of historic measurements to perform time series analysis. Remote sensing methodologies, via the Landsat archive, are used as a low-cost data source to allow analyses of gullies over the time period 2006 to 2021. In conjunction with longterm environmental variables, the Landsat data is used to establish land cover changes over the time period, via pixel-based classification, to identify its role in gully development. Aiming to link environmental characteristics and land cover changes with Urualla gully development and its rates of change, Digital Elevation Models (DEM) and remote sensing imagery are used to detect topographical and landscape characteristics and to calculate gully dimensions. Landscape analysis over the study period reveals a steady increase in Gully/Open land. The increasing area of Urualla gully consistently correlates with vegetation loss, (r= -0.97 (p&lt;0.05)) and also, when correlated with Built-Up Area over the same period of time, the correlation shows (r= 0.97 (p&lt;0.05). Analysis of study area topography at 30m resolution reveals that Urualla gully site developed on high slope of more than 400 . The study offers a method of monitoring Urualla gully development from early stage to maturity and exemplifies the complexity and variability of erosion drivers in the study area. It presents a verified approach in the monitoring of gullies, enacted through use of low budget/computing cost remote sensing and classification technologies, and serves to embolden civilian and governmental efforts to manage the societal and environmental menace of gully erosion.

Coppice loss and persistence in Germany

Coppicing is an ancient form of forest and woodland management. Coppice harbours important biodiversity and has a high cultural value, but has been transformed into high forest, neglected and abandoned over vast areas of Central Europe during the past century. Hotspots of remaining, active coppice systems are often under the radar of national and international forest and conservation authorities, because they are managed as socio-ecological systems by close-knit village communities. A better spatial overview of active and neglected coppice, and a quantification of trends in coppice management are important to allocate restoration and conservation priorities and identify potential areas for future coppice restoration.I quantified trends in coppice area across Germany, a country with a historically large proportion of coppice, over the past century. I studied temporal and spatial patterns in management and transformation in a remaining hotspot of coppice without standards (‘simple coppice’) harnessing the Landsat archive and very high resolution, publicly available satellite images. I supplemented the remote sensing analysis by a review of published and unpublished, grey literature.Across Germany, the area of active coppice with standards declined by 98.7% between 1927 and 2020, from ca. 400,000 ha to ca. 5,000 ha. The area of simple coppice declined from an estimated 680,000 ha to ca. 6,400 ha in the same period, i.e. by 99.1%. The area of overage (>35 years uncut) and abandoned coppice is difficult to define and quantify, but it likely exceeds 100,000 ha across Germany. Active coppice, dominated by oak (Quercus spp.) and birch (Betula spp.), is now found only in three regions. However, only in the federal state of Hesse, intact coppice landscapes have survived, comprising an area of ca. 2,000 ha. Here, traditional coppice management with relatively short rotation cycles and large coupes is maintained, with a stable area cut annually since 1986 and no coppice transformation to high forest since the 1970s. There was statistical evidence that the heating oil price influences the area that was cut annually, suggesting some potential for coppice restoration in the coming years, given rising energy prices.As a conclusion, intact coppice systems have virtually disappeared in Germany, and with them their unique biodiversity and cultural heritage. There is an urgent need to safeguard the remaining socio-ecological systems, to develop incentives to maintain current active coppice and to develop integrative approaches to restore abandoned coppice, as a small-scale alternative to commercial forestry and close-to-nature silviculture.

