Time Series Of Landsat Imagery Research Articles

Spatiotemporal changes and driving factors of ecological environmental quality in the Yongding-Luan River Basin based on RSEI

The ecological environmental quality (EEQ) of the Yongding-Luan River Basin (YLRB) is pivotal to the ecological security of the Beijing-Tianjin-Hebei (JJJ) region's core area. Evaluating the EEQ and analyzing its changes are essential for regional ecological management. However, long-term ecological changes in the YLRB remain uncovered. In this study, we constructed a seamless Remote Sensing Ecological Index (RSEI) for the YLRB from 1986 to 2022 using time-series Landsat imagery on the Google Earth Engine (GEE) platform. The Sen + Mann-Kendall method was employed to analyze the spatiotemporal trends of EEQ, and the Geodetector was used to quantitatively assess the driving factors and their interactions. The results show that: 1) The mean RSEI of the YLRB increased from 0.486 in 1986 to 0.532 in 2022, marking a 9.5% rise and indicating a fluctuating upward trend. 2) The EEQ of the YLRB experienced three distinct phases: improvement, deterioration, and re-improvement. Improvements were predominantly in the western YLRB, while deterioration was mainly in the northern Xilinguole region and the southern urban expansion areas of Beijing, Langfang, Tianjin, and Tangshan. 3) The driving factor detection indicates that land use type and annual average precipitation are the primary driving factors of RSEI change in the YLRB. Furthermore, their interaction results in a significant effect on RSEI, with a maximum of 0.691. These findings align with the historical urban expansion in the YLRB and the environmental policies implemented by the Chinese government. The ecological evolution and driving factors identified in this study offer a scientific basis for regional ecological decision-making and management.

Characterizing long-term tree species dynamics in Canada’s forested ecosystems using annual time series remote sensing data

Mapping tree species and changes in species distribution over time enables monitoring of successional dynamics and linking of realized species distributions to regional, climatic, and disturbance processes. Such knowledge is valuable for informing forest management activities regarding the role of climate change and various types of forest disturbances on tree species distributions and successional processes. In this study, we used time-series Landsat imagery to produce annual maps of dominant tree species (n = 37 tree species classes) from 1984 to 2022 at a 30-m spatial resolution for the 650 Mha of Canada’s forested ecosystems. The classification approach was based on spectral, geographic, climatic, and topographic descriptive metrics and was independently calibrated and validated with data from Canada’s National Forest Inventory. Preliminary results were informed by disturbance events and post-processed to ensure consistency of the annual species maps. Assessment of the resulting annual species maps using independent validation data resulted in an overall accuracy of 86.1 % ± 0.14 % (95 %-confidence interval). Over the study period, Canada’s treed area increased from 335 to 359 Mha. The area dominated by conifers increased in absolute terms but decreased in relative terms (from 86 % to 84.9 %), especially in western ecozones where wildfire is the main agent of disturbance. The area dominated by black spruce (Picea mariana), the most common tree species in Canada, remained stable, but its relative area decreased by 4.6 %. The area dominated by balsam fir (Abies balsamea, 1.5 %), trembling aspen (Populus tremuloides, 0.9 %), jack pine (0.5 %), and sugar maple (Acer saccharum, 0.4 %) increased. In burned areas, the prevalence of black spruce (74.7 %) and jack pine (Pinus banksiana, 14.6 %) was greater than their prevalence in Canada’s forested ecosystems overall (58.7 % and 3.8 %, respectively), while trembling aspen (2.4 % vs. 10 %) and subalpine fir (1.1 % vs. 4.6 %; Abies lasiocarpa) were underrepresented in burned areas. The area dominated by black spruce was underrepresented in harvested areas (46.8 % vs. 58.7 %) whereas balsam fir (6.9 % vs. 3 %), Engelmann spruce (5.5 % vs. 2 %; Picea engelmannii), red spruce (1.8 % vs. 0.4 %; Picea rubens), lodgepole pine (11.2 % vs. 5.6 %; Pinus contorta), Douglas-fir (2.7 % vs. 1.3 %; Pseudotsuga menziesii), and western hemlock (4.8 % vs. 2 %; Tsuga heterophylla) were found to be overrepresented. Both post-fire and post-harvest dynamics indicated a general trend of regeneration to the same pre-disturbance tree species, with post-harvest landscapes exhibiting a greater variety of different dominant tree species. These results provide valuable information for sustainable forest management and can inform conservation strategies by helping to better understand the short- and long-term effects of forest disturbances on species presence and distribution.

