Time Series Of Satellite Imagery Research Articles

Morphological and Functional Polycentric Urbanization in Colombo Metropolitan of Sri Lanka Using Time-Series Satellite Images from 1988–2022

Examining urban growth in metropolitan areas from both morphological and functional perspectives is paramount for sustainable development in developing and developed countries. As a collaborative and critical approach, polycentrism operates concurrently at local, regional, and national levels. However, the decentralized morphologies and functional attributes of polycentric urban spaces are yet to be adequately explored, despite their significant influence on sustainable urban planning and management. Colombo, the only metropolitan area in Sri Lanka, has experienced considerable yet ambiguously imbalanced spatial growth due to insufficient data. This study employed time-series satellite imagery from 1988 to 2022, in conjunction with a network model, to elucidate Colombo’s uneven urban patterns, while achieving an accuracy of over 80% throughout the period. The results revealed that the metropolis has evolved into a polynuclear city, complicating the identification of centers and their corresponding developmental stages. Nevertheless, the study successfully pinpointed the morphological and functional Grama Niladhari divisions, disclosing the morphological features of centralized dispersion and the functional traits of decentralized concentration. These insights provide significant implications for local government, guiding urban planning initiatives to achieve sustainable development goals.

Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’s susceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest fires that occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively. Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused on examining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using the Google Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed to assess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVI and NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggest accelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE) index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire.

Lake total suspended matter retrieval by wind speed: A machine learning model trained by time-series satellite imagery

This study aims to develop an affordable and continuous method for monitoring water quality in arid, remote areas with high erosion rates. It presents a hypothesis that establishes a link between the concentration of Total Suspended Matter (TSM) and wind speed, emphasizing its ecological importance in lakes with dry conditions and high erosion rates. Building upon this hypothesis, the study introduces Wind2TSM-Net, a machine learning model that effectively bridges between different regression and neural network algorithms. This model connects on-site wind speed measurements with TSM data obtained through a physically remote sensing approach. It accurately predicts TSM concentration values, overcoming challenges such as cloud interference and reducing reliance on satellite imagery. The model was applied to Iran's Chah-Nimeh Reservoirs (CNRs) as a case study in an arid and remote area. The results revealed a significant correlation between TSM concentration and wind speed measurements, with impressive performance metrics (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 1.97 g/m3, mean absolute error (MAE) = 1.33). These findings highlight the effectiveness of Wind2TSM-Net in monitoring TSM values in remote and dry regions, particularly during extreme weather conditions when on-site measurements are impractical or cloud cover obstructs satellite observations.

The World Cup reshaped the urban green space pattern of Qatar

The World Cup stands as the most momentous global sporting event, and significantly impacts the urban green space (UGS) of host cities. However, the impacts, processes, and pattern characteristic of the World Cup on UGS have not yet been fully understood. To fill this gap, we employ time-series satellite imagery and compute the normalized difference vegetation index (NDVI) across detailed maps of UGS in Qatar from 2000 to 2022. In our quantitative assessment, we investigate UGS coverage, landscape patterns, and exposure both before and after the World Cup. Additionally, we compile seven instances of greening in Qatar, and compare them with greening processes in three cities located in neighboring countries. This contextual analysis aims to unravel the nuanced impact of the World Cup on UGS in Qatar. Our results demonstrate: (1) The World Cup emerges as a significant contributor to UGS growth, with UGS expansion accounting for 94.3% of the overall increase in the built-up area during the tournament. This surge in growth is equivalent to an additional area equivalent to 38 Manhattan Central Parks. (2) The World Cup induces a transformation in UGS landscapes, rendering them more complex and fragmented. The degree of change in the landscape index within the built-up area is 35 times greater than those changes observed in the pre-World Cup period. (3) The World Cup brings about a significant enhancement in the minimum level of green exposure for citizens, marking an 8.7-fold increase. This event has proven to be instrumental in propelling Qatar towards multifaceted urban greening, establishing the country as a leading model for regional greening processes. Our study thus confirms the World Cup's role in promoting and reshaping urban green space in Qatar, offering fresh insights into its contribution to urban greening and sustainable urban development.

