Landsat Time Series Research Articles

Spatio-temporal Dynamics of Forest Cover in Mid-Upstream Basins of Taedong River in DPR Korea Using Landsat Time Series

Spatio-temporal Dynamics of Forest Cover in Mid-Upstream Basins of Taedong River in DPR Korea Using Landsat Time Series

Assessment of environmental degradation of lakes of Nainital district: an ecohydrological perspective

Human activities have degraded lakes in Uttarakhand, endangering their vital role in urban sustainability, which includes providing essential services like water supply, flood mitigation, agriculture support, and biodiversity conservation in the Himalayan region. This study focused on Nainital district lakes, utilizing remote sensing and GIS techniques to assess their condition. Time series Landsat 8 satellite imageries acquire by USGS earth explorer from 2017 and 2022 were captured, pre-processed, and subjected to spectral-based classification algorithms in ArcGIS software to calculate Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), and Normalised Difference Built-up Index (NDBI) indices to assess changes in vegetation, water bodies, and build-up area in and around the lakes respectively. The results indicate a decrease in built-up areas for Nainital district lakes from 2017 to 2022: Naini Lake (1.42%), Bhimtal Lake (1.83%), Naukuchiatal Lake (1.45%), Sattal Lake (2.18%), Khurpatal Lake (2.25%), and Sariyatal Lake (1.3%). Additionally, Bhimtal, Naukuchiatal, and Khurpatal lakes exhibited reductions in shrub and grass vegetation by approximately 12%, 16%, and 0% over the five-year period. Notably, Sattal and Khurapatal lakes demonstrated significant decreases in built-up areas, likely attributed to restoration efforts or landslides. Findings emphasize the need for conservation, sustainable land-use practices, and effective management to protect lake ecosystems.

Fully automatic training sample collection for detecting multi-decadal inland/seaward urban sprawl

Accurate and timely monitoring of urban sprawl using remote sensing provides valuable insights for urban management and sustainable development. While existing studies have explored methods for large-scale monitoring of urban sprawl, most of them rely on the collection of manually labeled training samples for supervised classification. Additionally, previous studies generally focused on inland urban sprawl, neglecting the process of urban sprawl towards sea areas, known as seaward urban sprawl, which has profound ecological implications for coastal ecosystems. Here we develop a fully automatic algorithm for detecting urban sprawl, named Fully Automatic Detection of Urban Sprawl (FADUS), using the time-series Landsat imagery without manually collecting training samples or existing urban maps for reference. FADUS automatically generates initial samples through a sequential binary unsupervised clustering technique and then updates them by removing the spectral outliers to obtain a high-quality training set for subsequent supervised classification. A tidal module is included in FADUS to avoid pseudo-urban changes caused by tidal fluctuations so that both inland and seaward urban sprawl can be detected. By applying the algorithm to 75 coastal cities in China, the most rapidly urbanizing region in the world, we uncovered a neglected but dramatic seaward urban sprawl process that has converted sea areas to urban space since 1985. Our results indicate that 9904.36 km2 of coastal wetlands and nearshore seawater areas have been converted to land area, of which 44.83% is currently covered by built-up area. With our method, the fully automatic detection of urban dynamics is now possible for both coastal and inland cities worldwide. Our derived map of seaward urban sprawl in China provides valuable references for coastal land-cover change monitoring, wetland protection and restoration, and integrated coastal management.

Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies.

Mapping of Ecological Environment Based on Google Earth Engine Cloud Computing Platform and Landsat Long-Term Data: A Case Study of the Zhoushan Archipelago

In recent years, with the rapid advancement of China’s urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas.

Temporal and spatial analysis of vegetation cover change in the Yellow River Delta based on Landsat and MODIS time series data.

