Landsat Time Series Data Research Articles

Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data

The changing climate directly affects spatial and temporal patterns of snow and ice cover globally and in the Hindu-Kush Himalayan (HKH) region. In the HKH, around 3.3 billion people across 11 countries depend on water originating from mountain glaciers and snowfields, and melting snow cover has a direct impact on their livelihood and well-being. Various studies have shown that the snow and ice cover in the HKH is declining at an alarming rate but have been limited in geographic, spatial and temporal scales. Here, we employed the Global Land Analysis and Discovery analysis ready Landsat time-series data (GLAD ARD) to map changes in perennial snow and ice between 2001 and 2021 at five-year epochs using decision tree ensemble models. These maps were used to create a stratified sampling design for reference data collection to estimate area and map accuracy. All five-year epoch maps have user's accuracies above 90% and producer's accuracies above 91%. Our sample data analysis showed that one-eighth of the extent of perennial snow and ice in the HKH, totalling 15,770 km2 (CI ± 3195 km2), disappeared over the last two decades with 105,935 km2 (CI ± 3396 km2) remaining in 2021. From map-based estimates, the largest decline of perennial snow and ice cover among the mountain ranges was found in the Himalayas with an estimated reduction of 5741 km2. Among HKH countries, China had the largest area of perennial snow and ice reduction of 10,654 km2, Nepal had the highest net perennial snow and ice reduction rate (31%). Among the river basins, the maximum net loss of perennial snow and ice cover between 2001 and 2021 was in the Indus (24.8%), followed by Brahmaputra (18.3%), and Tarim (15.7%). Our results confirm wide-spread loss of perennial snow and ice across the HKH region and provide both regionally consistent maps with derived area estimates at landform, national and basin-levels and methodology to continue monitoring snow and ice melt.

An Assessment of Spatio-temporal Land Use/Land Cover Dynamics Using Landsat Time Series Data (2008-2022) in Kuliyapitiya West Divisional Secretariat Division in Kurunagala District, Sri Lanka

The main aim of the study is to detect the changes of land use land cover (LULC) in Kuliyapitiya West Divisional Secretariat division during 2008-2022. Following the download of Landsat images in the study area, several procedures were taken to pre-process the images. This included performing radiometric and geometric corrections to eliminate undesired sensor data and atmospheric noise. The Maximum Likelihood Classification (MLC) was used as the main technique of image post-processing to derive land use maps for the target years. To evaluate the accuracy of outputs 525 ground control points were verified using the Google Earth Pro engine. The classification accuracy of the study was 86% for 2008, 79 % in 2015, and 80 % in 2022. The results showed that over the past 15 years, settlements and built-up areas increased from 20.84% to 28.14% and 0.33% to 2.71%, respectively, whereas coconut lands decreased from 58.3% to 48.6%. The settlement, which showed an increase of land area of 11.9 km2 throughout the period, was identified as the main land use gainer while coconut was the main land use that lost 15.9 km2 of its land area over the past fifteen years. The built-up area showed a 3.96 km2 overall gain during the period due to urbanization and the expansion of the industrial, educational, and service sectors in the study area. The other four land use classes have not undergone any significant changes throughout the relevant time. The study highlights the importance of combining accuracy evaluation and image classification algorithms to gain a more comprehensive understanding of the LULC changes. Hence, our findings could assist decision-makers in land use planning to efficiently guide the sustainable land management.

Historical Dynamic Mapping of Eucalyptus Plantations in Guangxi during 1990–2019 Based on Sliding-Time-Window Change Detection Using Dense Landsat Time-Series Data