How community forest management performs when REDD+ payments fail

The reduced emissions in deforestation and degradation (REDD+) initiative uses payments for ecosystem services as incentives for developing countries to manage and protect their forests. REDD+ initiatives also prioritize social (and environmental) co-benefits aimed at improving the livelihoods of communities that are dependent on forests. Despite the incorporation of co-benefits into REDD+ goals, carbon sequestration remains the primary metric for which countries can receive payments from REDD+, but after more than 10 years of REDD+, many site-specific programs have failed to complete the carbon verification process. Here, we examine whether the REDD+ social co-benefits alone are sufficient to have slowed deforestation in the absence of carbon payments on Pemba, Tanzania. Using satellite imagery (Landsat archive), we quantified forest cover change for the period before (2001–2010) and after (2010–2018) the launch in 2010–2011 of Pemba island’s REDD+ readiness project. We then compared rates of forest cover change between shehia (administrative units) that were part of REDD+ readiness intervention and those that were not, adjusting for confounding variables and the non-random selection of REDD+ shehia with a statistical matching procedure. Despite considerable variation in forest outcomes among shehia, the associated co-benefits with the Pemba REDD+ project had no discernible effect on forest cover change. Likewise, we did not detect an effect of socioecological covariates on forest cover change across all shehia, though island-wide human population growth since 2012 may have played a role. These findings are unsurprising given the failure to secure carbon payments on Pemba and indicate that co-benefits alone are insufficient to reduce deforestation. We conclude that better oversight of all-involved parties is needed to ensure that REDD+ interventions satisfactorily conclude the process of securing a mechanism for carbon payments, if slowing deforestation is to be achieved.

Soil moisture content retrieval from Landsat 8 data using ensemble learning

Although detailed spatial and temporal distribution of soil moisture is crucial for numerous applications, current global soil moisture products generally have low spatial resolutions (25–50 km), which largely limit their application at local scales. In this study, we developed a high-resolution soil moisture retrieval framework based on ensemble learning by integrating Landsat 8 optical and thermal observations with multi-source datasets, including in-situ measurements from 1,154 stations in the International Soil Moisture Network, the Soil Moisture Active Passive (SMAP) soil moisture product, the ERA5-Land reanalysis dataset, and auxiliary datasets (terrain, soil texture, and precipitation). Two widely used ensemble learning models were explored and compared using ten-fold cross-validation. The extreme gradient boosting (XGBoost) model performed slightly better than the random forest (RF) model, with a root mean square error (RMSE) of 0.047 m3/m3 and correlation coefficient (R) of 0.952, respectively. Further validation using data from four independent soil moisture networks demonstrated that the prediction accuracy of the XGBoost model was comparable to the SMAP soil moisture product, but with a much higher spatial resolution. The model was finally used to map soil moisture over the high-altitude Tibetan Plateau, which is especially sensitive to climate change, from May to September of 2015. The comparison between our fine-scale soil moisture map at 30 m resolution and the coarse-scale SMAP soil moisture product (36 km) revealed high spatial consistency. These results suggest that there is potential to generate accurate soil moisture products globally at 30 m spatial resolution from the long-term Landsat archive. This finding has practical implications in scenarios requiring fine-scale soil moisture maps, such as climate change and permafrost modeling, hydrological and land surface modeling, and agriculture monitoring.

Regional-scale fire severity mapping of Eucalyptus forests with the Landsat archive

Fire managers seek accurate methods to map fire severity for forest management efforts. Fire severity data with wide spatial and temporal coverage enhances understanding of fire's impact on vegetation and improves planning to mitigate the risk of destructive wildfires. In this study, we develop a machine learning workflow that converts a spatial database of known fire events into a collection of gridded fire severity maps representing levels of canopy scorch due to wildfires and planned fuel reduction burns (prescribed burns). This workflow uses a dataset of observed fire severity interpreted from post-fire aerial images (3–7 cm spatial resolution; ~11,649 labels from 64 unique fires) to train and test a supervised classifier that predicts fire severity using 13 Landsat-derived spectral indices. For a target fire, the classifier generates a pixel-level prediction of fire severity with classes belonging to one of five ordinal categories ranging from unburnt, three levels of canopy scorch, and canopy consumed by fire. The classifier's accuracy was 84.2% (Kappa score = 0.799) and testing was undertaken across multiple fire types (wildfire / prescribed burn), seasons, forest types and topography. MODIS thermal anomalies and a MODIS-derived burn area product were used to augment the fire severity database with start- and end-of-fire dates to enable accurate delineation of pre- and post-fire Landsat imagery; this method increased model accuracy from 78.0% (Kappa = 0.72). The model was then applied to a regional 16-year (2005 to 2020) history of fire events (n fires = 713, ~1Mha) in the sclerophyll eucalypt forests and woodlands of the Northern Jarrah Forest, Western Australia. On fires with MODIS-adjusted start and end dates, high severity fire (>80% canopy scorch) was predicted more often in wildfires (67.9%, 124900 ha) compared to prescribed burns (14.6%, 110300 ha). However, the total predicted area of high-severity fire was comparable between fire types due to prescribed burns being more frequent on the landscape. We provide the first fire severity model in Western Australia that is broadly calibrated to both prescribed burns and wildfires across multiple conditions. Our model outputs are made freely available and offer opportunities to better understand the interactions between prescribed burns and wildfires, and the effects of fire severity on the environment.