Based on the Landsat normalized difference vegetation index (NDVI) and the NDVI product of MODIS, this study synthesized two kinds of time-series images. The features were selected according to the characteristics of the time series, and the random forest algorithm was used for classification. Based on the classification results and GIS spatial analysis, the temporal and spatial changes in vegetation cover in the Yellow River Delta from 2000 to 2020 were studied. The results showed that from 2000 to 2020, the vegetation first increased and then decreased, and the dynamic degree of land cover change was generally low. The monthly average minimum NDVI values during the vegetation growth period mostly occurred before 2010, and the maximum values occurred after 2010. From the spatial perspective, the average vegetation area of the Yellow River Delta accounted for 31.54% of the total study area; specifically, the spatial pattern of vegetation distribution was relatively fixed, and the fixed vegetation area accounted for 63.90% of the total vegetation area. The spatial distribution had significant differences, and the vegetation was distributed radially from the center of the Yellow River to the periphery, with significant fragmentation found outside the watershed. The Yellow River had a strong interference with vegetation growth, and the stable vegetation distribution areas were concentrated near the Yellow River. The correlation coefficient between vegetation distribution and the location of the Yellow River was - 0.9964.

Monitoring of subpixel impervious surface dynamics using seasonal time series Landsat 8 OLI imagery

Since impervious surface is one of the core urban ecological indices, its high-precision time series monitoring at regional or global scales is highly desirable. Spectral features, spatial features, and temporal features have been successfully applied to regional impervious surface monitoring. However, few studies focus on the extraction of optimal features for subpixel impervious surface monitoring at the regional or global scales. In order to solve the above problem, this study proposes a new method for subpixel time series impervious surface estimation based on optimal spectral-temporal features. The proposed method first extracts various spectral indices from time series Landsat imagery to construct a spectral-temporal feature library. A feature scoring model is then established to optimize spectral-temporal features from the feature library. Based on the optimal spectral-temporal features, a modified semi-supervised Fuzzy C-means (FCM) method is proposed to identify stable pixels and extract endmembers from stable pixels. Finally, the endmembers are applied to the Fully Constrained Least Squares (FCLS) for time series impervious surface abundance extraction. The optimal spectral-temporal features can effectively distinguish impervious surfaces from pervious surfaces through feature optimization selection, and improve the feature utilization performances, thereby obtaining high-precision impervious surface mapping. The proposed method is applied in Wuhan, China, from 2013 to 2021, using seasonal time series Landsat 8 OLI images. Experimental results show that the impervious surface area of Wuhan expands from 567.27 km2 in winter 2013 to 1056.95 km2 in winter 2021 at the pixel scale. The impervious surface area expands from 545.80 km2 in winter 2013 to 921.01 km2 in winter 2021 at the subpixel scale. The subpixel impervious surface monitoring effectively solve the overestimation and underestimation problem of the pixel-scale impervious surface monitoring. This study characterizes the long-term dynamics of impervious surfaces at the subpixel scale and can provide reliable urban growth information for evaluating the impact of urbanization on the ecological environment.

Planning for cooler cities in Ghana: Contribution of green infrastructure to urban heat mitigation in Kumasi Metropolis

This study investigates the spatial variability of some remote sensing indices representing built-up areas, vegetation, bareness, and urban heat island (UHI), based on time-series Landsat TM/ETM+ and OLI/TIRS datasets archived for 1980–2020 period from the US Geological Survey’s website for Kumasi Metropolitan Area in Ghana. Modules for Land Use Change Evaluation (MOLUSCE) and Cellular Automata Artificial Neural Network (CA-ANN) algorithms and simulations in QGIS were used to predict future changes (2020–2050) for land-use systems in Kumasi. Findings revealed urbanization/built-up areas (+108.02%) contributed massively to the decline of forest areas (−93.34%) and farmlands/shrubs (−31.53%), thereby making Kumasi lose its once critical green position as the “Garden City of West Africa.” UHI moderately and strongly correlated positively against built-up (R2=0.78, p < 0.0001) and bareness (R2=0.96, p < 0.0001) indices, respectively. By contrast, UHI showed a statistically significant inverse relationship with the vegetative index (R2=0.97, p < 0.0001). Future land-use scenarios revealed more forests, waterbodies, and farmlands/shrubs will be lost, influencing urban temperature and water supply. The multipurpose advantages of urban green space are ingrained in the grand urban model. Contextually, the Metropolis’s resilience has been hampered by inconsistency in the performance of institutional roles, competition for land ownership rights over green areas, and little investments or prioritization of green spaces. An integrated collaborative governance framework is proposed to unify actions, address power crisis and factors that influence governance of green infrastructure, UHI and land cover change.