Eucalyptus plantations are expanding rapidly in southern China owing to their short rotation periods and high wood yields. Determining the plantation dynamics of eucalyptus plantations facilitates accurate operational planning, maximizes benefits, and allows the scientific management and sustainable development of eucalyptus plantations. This study proposes a sliding-time-window change detection (STWCD) approach for the holistic characterization and analysis of eucalyptus plantation dynamics between 1990 and 2019 through dense Landsat time-series data. To achieve this, pre-processing was first conducted to obtain high-quality reflectance data and the monthly composite maximum normalized-difference vegetation index (NDVI) time series was determined for each Landsat pixel. Second, a sliding time window was used to segment the time series and obtain the NDVI change characteristics of the subsequent segments, and a sliding time window-based LandTrendr change detection algorithm was applied to detect the crucial growth or harvesting phases of the eucalyptus plantations. Third, pattern-matching technology was adopted based on the change detection results to determine the characteristics of the eucalyptus planting dynamics. Finally, we identified the management history of the eucalyptus plantations, including planting times, generations, and rotation cycles. The overall accuracy of eucalyptus identification was 90.08%, and the planting years of the validation samples and the planting years estimated by our algorithm revealed an apparent correlation of R2 = 0.98. The results showed that successive generations were mainly first- and second-generations, accounting for 75.79% and 19.83% of the total eucalyptus area, respectively. The rotation cycles of the eucalyptus plantations were predominantly in the range of 4–8 years. This study provides an effective approach for identifying eucalyptus plantation dynamics that can be applied to other short-rotation plantations.

Mapping Urban Expansions along China–Europe Railway Express with the 30 m Time-Series Global Impervious Surface Area (GISA-2) Data from 2010 to 2019

The China–Europe Railway Express (CRE) plays a crucial role in promoting local economic development and bolstering regional connectivity. Quantitative assessments of urban changes along the CRE are essential for understanding its influence on the local eco-environment and the economy, which is a cornerstone for fostering sustainable growth. Urban boundaries are vital indicators of city development; however, accurately extracting these boundaries over multiple years at a large scale and fine granularity remains challenging. On the basis of a global impervious surface area (GISA2) dataset from 2010 to 2019, this study extracted fine-scaled city boundaries annually for European countries and analyzed the spatiotemporal patterns of urbanization in areas along and not along the express during the past decade. The generated enhanced urban boundaries (EUBs) show strong agreement with the Landsat time-series data and align well with existing global urban time-series products (GUB, GAUD, and NTL). From 2010 to 2019, urban areas and clusters expanded across the entire region, with notable disparities observed between areas along the express (noted as ON-CRE) and those not along (noted as OFF-CRE). ON-CRE cities experienced more substantial urban growth near the express, resulting in new mega urban agglomerations and expansion in small and moderate cities. In contrast, most OFF-CRE cities exhibited stable development with limited urban sprawl. Dynamic proportions of the impervious surface area (ISA) within the EUB indicated more compact impervious areas within ON-CRE boundaries compared to OFF-CRE. This study contributes to understanding urban expansions driven by the long-term CRE operations. The EUB dataset developed herein provides a valuable resource for future investigations into the impacts of urbanization on biotic stress and urban health in European countries, thus supporting the achievement of sustainable development goals.

A remote sensing based index for assessing long-term ecological impact in arid mined land

Large-scale mining operations in western China would accelerate the fragility and degradation to arid ecological environments. Remotely sensed monitoring and assessing of mining effects in long term change could provide sound understanding to guide mine ecological restoration and local ecosystem sustainability. This study's primary goal is to develop an arid mined land status (AMLS) index based on remote sensing data to assess ecological effects in mining areas of arid land. Furthermore, the performance of AMLS was contrasted with that of the remote sensing ecological index (RSEI) and the land surface ecological status composition index (LSESCI) in arid mines under various landscapes conditions. Eventually, the Sen + Mann–Kendall method was used to analyze the temporal and spatial land status variations in the mining effect between 2005 and 2020 based on Landsat time-series images. The following was revealed by the results: (1) AMLS had high correlation (r > 0.65) with each factor, including bare soil flatness, dryness, land surface temperature, and slope. (2) Comparing with LSESCI and RSEI, the proposed AMLS could characterize the mining area and surrounding natural bare soil and rock with different ecological status levels in three mines, hence indicated the mining directional heterogeneity and distance attenuation of ecological status. (3) Based on time series Landsat data, the AMLS demonstrated long-term ecological variability from 2005 to 2020 due to mining and reclamation in three mines.