Spatial and remote sensing monitoring shows the end of the bark beetle outbreak on Belgian and north-eastern France Norway spruce (Picea abies) stands.

In 2022, Europe emerged from eight of the hottest years on record, leading to significant spruce mortality across Europe. The particularly dry weather conditions of 2018 triggered an outbreak of bark beetles (Ips typographus), causing the loss of thousands of hectares of Norway spruce stands, including in Wallonia and North-eastern France. A methodology for detecting the health status of spruce was developed based on a dense time series of satellite imagery (Sentinel-2). The time series of satellite images allowed the modelling of the spectral response of healthy spruce forests over the seasons: a decrease in photosynthetic activity of the forest canopy causes deviations from this normal seasonal vegetation index trajectory. These anomalies are caused by a bark beetle attack and are detected automatically. The method leads in the production of an annual spruce health map of Wallonia and Grand-Est. The goal of this paper is to assess the damage caused by bark beetle using the resulting spruce health maps. A second objective was to compare the influence of basic variables on the mortality of spruce trees in these two regions. Lasted 6years (2017-2022), bark beetle has destroyed 12.2% (23,674ha) of the spruce area in Wallonia and Grand-Est of France. This study area is composed of three bioclimatic areas: Plains, Ardennes and Vosges, which have not been equally affected by bark beetle attacks. The plains were the most affected, with 50% of spruce forests destroyed, followed by the Ardennes, which lost 11.3% of its spruce stands. The Vosges was the least affected bioclimatic area, with 5.6% of spruce stands lost. For the most problematic sites, Norway spruce forestry should no longer be considered.

Self-supervised pre-training for large-scale crop mapping using Sentinel-2 time series

Large-scale crop mapping is essential for various agricultural applications, such as yield prediction and agricultural resource management. State-of-the-art techniques for crop mapping utilize deep learning (DL) models on satellite imagery time series (SITS). Despite advancements, the efficacy of DL-based crop mapping methods is impeded by the arduous task of acquiring crop-type labels and the extensive pre-processing required on satellite data. To address these issues, we proposed a Transformer-based DL model and a self-supervised pre-training framework for the label-scarce crop mapping task. Specifically, we first developed a Transformer-based Spectral Temporal Network (STNet) which is designed to extract task-informative features from time-series remote sensing (RS) imagery via the self-attention mechanism. A self-supervised pre-training strategy, namely SITS-MoCo, was then proposed to learn robust and generalizable representations from time-series RS imagery that is invariant to spectral noise, temporal shift, and irregular-length data. To evaluate the proposed framework, experiments were conducted using Sentinel-2 time series and high-confident Cropland Data Layer (CDL) reference data on six geographically scattered study sites across the United States from 2019 to 2021. The experimental results demonstrated that the framework had superior performance in comparison to other advanced DL models and self-supervised pre-training techniques. The pre-training strategy was proven to effectively alleviate the need for complex data pre-processing and training labels for the downstream crop mapping task. Overall, this research presented a novel pipeline for improving model performance on large-scale crop mapping with limited labels and provided a viable solution to efficiently exploit available satellite data that can be easily adapted to other large-area classification tasks. The source code is available at: https://github.com/YXu556/SITS-MoCo.

Outbreak Mechanism of Locust Plagues under Dynamic Drought and Flood Environments Based on Time Series Remote Sensing Data: Implication for Identifying Potential High-Risk Locust Areas

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture.

Assessing the status and spatial-temporal dynamics of the Bamenda Mountains (BM), North West region of Cameroon.