Sustainable restoration in sandy lands is challenged by the divergent responses of grassland functional types to disturbance

The effective management of regional grassland degradation requires a deep understanding of how different grassland types respond to climate change and human disturbance over different spatial and temporal scales. This study examined the dynamics and underlying driving mechanisms of various grassland types in the Otindag Sandy Land over 36 years (1986–2021) by integrating time-series Landsat images and extensive climate and socio-environmental data. We found that the dynamics and driving factors of different grassland types varied significantly. In low-cover grasslands, the area of desert grassland fluctuated with a decreasing trend after the breakpoint, while that of sandy grassland increased continuously without any breakpoints. In high-cover grasslands, the area of typical grassland only increased after the breakpoint, whereas that of temperate savanna grasslands first decreased and then increased insignificantly. Additionally, the desert grassland area was significantly negatively correlated with annual precipitation (AP), whereas the sandy grassland area was negatively correlated with annual mean wind speed (AMWS). The dynamics of typical grasslands were jointly influenced by climate and human factors, with temperate savannas mainly driven by the AP and AMWS. Moreover, high-cover grasslands were more sensitive to human activities than low-cover grasslands, and the most significant driving factors changed before and after the breakpoint in the area dynamics. These findings provide valuable insights into the monitoring and conservation of sandy land ecosystems.

Characterizing annual dynamics of urban form at the horizontal and vertical dimensions using long-term Landsat time series data

The dynamics of built-up height are a crucial aspect of urban form, enabling the characterization of urban growth patterns and the attainment of sustainable development goals. While past studies have focused on urban extent mapping, little has been done to reveal changes in vertical structures in built-up areas. In this study, we reconstructed annual urban form dynamics in Beijing, China, from 1990 to 2020, utilizing Landsat time-series data. First, we identified urban growth and renewal years using a temporal segmentation approach. Then, we reconstructed the annual dynamics of built-up heights through logical reasoning, using the reference height in 2020 and the identified changing years. We evaluated the derived dataset and analyzed the urban growth patterns (i.e., upward and outward) over the past three decades. The detected change years are reliable, with an overall accuracy of 88% (±1 year). Meanwhile, the reconstructed built-up height data showed good agreement with surveyed results (R2: 0.92), with a root mean square error of 2.87 m and a mean absolute error of 2.07 m. Most expanded built-up areas during 1990–2020 were located at the fringe of the central city, and about 16% experiencing multiple changes. Overall, outward growth was prominent in Beijing during the first decade, while upward growth became dominant in most districts thereafter. The proposed framework provides a new way to reveal annual urban form dynamics through time series analysis, with great potential to support relevant studies such as urban climate modeling and carbon emissions studies.

A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information

Remote sensing is an important tool for monitoring soil information. However, accurate spatial modeling of soil organic matter (SOM) in areas with high vegetation coverage, typically represented by agroecosystems, remains a challenge for field-scale estimation using remote sensing. To date, studies have focused on using single-period or multi-temporal vegetation information to characterize SOM. Thus, the relationship between SOM content and time-series vegetation biomass has not yet been fully explored. In addition, most studies have ignored the effects of critical soil properties and human activities (e.g., soil salinization, soil particle size fractions, history of land-use changes) on SOM. By integrating information on vegetation, soil, and human activities, we propose a novel framework for assessing SOM in cotton fields of artificial oases in northwest China, where returned straw is one of the primary sources of SOM coming from vegetation. We developed an Annual Maximum Biomass Accumulation Index (AMBAI) using time-series Landsat images from 1990 to 2019. Subsequently, we quantified the information of the planting years (PY) of cropland using spectral index threshold and incorporated proximal sensing data (soil hyperspectral and apparent conductivity data) and soil particle size fractions to establish a predictive model of SOM using partial least squares regression (PLSR), random forest (RF), and convolutional neural network (CNN). The results revealed that AMBAI had the highest correlation coefficient (r) with SOM (0.76, P < 0.01). AMBAI, soil hyperspectral data, and PY were the most relevant predictors for estimating SOM. The CNN model integrating vegetation, soil, and human activity information performed best, with coefficient of determination (R2), relative analysis error (RPD), and root mean square error (RMSE) of 0.83, 2.38 and 1.38 g kg−1, respectively. This study confirmed that AMBAI and PY had great potential for characterizing SOM in arid and semi-arid regions, providing a reference for other relevant studies.