Assessing Long-Term Thermal Environment Change with Landsat Time-Series Data in a Rapidly Urbanizing City in China

The studies of urban heat islands or urban thermal environments have attracted extensive attention, although there is still a lack of research focused on the analysis of long-term urban thermal environment change with fine spatial resolution and actual exposure of urban residents. Taking the rapidly urbanizing city of Nanjing, China as an example, this study utilizes the Landsat-derived daytime time-series land surface temperature data to comprehensively assess the city’s long-term (30-year) urban thermal environment change. The results showed that: (1) The overall surface urban heat island intensity showed a noticeable trend of first increasing and then decreasing from 1990 to 2020. (2) It exhibited the detailed spatial distribution of urban heat/cold islands within the urban center boundary. The percentage of surface urban heat islands was 77.01% in 1990, and it increased to 85.79% in 2010 and then decreased to 80.53% in 2020. (3) More than 65% of the urban residents have lived in areas with a surface urban heat island intensity greater than 3.0 °C, which also showed a trend of first increasing and then decreasing from 1990 to 2020. The methods and findings of this study can provide a reference for other studies on urban thermal environment changes and urban sustainable development.

Reconstructing Long-Term Synthetic Aperture Radar Backscatter in Urban Domains Using Landsat Time Series Data: A Case Study of Jing–Jin–Ji Region

Synthetic aperture radar (SAR) records important information about the interaction of electromagnetic waves with the Earth’s surface. However, long-term and high-resolution backscatter coefficient data are still lacking in many urban studies (e.g., building height estimation). Here, we proposed a framework to reconstruct the 1-km backscatter coefficient in 1990–2022 utilizing the Sentinel-1 Ground Range Detected data and Landsat time series data in the Jing–Jin–Ji (JJJ) region. First, we developed a regression model to convert the optical signals from Landsat into backscatter coefficients as the Sentinel-1 data, using observations from 2015 to 2022. Then, we reconstructed backscatter coefficients from 1990 to 2022 using the long-term Landsat data. Using the reconstructed backscatter coefficients, we analyzed the dynamic patterns of building height over the past decades. The proposed approach performs well on estimating the backscatter coefficient and its spatial pattern, with the annual mean absolute error, root mean square error, and R 2 of 1.10 dB, 1.50 dB, and 0.64, respectively. The temporal trends revealed from the reconstructed backscatter data are reliable compared with satellite observations at a relatively coarse resolution, with Pearson’s coefficients above 0.92 in 6 sample cities. The derived building height from the reconstructed SAR data indicates that the JJJ region experienced a noticeable upward expansion in 1990–2022, e.g., Beijing has the fastest growth rate of 0.420 km 3 /decade regarding the total building volumes. The proposed framework of reconstructing SAR data from optical satellite images provides a new insight to complement the long-term and high-resolution backscatter from local to global scales.

Characterizing forest recovery following stand-replacing disturbances in boreal forests: contributions of optical time series and airborne laser scanning data

The success and rate of forest regeneration following disturbance has implications for sustainable forest management, climate change mitigation, and biodiversity, among others. Systematic monitoring of forest regeneration over large and often remote areas of the boreal forest is challenging. The use of remotely sensed data to characterize post-disturbance recovery in the boreal forest has been an active research topic for more than 30 years. Innovations in sensors, data policies, curated data archives, and increased computational power have enabled new insights into the characterization of post-disturbance forest recovery, particularly following stand-replacing disturbances. Landsat time series data have emerged as an important data source for post-disturbance forest recovery assessments, with Landsat’s 40-year archive of 30-m resolution data providing consistent observations on an annual time step and enabling retrospective capacity to establish spatially explicit recovery baselines. The application of remote sensing for monitoring post-disturbance forest recovery is a rapidly growing area of research globally; however, despite the large amount of disturbance and the disproportionate effects of climate change in the boreal, the boreal biome is relatively underrepresented in the remote sensing forest recovery literature. Herein, the past and present contributions of optical time series and airborne laser scanning data to the characterization of forest recovery in boreal forests are highlighted, and future research priorities are identified.

Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data

Extraction of Building Construction Time Using the LandTrendr Model With Monthly Landsat Time Series Data

Impact of water supply on the restoration of the severely damaged riparian plants along the Tarim River in Xinjiang, Northwest China

Restoring degraded ecosystems in drylands is challenging because of low precipitation and high evapotranspiration. Water availability determines the existence and spatiotemporal distribution patterns of key endangered species struggling to survive in the severely damaged desert riparian ecosystem of the lower Tarim River (LTR) in NW China. Ecological water flow restoration measures with high costs and uncertainties have been implemented since 2000. The key task for scientists is to assess the success and drawbacks of this restoration project and propose specific recommendations for stakeholders to optimize the ecological benefits of the project in a cost-effective manner. In this study, Landsat time-series data from 1990 to 2020 were used to extract vegetation index values from river corridors along the LTR and explore the spatiotemporal patterns of desert riparian forest (DRF) vegetation. Based on land use and surface water data, we extracted the interannual spatial distribution range of vegetation and surface water, and selected several sample plots to demonstrate the influence of surface water and groundwater on vegetation change trends. The study results showed the following. 1) From 1990 to 1999, the vegetation slowly degraded; from 2000 to 2012, the extent of vegetation restoration was approximately 1 km from the river; and from 2013 to 2020, the extent of vegetation restoration at the Yingsu and Karday sections increased to 4.5–6.0 km, while that at the Arghan and Yiganbujima sections remained within 1 km from the river. 2) The vegetation growth rate increased substantially and was spatially heterogeneous when the groundwater level reached 4.0–5.0 m and was influenced by surface water divergence; the average vegetation expansion rates per square km for P1–P4 were 0.017 km2/a, 0.011 km2/a, 0.005 km2/a, and 0.002 km2/a, respectively. 3) Flooding by surface water was the main driver of vegetation expansion when groundwater was stable and elevations in surface vegetation area were often accompanied by the spreading of surface water. Surface water was significantly correlated with vegetation cover and normalized difference vegetation index (NDVI) (Water pixels–Vegetation cover [P1: R2 = 0.57, P < 0.001; P2: R2 = 0.45, P < 0.01]; Water pixels–NDVI [P1: R2 = 0.22, P < 0.05; P2: R2 = 0.40, P < 0.01]). The increase in surface vegetation has alleviated the desertification process in the LTR, and the survival of the poplar has been mitigated. The results of this paper can help decision makers to formulate reasonable ecological water conveyance strategies and realize sustainable management of DRF ecosystems in arid zones.

A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Classifying forest disturbance agents with Landsat time series data

Classifying forest disturbance agents with Landsat time series data

Predicting net growth rates in boreal forests using Landsat time series and permanent sample plot data

Abstract Increasing temperature and changes in water dynamics are bringing uncertainty regarding the future productivity of boreal forests, even in the absence of stand-replacing disturbances. There is accumulating evidence that water deficits caused by warmer summer temperatures are linked to decreases in the growth rate of boreal tree species in some regions. In this context, it is essential to provide forest professionals with a means of monitoring net forest growth rates in undisturbed areas and at the scale of a management unit in order to determine where and when changes in growth are taking place. This is challenging using conventional forest inventory approaches. In this study, we use Landsat time series and data from permanent sample plots (PSP) to develop spatially explicit estimates of annual net basal area growth at a 30-m spatial resolution for a forest management unit in Canada. An ordinary least square regression model was developed using data from 120 PSPs and validated on an independent set of 60 PSPs, with R2 values of 0.61 and 0.58, respectively. Applying the model over a 586 607-ha study area revealed considerable temporal and spatial variability in the predicted growth rates and their evolution through time. There was an overall decline in predicted growth rates over time, with this trend corroborated by the PSP data and attributed to the ageing demographics of the forests in the study area. This variability was related to forest development stage, species composition, and structural attributes derived from light detection and ranging (LiDAR). The information generated by the suggested approach can help to improve yield predictions, optimize rotation lengths, and allow for the identification of target areas where silvicultural interventions aimed at maintaining or enhancing growth could be conducted.

Comparison of different spectral indices to differentiate the impact of insect attack on planted forest stands