Change in land use and land cover (LULC) contributes in worsening ecological issues. Studying the trends of change in land use is highly significant to deal with global climate change and sustainable development. The aim of this paper is to evaluate the spatial-temporal dynamics of LULC of the Bamenda Mountains (BM) in the North West region of Cameroon, over a period of 34 years (1988-2022) and predict 34 years (2022-2056) future land use scenario of this site using time series satellite imagery (MSS, TM, ETM+, and OLI-TIRS) and ancillary data and to comprehend the driving forces of land use/land cover change (LULCC). The trends of LULCC were quantified; LULC maps were derived by classifying time series satellite images. Six LULC categories were identified during the study period (1988-2022). The research revealed a significant LULCC of the BM which can be justified by increase in the human population observed in the study area and the desire to extend agricultural lands to sustain the growing population. Overall, cultivated area 5684 ha (10.47%), 10680 ha (19.57 %), and 15163 ha (27.78%) and built-up area 449 ha (0.83%), 996 ha (1.83%), and 3242 ha (5.94%) for the study years 1988, 2003, and 2022, respectively, were all on the increase throughout the study period at the expense of other land cover types. The predicted figures of 2056 showed a continuous reduction of montane forest and savanna: 2401.92 ha (4.40%) and 25,862.67 ha (47.39%), respectively. Bare area is expected to drop in 2056 (2905.92 ha (5.32%)). The above decrease, when compared to 2022 figures, represents a loss of 3.97%, 4.53%, and 0.57%, respectively. The losses observed are gained by built-up and cultivated land (5.72% and 3.39%, respectively), covering surfaces areas of 6364.89 ha (11.66%) and 17,008.56 ha (31.17%), respectively. The above findings suggest that population growth is likely the major menace to the natural environment. It is thus safe to say that substantial LULCC was observed throughout the study period and will undoubtedly continue if nothing is done. This necessitates urgent measures such as reforestation and afforestation, encouraging off-farm activities and even improving technologies to combat the rate of forest degradation of the BM. Additionally, rebuilding trust between the French and English Cameroons through dialogue is premodial, to end the curent conflictual civil war and lessen the landscape configuration in Bamenda.

Mapping deforestation and recovery of tropical montane forests of East Africa

Deforestation poses a major threat to the tropical montane forest ecosystems of East Africa. Montane forests provide key and unique ecological and socio-economic benefits to the local communities and host diverse flora and fauna. There is evidence of ongoing deforestation and forest clearance in these montane forests although estimates diverge among different sources suggesting rates of 0.4%–3% yr−1. Quantifying deforestation rates and forest disturbance is critical to design conservation and sustainable management policies for forest management. This study quantified the rate of deforestation and forest recovery over the last three decades for the Mau Forest Complex and Mount Elgon forests in Kenya and Uganda using Landsat time-series satellite imagery. With the analysis, classification accuracies of 86.2% and 90.5% (kappa 0.81 and 0.88) were achieved for the Mau Forest Complex and the Mt Elgon forests, respectively. 21.9% (88,493 ha) of the 404,660 ha of Mau forest was lost at an annual rate of −0.82% yr−1 over the period between 1986 and 2017. More positively, 18.6% (75,438 ha) of the forest cover that was disturbed during the same period and is currently undergoing recovery. In Mt Elgon forest, 12.5% (27,201 ha) of 217,268 ha of the forest cover was lost to deforestation at an annual rate of −1.03% yr−1 for the period between 1984–2017 and 27.2% (59,047 ha) of the forest cover disturbed is undergoing recovery. The analysis further demonstrated agriculture (both smallholder and commercial) was the main driver of forest cover loss in Mau forest, accounting for 81.5% (70,612 ha) of the deforestation, of which 13.2% was due to large scale and 68.3% was related to the smallholders. For the Mt Elgon forest, agriculture was also the main driver accounting for 63.2% (24,077 ha) of deforestation followed by the expansion of human settlements that contributed to 14.7% (5,597 ha) of forest loss. This study provides accurate and novel estimates of the rate of deforestation for the Mau forest complex and Mt Elgon forest ecosystems. These rates are higher than previously estimated and recent deforestation has been identified, which provides a quantitative basis for forest restoration programs and to design conservation policies.