Spectral Drought Indices: A Methodology for Detecting Basin-Wide Drought

Drought is a normal disaster that profoundly impacts several factors, including the economy, agriculture, environment, and society. Using spectral indices from Landsat, the current research evaluated droughts' magnitude and frequency in the Diyala River Catchment, Iraq, during the growing season for 2013 through 2022. Fifteen mosaics were created over ten years using forty images in TM and ETM+ (167/36 and 168/37) that had been gathered from 2013 to 2022. The scenes are from two Landsat time series. The drought situation was assessed using the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index, and Normalized Difference Water Index. The research's findings revealed a rise in the DRC's occurrence and severity of drought over the past ten years, especially in 2000 and 2008. However, between 2021 and 2022, the total vegetation covered based on NDVI decreased by 20.67% and 36.33%, respectively. In 2021 and 2022, the vegetation cover significantly decreased (54.3% and 29.5%, respectively). Derbendekan and Hemrin lakes, conversely, experienced declines of 15.1%, 7.12%, 108.38%, and 143.33% in 2021 and 2022, respectively. Between 2013 and 2022, the DRC experienced an increase in drought, a decline in water body surface area, and a decrease in precipitation averages.

Using Landsat and Sentinel-2 spectral time series to detect East African small woodlots

Accurate maps of gains in tree cover are necessary to quantify carbon storage, wildlife habitat, and land use changes. Satellite-based mapping of emerging smallholder woodlots in heterogeneous landscapes of sub-Saharan Africa is challenging. Our goal was to evaluate the use of time series to detect and map small woodlots (<1 ha) in Tanzania. We distinguished woodlots from other land cover types by woodlots' distinct multi-year spectral time series. Woodlots exhibit greening from planting to maturity followed by browning at harvest. We compared two time series approaches: 1) a linear model of Tasseled Cap Wetness (TCW) and other indices, and 2) LandTrendr temporal segmentation metrics. The approaches had equivalent woodlot detection accuracy, but LandTrendr segments had lower accuracy for characterizing woodlot age. We tested the effect of the following factors on woodlot detection and mapping accuracy: the length of the time series (2009–2019), frequency of observations (all Landsat vs. only Landsat-8), spatial resolution (30-m Landsat vs. 10-m Sentinel-2), and woodlot age and size. Woodlot mapping accuracies were higher with longer time series (54% at 3-yrs vs 77% at 7-yrs). The accuracies also improved with more observations, especially when the time series was short (3-yrs Landsat-8 only: 54% vs. all-Landsat: 64%, p-value <0.001). Sentinel-2's higher spatial resolution minimized commission errors even for short time series. Finally, less than half of young and small (<0.4 ha) woodlots were detected, suggesting considerable omission errors in our and other woodlot maps. Our results suggest that the accurate detection of woodlots is possible by analyzing multi-year time series of Landsat and Sentinel-2 data. Given the region's woodlot boom, accurate maps are needed to better quantify woodlots' contribution to carbon sequestration, livelihoods enhancement, and landscape management.

Combining Landsat time series and GEDI data for improved characterization of fuel types and canopy metrics in wildfire simulation

Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classification model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grasslands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R2 = 0.23–0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making.

Calibration interval scenario approach in spatial modeling of land cover change in East Kalimantan from 2016 to 2036

Spatial modeling can be used to predict future land cover changes based on past and present conditions. However, it is not yet known to what extent this model can be used to predict the future with reliable accuracy. Therefore, by using multi-temporal land cover data, this study aims to build an optimal model based on the calibration interval scenario. The optimal model is then used to predict and analyze changes in land cover in East Kalimantan in 2016–2036. 11 classified multi-temporal land cover maps from the Landsat Time Series using Random Forest in Google Earth Engine are used to model 14 calibration interval scenarios. A land Change Modeler is used to model and predict land cover change with 14 driving variables. The results of the classification of multi-temporal land cover maps show a good level of accuracy, with an Overall Accuracy value of 71.43–85.14% and a Kappa value of 0.667–0.827. Then 2016–2021 is one of the best scenarios with 5-year intervals where the accuracy of future predictions can still be relied upon for up to three prediction iterations. The calibration interval scenario approach in spatial modeling in East Kalimantan can be relied upon to show a decrease in forest cover from 2016 to 2021, with a deforestation rate of 651 km2/year. The prediction of land cover in 2036 estimates that the remaining forest cover area in East Kalimantan is 69.203 km2. It is believed that topography is the most influential variable driving land cover change in East Kalimantan.