Simple Method to Extract Lake Ice Condition From Landsat Images

Ice plays key roles in regulating hydrological, ecological, biogeochemical, and socioeconomic functions of lakes. Long-term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> lake ice phenological records indicate that lake ice is trending toward later freeze-up, earlier breakup, and a shorter ice duration. Parallel to study of lake ice using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> records and process-based models, satellite remote sensing can expand our understanding of lake ice change over large spatial scales. However, most remote sensing studies have focused on large lakes or short periods of time, which may not robustly represent changes over multidecadal time periods or in the much more numerous small lakes. Here, we present a random forest model, Sensitive Lake Ice Detection (SLIDE), to accurately extract ice conditions from Landsat TM, ETM+, and OLI images. We trained the model using a manually labeled lake ice dataset (1089 labeled areas over 995 lakes globally). Our results show that our model achieves accurate classification between ice/snow and water (accuracy: 97.8%, kappa coefficient: 95.5%). Comparing Landsat-derived ice cover with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> ice conditions, we show that our model produces less bias, lower RMSE, and higher kappa than does the Landsat snow/ice flag from the quality assessment band. This is especially true during the transitional period surrounding the ice on and off dates reported from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> (mean bias −7.3% from our model, −17.3% from the Landsat quality band). Our results demonstrate the feasibility of mining the rich Landsat archive to study lake ice dynamics and of better flagging ice-affected lake observations.

Geomorphic insights into Australia's coastal change using a national dataset derived from the multi-decadal Landsat archive

Australia has a vast, diverse and dynamic shoreline, and national perspectives on the mechanisms, magnitude and trajectories of coastal adjustment are required to underpin informed management. Geoscience Australia's national Digital Earth Australia Coastlines (DEA Coastlines) was developed to help address this need. These datasets were derived by the application of sub-pixel analysis techniques to the Landsat archive to provide annual coastal contours for the years 1988–2019, and accompanying national point datasets provide change statistics at two scales (30 m and 5 km alongshore spacing). This study examines the utility of these three datasets for understanding temporal and spatial patterns of geomorphic change for representative case examples around Australia. These examples have been selected to demonstrate DEA Coastlines capability for examining the dynamics of Australia's diverse shorelines, from the mangrove-fringed, macrotidal systems of north-western Australia to the microtidal clastic barrier systems of the south-east. We examine this capability at tertiary to sub-tertiary coastal compartment (TCC; sediment cell) spatial scales. While some TCC are strongly and consistently net-progradational or net-erosional, most TCC demonstrate alongshore variations in trajectories and rates of change. We use these patterns of change to infer sub-TCC sediment pathways, and examine the temporal trends contained within representative point datasets to link changes to known process drivers. DEA Coastlines also captures diminishing widths of coastal barriers and has the potential to be combined with more detailed site analyses to forecast future trajectories of change. There are, however, some limitations to DEA Coastlines. Owing to the annual scale of the analysis used to develop the datasets, rapidly moving intertidal bars can introduce considerable error, and an uncertainty field successfully flags most of these locations. Other overlapping and complex coastline contours can sometimes be unpacked into more discrete phases to provide a richer understanding of non-linear coastal trends, and these examples highlight the need to simultaneously consider both of the DEA Coastlines 30 m point statistic and contour datasets. The Australian DEA Coastlines dataset thus provides nationally consistent perspectives for improved coastal management, and has been made freely available online to support a broad range of end users. While these datasets are presently exclusive to the Australian coast, the approach also lends itself to future application to the global Landsat archive.

Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive

Remote sensing provides full-coverage and dynamic water quality monitoring with high efficiency and low consumption. Deep learning (DL) has been progressively used in water quality retrieval because it efficiently captures the potential relationship between target variables and imagery. In this study, the multimodal deep learning (MDL) models were developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a), total phosphorus (TP), and total nitrogen (TN) in Lake Simcoe, Canada. Since TP and TN are non-optically active, their retrievals were based on the fact that they are closely related to the optically active constituents (OACs) such as Chl-a. We trained the MDL models with one in-situ measured data set (for Chl-a, N = 315, for TP and TN, N = 303), validated the models with two independent data sets (N = 147), and compared the model performances with several DL, machine learning, and empirical algorithms. The results indicated that the MDL models adequately estimated Chl-a (mean absolute error (MAE) = 32.57%, Bias = 10.61%), TP (MAE = 42.58%, Bias = −2.82%), and TN (MAE = 35.05%, Bias = 13.66%), and outperformed several other candidate algorithms, namely the progressively decreasing deep neural network (DNN), a DNN with trainable parameters similar to MDL but without splitting input features, the eXtreme Gradient Boosting, the support vector regression, the NASA Ocean Color two-band and three-band ratio algorithms, and another empirical algorithm of Landsat data in clear lakes. Using the MDL models, we reconstructed the historical spatiotemporal patterns of Chl-a, TP, and TN in Lake Simcoe since 1984, and investigated the effects of two water quality improvement programs. In addition, the physical mechanism and interpretability of the MDL models were explored by quantifying the contribution of each feature to the model outputs. The framework proposed in this study provides a practical method for long-term Chl-a, TP, and TN estimation at the regional scale.

A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p &lt; 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.

Surface Water Dynamics and Rapid Lake Drainage in the Western Canadian Subarctic (1985–2020)

AbstractThe area and distribution of surface water are shifting rapidly in many regions across the circumpolar Arctic. In this study, we explore the effects of climate and terrain factors on the area of lakes in the Northwest Territories, Canada. We used the Landsat satellite archive to map interannual changes in 5,328 lakes and ponds in the Lower Mackenzie Plain between 1985 and 2020. The high temporal resolution of our dataset allowed us to classify gradual and abrupt changes in lake area and identify rapid drainage events. We used Generalized Additive Models and Random Forests to test the effects of climate and terrain factors on changes in lake area. Despite increases in the area of smaller lakes driven by increasing precipitation, we found that the total lake area has decreased by approximately 1%. Overall, 29% of lakes exhibited an increasing trend in the area, while 11% exhibited a decreasing trend, and the majority of these changes (65%) were non‐linear in nature. Lakes located in fire scars were also 3.8 times more likely to show a decreasing trend in area. Analysis of a large fire indicates that lakes within the burned region exhibited declines in an area that persisted until the end of the study period 20 years after the fire. These declines are likely related to the impact of fire on thaw depth, groundwater connectivity, and the development of new drainage pathways. Our results highlight the importance of rapid drainage and wildfire as drivers of declines in the lake area.

Ecological potential fractional component cover based on Long-Term satellite observations across the western United States

Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe, annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine

Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions.

Impacts of Dam Operation on Vegetation Dynamics of Mid-Channel Bars in the Mid-Lower Yangtze River, China