Dust detection and susceptibility mapping by aiding satellite imagery time series and integration of ensemble machine learning with evolutionary algorithms

To mitigate the impact of dust on human health and the environment, it is crucial to create a model and map that identifies the areas susceptible to dust. The present study focused on identifying dust occurrences in the Bushehr province of Iran between 2002 and 2022 using moderate-resolution imaging spectroradiometer (MODIS) imagery. Subsequently, an ensemble machine learning model was improved to prepare a dust susceptibility map (DSM). The study employed differential evolution (DE), genetic algorithm (GA), and flower pollination algorithm (FPA) - three evolutionary algorithms - to enhance the random forest (RF) ensemble model. A spatial database was created for modeling, including 519 dust occurrence points (extracted from MODIS imagery) and 15 factors affecting dust (Slope, bulk density, aspect, clay, altitude, sand, rainfall, lithology, soil order, distance to river, soil texture, normalized difference vegetation index (NDVI), soil water content, land cover, and wind speed). By utilizing the differential evolution (DE) algorithm, we determined the significance of these factors in impacting dust occurrences. The results indicated that altitude, wind speed, and land cover were the most influential factors, while the distance to the river, bulk density, and soil texture had less impact on dust occurrence. Data were preprocessed using multicollinearity analysis and the frequency ratio (FR) approach. For this research, three RF-based meta-heuristic optimization algorithms, namely RF-FPA, RF-GA, and RF-DE, were created for DSM. The effectiveness prediction of the constructed models by indexes of root-mean-square-error (RMSE), the area under the receiver operating characteristic (AUC-ROC), and coefficient of determination (R2) from best to worst were RF-DE (RMSE = 0.131, AUC-ROC = 0.988, and R2 = 0.93), RF-GA (RMSE = 0.141, AUC-ROC = 0.986, and R2 = 0.919), RF-FPA (RMSE = 0.157, AUC-ROC = 0.981, and R2 = 0.9), and RF (RMSE = 0.173, AUC-ROC = 0.964, and R2 = 0.878). The results showed that combining evolutionary algorithms with an RF model improves the accuracy of dust susceptibility modeling.

Shortfalls in the protection of persistent bull kelp forests in the USA

Kelp forests are one of the most productive ecosystems on earth, providing critical ecosystem services. Despite their global importance, their persistence in the face of human pressure and climate change is uncertain. We present a 38-year quarterly time series of satellite imagery that maps the distribution and persistence of surface canopy-forming kelp (dominated by the bull kelp, Nereocystis leutkeana) forests along eleven degrees of latitude in the western Pacific of the USA. We estimate kelp persistence as the fraction of years occupied by kelp canopy in the time series and evaluate the representation of kelp in marine protected areas (MPAs). While 3.6 % of kelp habitat is fully protected and 10.1 % is partially protected, only 0.7 % of the highly persistent kelp which may be indicative of climate refugia are fully protected. Regionally, the amount of kelp fully protected inside MPAs decreases from Central Northern California (8.0 %) to Oregon (5.9 %), Northern California (1.7 %), and Washington (0 %). Five years after the 2014–2016 marine heatwaves, kelp forests did not recover in California (∼90 % loss for both regions), while Oregon and Washington remained near pre-heatwave values. The low amount of protection in Northern California is concerning and likely exacerbates the vulnerability of kelp and associated species to marine heatwaves. Meeting a target of protecting 10 % of existing kelp habitat will require a 2.5-fold increase in kelp representation in MPAs. Moreover, we propose protecting highly persistent kelp is a cost-effective approach to increase representation and efficacy of MPAs to support climate resilience of kelp forest ecosystems.

Spatial-temporal analysis of coal fire risk identification and suppression assessment with satellite time series mapping 2013-2020 in Midong coalfield, Xinjiang, China