Expansion of Urban Impervious Surfaces in Lahore (1993–2022) Based on Gee and Remote Sensing Data

Impervious surfaces are an essential component of our environment and are mainly triggered by human developments. Rapid urbanization and population expansion have increased Lahore's urban impervious surface area. This research is based on estimating the urban imper- vious surface area ( uisa ) growth from 1993 to 2022. Therefore, we aimed to generate an accurate urban impervious surfaces area map based on Landsat time series data on Google Earth Engine ( gee ). We have used a novel global impervious surface area index ( gisai ) for impervious surface area ( uisa ) extraction. The gisai accomplished significant results, with an average overall accuracy of 90.93% and an average kappa coefficient of 0.78. We also compared the results of gisai with Global Human Settlement Layer-Built and harmonized nighttime light ( ntl ) isa data products. The accuracy assessment and cross-validation of uisa results were performed using ground truth data on ArcGIS and gee. Our research findings revealed that the spatial extent of uisa increased by 198.69 km2 from 1993 to 2022 in Lahore. Additionally, the uisa has increased at an average growth rate of 39.74 km2. The gisai index was highly accurate at extract- ing uisa and can be used for other cities to map impervious surface area growth. This research can help urban planners and policymak- ers to delineate urban development boundaries. Also, there should be controlled urban expansion policies for sustainable metropolis and should use less impermeable materials for future city developments.

Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations

Crop phenology has been widely detected from multiple historical satellite observations. Conversely, Near-Real-Time (NRT) monitoring of crop progress from timely available remote sensing data is barely investigated because of the lack of high-frequency cloud-free satellite observations and future potential crop development. To address the challenge, this study proposes a novel algorithm for operational NRT monitoring of crop progress at the field scale. This algorithm first fuses the high spatial resolution (30 m) Harmonized Landsat and Sentinel-2 (HLS) data and the high temporal frequent (10 min) Advanced Baseline Imager (ABI) observations to generate cloud-free time series of HLS-ABI EVI2 (two-band Enhanced Vegetation Index) with a Spatiotemporal Shape-Matching Model (SSMM). It then predicts future potential EVI2 values at a given pixel using a reference EVI2 time series obtained from the neighboring pixels in the preceding year. Integrating the currently available HLS-ABI observations and the predicted future EVI2 values to generate annual EVI2 time series, the algorithm finally detects six crop phenometrics including greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. The NRT monitoring, which are separated as near-real-time prediction (phenological event detected after the occurrence), real-time prediction (phenological event detected around the occurrence), and short-term prediction (phenological event detected before the occurrence), are continuously updated and improved with new HLS and ABI observations at a weekly basis throughout the growing season. We evaluate the NRT monitoring against standard phenology products, PhenoCam observations, as well as the weekly Crop Progress Reports (CPRs) released from the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) in 2020 across Iowa. The evaluation demonstrates the robustness of the developed algorithm in NRT monitoring of crop phenology. Although the uncertainties are relatively large for short-term prediction compared with standard detections, the real-time prediction shows that the Mean Absolute Difference (MAD) is <10 days for greenup and dormancy onset, and ∼ 5 days in the other four phenometrics. Further, the real-time prediction aligns well with PhenoCam observations (R2 = 0.96, P < 0.001) with a MAD of 7.8 days. Moreover, the HLS-ABI real-time prediction of crop phenometrics is capable of tracking NASS crop progress closely with small time shifts (≤ 5 days) and significant correlations (R2 > 0.85, P < 0.001) for various phenological stages of corn and soybean. These results prove that the algorithm could be implemented for NRT monitoring of various crop phenometrics from field, state, to national scales.

Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram

Abstract. Glacier surges are prevalent in the Karakoram and occasionally threaten local residents by inundating land and initiating mass movement events. The Kyagar Glacier is well known for its surge history, and in particular its frequent blocking of the downstream valley, leading to a series of high-magnitude glacial lake outburst floods (GLOFs). Although the surge dynamics of the Kyagar Glacier have been broadly described in the literature, there remains an extensive archive of remote sensing observations that have great potential for revealing specific surge characteristics and their relationship with historic lake outburst floods. In this study, we propose a new perspective on quantifying the surging process using successive digital elevation models (DEMs), which could be applied to other sites where glacier surges are known to occur. Advanced Spaceborne Thermal Emission and Reflection Radiometer DEMs, High Mountain Asia 8-meter DEMs, and the Shuttle Radar Topography Mission DEM were used to characterize surface elevation changes throughout the period from 2000 to 2021. We also used Landsat time series imagery to quantify glacier surface velocities and associated lake changes over the course of two surge events between 1989 and 2021. Using these datasets, we reconstruct the surging process of the Kyagar Glacier in unprecedented detail and find a clear signal of surface uplift over the lower glacier tongue, along with uniformly increasing velocities, associated with the period of surge initiation. Seasonal variations in surface flow are still evident throughout the surge phase, indicating the presence of water at the glacier bed. Surge activity of the Kyagar Glacier is strongly related to the development and drainage of the terminal ice-dammed lake, which itself is controlled by the drainage system beneath the glacier terminus.

A tool for detecting giant kelp canopy biomass decline in the Californias.

Kelp species provide many ecosystem services associated with their three-dimensional structures. Among these, fast-growth, canopy-forming species, like giant kelp Macrocystis pyrifera, are the foundation of kelp forests across many temperate reefs. Giant kelp populations have experienced regional declines in different parts of the world. Giant kelp canopy is very dynamic and can take years to recover from disturbance, challenging comparisons of standing biomass with historical baselines. The Santa Barbara Coastal LTER (SBC LTER), curates a time series of Landsat sensed surface cover and biomass for giant kelp in the west coast of North America. In the last decade, this resource has been fundamental to understanding the species' population dynamics and drivers. However, simple ready-to-use summary statistics aimed at classifying regional kelp decline or recovery are not readily available to stakeholders and coastal managers. To this end, we describe here two simple metrics made available through the R package kelpdecline. First, the proportion of Landsat pixels in decline (PPD), in which current biomass is compared with a historical baseline, and second, a pixel occupancy trend (POT), in which current year pixel occupancy is compared to the time-series long probability of occupancy. The package produces raster maps and output tables summarizing kelp decline and trends over a 0.25 × 0.25° scale. Using kelpdecline, we show how sensitivity analysis on PPD parameter variation can increase the confidence of kelp decline estimates.

Using Landsat time series and bi-temporal GEDI to compare spectral and structural vegetation responses after fire

Passive and active spaceborne remote sensing technologies play a key role in monitoring forests across large areas, particularly when combining the advantages of both sensor technologies. This study investigates the link between spectral and structural change metrics following forest fire disturbance, collected from Landsat satellites and the Global Ecosystem Dynamics Investigation (GEDI) mission, respectively. The relationships were analysed across 1849 GEDI sampling locations (footprints), spread across south-east Australian forests. To assess structural change on the footprint level, simulated pre-fire GEDI observations were compared with real GEDI observations from one year after the fires. Results show relatively strong fire responses across Landsat spectral indices, with a median decline to between 46.1 % (Normalised Burn Ratio 2) and 77 % (Normalised Difference Vegetation Index) of pre-fire levels. GEDI’s structural change metrics demonstrated a markedly different response, with most showing an even more pronounced decline. In contrast, canopy height demonstrated a less substantial decline, dropping to 82.7 %. Results also suggest that fire severity and forest type impact the fire response of some of the examined spectral and structural metrics. In particular, taller forests and increased fire severity were associated with a more pronounced post-fire decline. The findings of this study highlight the large variation of forest structural responses to fire and their divergence from spectral change metrics, and emphasise the potential of integrating GEDI observations into wall-to-wall spectral forest change monitoring. The concept of bi-temporal GEDI observations as demonstrated in this study is promising, as it captures both pre- and post-disturbance structure on the footprint level and might enhance modelling of forest structural change in future approaches.