Eucalyptus plantations are an important contributor to the South African economy and are geographically concentrated in the productive Zululand region. However, this area is becoming increasingly vulnerable to various forest disturbances such as insect attacks and drought. Information of the location, extent and duration of insect infestation is therefore crucial for sustainable timber production and forest management planning, and remote sensing approaches have been successful in the retrieval and analysis of such information along with field surveys. Sappi Forests frequently conduct field surveys to establish the nature of disturbances to improve the performance of trees, as these may be difficult to distinguish from other disturbances such as droughts. Great effort has been made to remotely detect the impact of G. scutellatus using single-date high-resolution commercial satellites, whose application is restricted by high costs. Here, we compare the performance of spectral indices for detecting the impact of G. scutellatus infestation in KwaMbonambi plantations using publicly available Landsat data and also apply anomaly detection framework to further test their performance for detecting the impact of insect damage on trees and other disturbances that occurred on the same compartment over time. Our results showed high performance of short-wave infrared (SWIR) band for detecting the impact of insect damage, followed by the normalized difference infrared index (NDII) and normalized difference vegetation index NDVI, when anomaly detection was applied. Without anomaly detection, the spectral indicators showed changes even to drought impact in 2015, which did not when anomaly detection was used. We believe that indices containing the SWIR would perform better for detecting anomalous changes, and further research is required to explore this. Anomaly detection only showed the impact of insect damage and clearcutting as anomalous changes. Overall, we demonstrated the benefit of identifying vegetation anomalies caused by the impact of insect damage and other disturbances using freely available Landsat time-series data, particularly in data-poor regions that have restricted computational resources.

Mangrove Forest Cover Change in the Conterminous United States from 1980–2020

Mangrove forests in developed and developing countries are experiencing substantial transformations driven by natural and anthropogenic factors. This study focuses on the conterminous United States, including Florida, Texas, and Louisiana, where coastal development, urbanization, hydrological pattern alterations, global warming, sea level rise, and natural disasters such as hurricanes contribute to mangrove forest changes. Using time-series Landsat data and image-processing techniques in a cloud computing platform, we analyzed the dynamics of mangrove forests every five years from 1980 to 2020. Each thematic product was independently derived using a region of interest (ROI) suitable for local conditions. The analysis was performed using consistent data sources and a unified classification methodology. Our results revealed that the total mangrove area in the conterminous United States (CONUS) in 2020 was 266,179 ha. with 98.0% of the mangrove area in Florida, 0.6% in Louisiana, and 1.4% in Texas. Approximately 85% of the CONUS mangrove area was found between 24.5° and 26.0° latitude. Overall, mangrove forests in the CONUS increased by 13.5% from 1980 to 2020. However, the quinquennial variation in aerial coverage fluctuated substantially. The validation of 2020 using a statistical sample of reference data confirmed the high accuracy of 95%. Our results can aid policymakers and conservationists in developing targeted strategies for preserving the ecological and socio-economic value of mangrove forests in the conterminous United States. Additionally, all the datasets generated from this study have been released to the public.

Detecting semi-arid forest decline using time series of Landsat data

ABSTRACT Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations.

Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988–2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.

Effects of Groundwater Depth on Vegetation Coverage in the Ulan Buh Desert in a Recent 20-Year Period

As a typical desert in the Inner Mongolia Autonomous Region, the Ulan Buh Desert has a dry climate and scarce precipitation all year round. Groundwater has become the main factor limiting the growth of vegetation in this region. It is of great significance to study the influence of groundwater depth on the spatial distribution pattern of vegetation in this region. Based on the PIE-Engine platform and using long-term time-series Landsat data, this paper analyzed the spatial–temporal distribution characteristics and trends in vegetation coverage in the Ulan Buh Desert in the last 20 years using a pixel dichotomy model and the image difference method. The Kriging interpolation method was used to interpolate the groundwater depth data from 106 monitoring wells in the Ulan Buh Desert over the past 20 years, and the spatial distribution characteristics of groundwater depth in the Ulan Buh Desert were analyzed. Finally, the correlation coefficient between changes in vegetation coverage and changes in groundwater depth was calculated. The results showed the following: (1) The vegetation coverage in the Ulan Buh Desert was higher in the periphery and lower in the center of the desert. The overall vegetation level showed an increasing trend year by year; the growth rate was 4.73%/10 years, and the overall vegetation cover showed an improving trend. (2) The overall groundwater depth in the Ulan Buh Desert was deep in the southwest and shallow in the northeast. In the past 20 years, the groundwater depth in the Ulan Buh area has become shallower, and the ecological condition has gradually improved. (3) On the whole, the vegetation coverage varied with the groundwater depth, and the shallower the groundwater depth, the greater the vegetation coverage. When the groundwater depth increased to more than 4 m, the change in the groundwater depth had a significant effect on the vegetation coverage. However, when the groundwater depth was greater than 6 m, the change in the groundwater depth had no significant effect on the change in vegetation coverage.

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.

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.