ABSTRACT Spontaneous combustion of underground coal seam leads to significant loss of coal resource while causing serious geological disasters, environmental degradation and social security problems. The Midong Coalfield in Urumqi was selected to conduct research on dynamic detection and assessment of underground coal fires from 2013 to 2020 based on time series satellite imagery of Landsat-8. The land surface temperature (LST) was retrieved using RTE. The hotspot SFSE was proposed to map the spatial-temporal distribution of coal fires. The CTIR was also established to solve how to comprehensively evaluate the intensity and spatial variations of coal fire thermal island effect. Based on Sequence Overlap Analysis the integrated dynamic interpretation of thermal anomaly intensity, area and migration of coal fire hazard has been provided. In addition, the TADC was proposed to effectively reflect the active fire centre and aggregation tendency. The results show that: The RTE inversion error is within 4.6 °C, verified with the surveyed LST. The overall accuracy of fire thermal anomaly extraction by HSA by field-tested exceeds 65%. A feature can be judged to be a fire pixel when its recurrence reaches 75% or more of the total number of observation frequency. The maximum and minimum extent extracted of the total fire area is 65.6×105 and 46.6×105 m2, respectively. And the overall expansion rate is 1.29×105 m2 year−1, in 82% agreement with the measurement. The burning intensity of CTIR first increases 0.024 year−1 averagely, but then decreases at a rate of 0.0055 year−1. Fractures and faults in the fire-affected area play a critical role in fire induction and distribution. Frequent mining and excavation activities are confirmed to accelerate the further fire production and propagation. After suppression, the relevant fire area has shrunken by up to 60%, while the overall CTIR is reduced by more than 70%.

Automatic forest change detection through a bi-annual time series of satellite imagery: Toward production of an integrated land cover map

Land cover mapping is fundamental for national and international agencies to monitor forest resources. However, monitoring forest disturbances by direct comparison of these maps poses several difficulties and challenges. As a result, different methodologies have been explored to detect forest disturbances. However, most of them cannot be fully integrated with land cover map production since they require additional input data, while others are not suitable for monitoring small land parcels. This study presents a methodology that fulfils the need to integrate land cover mapping with land cover change detection. Specifically, this methodology was designed to complement the Sentinel-2-based land cover mapping used in Galicia, northwest Spain, a region characterized by small land parceling. First, two previously obtained land cover maps from 2019 and 2020 were compared to identify all the pixels with potential land cover changes using QGIS. The behavior of spectral indexes in a time series were then analyzed to identify which of the previously identified pixels correspond to forest disturbances. This step was implemented in the software R. Using the Normalized Difference Vegetation Index (NDVI) to detect different land cover changes it was obtained an overall accuracy of 82%, considering the existence of varying phenologies, diverse topographic conditions, and areas with a high level of stand fragmentation. This study could help agencies that have already developed their own land cover maps to easily advance the integration of their maps with land cover change detection, since this technique can be applied with any land cover mapping methodology based on multitemporal analysis of satellite images, without the need for additional input data.

Diversified land conversion deepens understanding of impacts of rapid rubber plantation expansion on plant diversity in the tropics

Understanding the status and changes of plant diversity in rubber (Hevea brasiliensis) plantations is essential for sustainable plantation management in the context of rapid rubber expansion in the tropics, but remains very limited at the continental scale. In this study, we investigated plant diversity from 10-meter quadrats in 240 different rubber plantations in the six countries of the Great Mekong Subregion (GMS)—where nearly half of the world's rubber plantations are located—and analyzed the influence of original land cover types and stand age on plant diversity using Landsat and Sentinel-2 satellite imagery since the late 1980s. The results indicate that the average plant species richness of rubber plantations is 28.69 ± 7.35 (1061 species in total, of which 11.22 % are invasive), approximating half the species richness of tropical forests but roughly double that of the intensively managed croplands. Time-series satellite imagery analysis revealed that rubber plantations were primarily established in place of cropland (RPC, 37.72 %), old rubber plantations (RPORP, 27.63 %), and tropical forests (RPTF, 24.12 %). Plant species richness in RPTF (34.02 ± 7.62) was significantly (p < 0.001) higher than that in RPORP (26.41 ± 7.02) and RPC (26.34 ± 5.37). More importantly, species richness can be maintained for the duration of the 30-year economic cycle, and the number of invasive species decreases as the stand ages. Given diverse land conversions and changes in stand age, the total loss of species richness due to rapid rubber expansion in the GMS was 7.29 %, which is far below the traditional estimates that only consider tropical forest conversion. In general, maintaining higher species richness at the earliest stages of cultivation has significant implications for biodiversity conservation in rubber plantations.