Vegetation dynamics on mid-channel bars (MCBs) is essential for supporting ecosystem functions and associated services in river systems, especially in dammed large rivers. Generally, there are two possible changing patterns that vegetation of MCBs downstream a dam would experience. On one hand, the vegetation area may shrink because of a decrease in the MCB area in the post-dam period, which has been observed in many rivers around the world. On the other hand, the MCB vegetation area may expand because flood disturbances would be weakened by dam operation. However, little evidence has been reported to clarify such confusion. Therefore, vegetation dynamics of MCBs in the mid-lower Yangtze River downstream the Three Gorges Dam (TGD; the world’s largest dam) is selected as a case study to address the issue. Using long-term (1987–2017) Landsat archive images, this study reveals the spatiotemporal variations of vegetation area change intensities (VACIs; indicated by dynamic trends) on MCBs in the mid-lower Yangtze River. Results show that an overall VACI in the post-dam period (2003–2017) is about three times faster than that in the pre-dam period (1987–2002). In other words, the rate of vegetation colonization accelerated after the TGD operation began in 2003. Moreover, the VACIs in the post-dam period are size dependent, where large size MCBs are likely to gain higher VACIs: Small-sized MCBs (0.33 km2/yr), medium-sized MCBs (1.23 km2/yr), large-sized MCBs (1.49 km2/yr). In addition, VACIs of individual MCBs in the post-dam period are distance dependent, where the further a MCB was from the TGD, the higher the VACI. It is also suggested that the weakened flood disturbances in the post-dam could explain the rapid vegetation growth and colonization. This work is not only beneficial for managing and protecting MCBs downstream the TGD after its operation, but is also helpful in understanding vegetation dynamics of MCBs in other dammed river systems around the world.

Detection of thermokarst lake drainage events in the northern Alaska permafrost region

The rapidly warming Arctic climate is reducing the stability of near-surface permafrost, and the thawing of ice-rich permafrost causes landscape changes known as thermokarst processes. Growing evidence suggests an increasing trend in the frequency and magnitude of thermokarst lake drainage events, which would significantly alter topography and hydrology, affecting ecosystem stability and carbon cycling. Dynamic monitoring of thermokarst lakes through satellite imagery remains a challenging task, as current temporal trend analysis methods have difficulty in accurately detecting when thermokarst lake drainage events occur. In this study, to improve the detection of time series breakpoints, an advanced temporal segmentation and change detection algorithm developed for forest change detection was, for the first time, transposed to monitor thermokarst lake dynamics. Moreover, to filter out spurious signals caused by fluctuations in lake area, we developed a hybrid algorithm to validate the detected thermokarst lake drainage events at the pixel-level and lake object-level, respectively. The method developed in this study demonstrates its effectiveness in detecting thermokarst lake drainage events in Arctic permafrost ecosystems and the potential to monitor the evolution of thermokarst landscapes using Landsat archive. A time-series analysis of changes in the thermokarst lake region of northern Alaska since 2000 using all available Landsat continuous data was performed on the Google Earth Engine platform. In total, 90 drainage lakes larger than 5 ha in size were detected in our study area, nearly a third of which were almost completely drained. As thermokarst lakes drainage represent hotspots of permafrost degradation, we publicly share information on these drained lakes to help select more targeted sites for costly fieldwork and validation activities. This study provides a basis for understanding and quantifying thermokarst lake dynamics in the Arctic permafrost region, which will contribute to the goal of integrating thermokarst processes into earth system models.

Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine

Most of the worlds’ population relies on the processes and ecosystems in the coastal zone. Understanding the long-term change of coastlines is critical for the effective management of these complex, and heavily utilised regions. There has been a recent increase of studies focused on large-scale shoreline change mapping. However, most current methods are optimized for extracting shorelines of wave-dominated sandy beaches, which are only 30% of the global coasts, resulting in uncertainty for other environments such as tidal flats and bedrock. Here, we propose a new shoreline change mapping workflow, using the Landsat archive and Google Earth Engine, which increases compute efficiency and is suitable for retrieving shoreline changes for various coastal landforms at high tide instead of mean sea level. By validating against regional and continental datasets in Australia, we found the approach here produced high mapping accuracy and showed particularly better performance at tide-dominated coasts, where tidal flats and intertidal bars and ridges are present, when compared to past approaches. This is an important step forward since tide-dominated and tide-modified coasts are widely distributed at tropical low latitudes. We also explored the global application of the proposed method and derived hotspots of shoreline erosion and accretion that agreed with multiple regional studies across the world. Most of these hotspots were related to river sediment discharge and human intervention on the coast, as expected. Although it requires further validation, the global application of our method demonstrates the significance of this approach in identifying potential threats to coastal zones, especially in complex tide-dominated environments, which can facilitate effective coastal management.