A temporal-spectral value and shape change detection method integrating thematic index information and spectral band information.

Satellite imagery time series change detection methods are effective in avoiding pseudochange due to vegetation phenology to a certain extent. Traditional time series change detection methods use thematic indexes (e.g., NDVI, RVI) to obtain time series information for corresponding change detection. However, change detection methods using several thematic index time series may not make full use of other spectral band information in remotely sensed images and may still suffer from over- and under-detections. To address this challenge, a temporal-spectral value and shape change detection method integrating thematic index information and spectral band information (TISB) is proposed. Possible clouds and cloud shadowing phenomena are removed according to the changes in the spectral values of the remotely sensed images to avoid the generation of pseudochanges in clouds. The spectral and time series information is used to obtain change information from the value perspective, and then, further possible enhanced change regions from a shape perspective to obtain the final change detection results through the expectation-maximization (EM) method. Experiments with Landsat images have shown that the TISB method improves detection results by approximately 1-4% compared to the comparison method.

Does Regional Urbanization Promote Balanced Land Development? Evidence from Long Time Series Satellite Imagery

The urban megaregion has been promoted as among the major urbanization forms in New-Type Urbanization in China, which aims to promote more balanced development among cities and between the urban and rural areas in a region. While numerous studies have examined developed land expansion in cities worldwide using remotely sensed imagery, fewer have investigated its dynamic process in a rural area and the differences in the growth magnitude and expansion morphology between urban and rural areas. Using Landsat imagery from 1986 to 2020, we examined the spatiotemporal patterns of developed land in both the urban and rural areas in the Changsha–Zhuzhou–Xiangtan urban megaregion, China, using morphological analysis. We found that (1) the differences in the growth magnitude between the urban and rural areas varied between the different-sized cities, with increases in the largest city of Changsha, but decreases in the smaller ones of Zhuzhou and Xiangtan, although there was a slight increase at the megaregional scale. (2) The dynamic process of developed land in rural areas was similar to that in urban areas but showed a clear time-lag effect, where the dominant expansion types in urban areas shifted from edge to infilling expansion and to another edge expansion in 1986–2000, 2000–2010, and 2010–2020, whereas that in rural areas changed from outlying to edge expansion in 1986–2000 and 2000–2020. (3) The positive relationships between the growth speed and outlying and edge expansion suggested that the CZT urban megaregion was in the rapid outward expansion stage. Such a pattern may cause similar ecological effects, such as habitat fragmentation and urban heat archipelagos, to that in the eastern megaregions. Understanding such differences and their changes in the urban and rural areas will help optimize the strategies of urban megaregion sustainability.

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States.

Episodes of forest mortality have been observed worldwide associated with climate change, impacting species composition and ecosystem services such as water resources and carbon sequestration. Yet our ability to predict forest mortality remains limited, especially across large scales. Time series of satellite imagery has been used to document ecosystem resilience globally, but it is not clear how well remotely sensed resilience can inform the prediction of forest mortality across continental, multi-biome scales. Here, we leverage forest inventories across the continental United States to systematically assess the potential of ecosystem resilience derived using different data sets and methods to predict forest mortality. We found high resilience was associated with low mortality in eastern forests but was associated with high mortality in western regions. The unexpected resilience-mortality relation in western United States may be due to several factors including plant trait acclimation, insect population dynamics, or resource competition. Overall, our results not only supported the opportunity to use remotely sensed ecosystem resilience to predict forest mortality but also highlighted that ecological factors may have crucial influences because they can reverse the sign of the resilience-mortality relationships.

Surface-area development of foredune trough blowouts and associated parabolic dunes quantified from time series of satellite imagery

• Blowout surface area quantified from medium-scale satellite imagery. • Blowouts grow, stabilize and decay with a life time exceeding multiple decades. • Seasonal variation in blowout area increases with latitude. • Foredune erosion results in episodic, yet temporary increase in blowout surface area. Foredune trough blowouts are elongated wind-eroded depressions in the most seaward dune and their adjoining depositional lobes. Despite their importance to the sand budget and floral diversity of coastal dunes, the spatiotemporal evolution of trough blowouts is not well understood. We designed an automated workflow in the Google Earth Engine platform to produce time series of blowout surface area from medium-resolution satellite imagery available since the mid-1980s and applied it to a blowout system in the Netherlands, Denmark and the USA. Blowout surface areas were found to vary on multi-annual, seasonal and episodic time scales. Multi-annual change reflects successive development through stages of growth, stabilization and decay. The transition from growth to stabilization appears to be related to a change in blowout shape (width-to-length ratio). The decay phase starts with vegetation obstructing the blowout connection to the beach; the lobe can still migrate inland and develop into a parabolic dune before also becoming fully vegetated. The seasonal variations in blowout area increase with latitude; the observed larger areas in winter at the Dutch and Danish site presumably reflect seasonal plant development and the effect of stronger winds in winter. Episodic increases in blowout area, observed during winter at the Danish site only, are associated with pronounced foredune erosion. None of the episodic events changed blowouts into a different stage or persistently affected seasonal dynamics. Future work should focus on the combined analysis of changes in blowout area and sand volume to improve our understanding of sand-vegetation interactions driving blowout dynamics.

Using multi-temporal tree inventory data in eucalypt forestry to benchmark global high-resolution canopy height models. A showcase in Mato Grosso, Brazil

The global monitoring of forest structure worldwide is increasingly being supported by refined and enhanced satellite mission datasets. Forest canopy height is a global metric to characterise and monitor dynamics in forest ecosystems worldwide. Satellite mapping missions as NASA's Global Ecosystem Dynamics Investigation (GEDI) are creating opportunities to refine global forest canopy height models adding forest structural information to time-series satellite imagery. A recent global canopy height model presented by Lang et al., (2022) using GEDI and 10-m Sentinel-2 and the map from Potapov et al., (2020) using GEDI and Landsat are both tested in this study using multi-temporal tree-level data collected over eucalypt plantations in Brazil. Our results at plot-level showed Lang et al., (2022)’s estimates of canopy height came short compared to 2020 maximum and mean tree height records in the plots, 7.6 and 3.6 m, respectively, but adding CHM standard deviation improves the agreement of ground records for maximum tree height. Higher errors were computed for the plots in 2019 using the Potapov's 30-m CHM: 14.2 and 9.5 m, respectively. Averaged stand values were more similar between the three sources tested. We report improvement from the 30-m CHM to the 10-m, but still height saturation problems were observed when accounting for height differences in tall eucalypt trees. As more global products for forest height and biomass are becoming available to users, more validation exercises as presented in this study are needed to assess the suitability of CHM products to forestry needs, and facilitate the uptake and actionability of the next generation of global height and biomass products. We provide recommendations and insights on the use of GEDI laser data for global mapping and on the potential of commercial forestry areas to benchmark the accuracy of satellite mapping missions focusing on tree height estimation in the tropics.

The Interplay between Spatial Urban Expansion and Morphologic Landscapes East of Cairo, Egypt Using Time Series Satellite Imagery

This study aims to monitor the magnitudes, rates, and directions of spatial urban expansions east of Cairo and their interactions with the initial morphologic landscapes. The approach relies on using CORONA satellite images acquired in 1969 with fine spatial resolution and time series images of Landsat and ASTER from 1984 to 2020. The CORONA images enable retrieval of the initial morphologic components, whereas the Landsat and ASTER images enable the spatial urban expansions to be mapped. The magnitudes of spatial urban expansions have been massive, in the order of 165 km2. These expansions have occurred through four main temporal phases with different spatial extents, rates, and directions in response to common urban policies and socioeconomic settings. Assessing the interactions between urban expansions and the morphology of watersheds in the study area indicates that the directions of urban expansion have been opposite to the geospatial orientations of the watersheds. In addition, significant urban areas in the order of ~8 km2 are under the direct threat of flash floods because they are misplaced within the valley floors of the studied watersheds. The study concludes that successful spatial urban expansion should consider the morphologic characteristics of the initial landscape for the purpose of maximizing interests and avoiding or reducing potential hazards against urban settlements.