Estimating forest aboveground biomass in tropical forests with Landsat time-series data and recurrent neural network

ABSTRACT The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests.

Exploring land use dynamics in rural areas. An analysis of eight cases in the Global North

Rural areas are pivotal in the global economy, hence rural development planners and decision makers face significant technical and information management challenges, hindering their ability to assess sustainable development and formulate strategies. These challenges, coupled with shifts in traditional rural activities, aging populations, and climate change impacts, create inequalities and threaten rural communities. Therefore, developing approaches for the assessment of changes in rural areas are required. In recent years, remote sensing has emerged as a useful tool for monitoring the complex dynamics of rural regions, with a notable increase in the accessibility and use of freely available satellite data and platforms for tracking changes in land use and land cover (LULC). In light of this trend, this article analyzes the spatio-temporal trends in eight different development regions in Japan, Germany, Poland and Spain and integrates them with population data. This research relied on the utilization of openly available datasets - satellite imagery from Landsat and WorldPop population data, processed in Google Earth Engine (GEE). The time series of Landsat imagery were employed to analyze changes in LULC over a 20-year period from 2000 to 2020 in each test site. Unique combinations (i.e. each pixel history) were systematically examined and categorized based on established rules, with improbable scenarios marked as "uncertain." The comparison demonstrates that all eight regions have unique spatial, temporal and thematic characteristics. The most significant changes observed include the transformation of tree-covered areas into built-up areas in the case of Tsukuba and into agricultural lands in Shibushi. In the Bodzentyn and Połaniec communes in Poland, agricultural lands have transitioned into forested areas. In Spain, there is an increase in built-up areas, particularly at the outskirts of established towns. In Germany, the results indicate an expansion of built-up and agricultural areas in the Fürth region, while the Regen region experienced an increase in agricultural land. Incorporating population data reveals relationships between different types of LULC conversions and population trends, showing clear positive trends in areas transformed from agricultural and tree-covered land to built-up areas, with the most profound changes observed in Spanish cases and Tsukuba. The findings from automatic LULC classification highlight the usefulness of this tool in identifying socio-economic changes, especially when it is integrated with and compared to statistical data. Policy makers and planners at local levels should consider these specific values as a foundational reference point for shaping their plans related to sustainable development, land use, environmental preservation, and resource management.

Algorithm, Progresses, Datasets and Validation of GLC_FCS30D: the first global 30 m land-cover dynamic product with fine classification system from 1985 to 2022

Abstract. Land cover change information plays an indispensable role in environmental monitoring, climate change research, agricultural planning, urban development, biodiversity conservation, and natural disaster risk assessment. Recently, the free access of Landsat imagery and improvement of computation capacity especially supported by Google Earth Engine platform provides great chance in time-series land-cover change monitoring. We used the stratified land-cover monitoring strategy and time-series Landsat imagery to develop a novel global 30 m land-cover dynamic product with fine classification system from 1985 to 2022 (GLC_FCS30D). Firstly, we used the multitemporal classification to generate the time-series impervious surfaces, wetlands and tidal flat products. Then, we proposed to combine the continuous change detection algorithm and local adaptive updating model to capture the land-cover changes, and to generate a new global 30 m land-cover dynamic product (impervious surfaces, wetlands and tidal flat types were excluded in this step). Next, after overlapping the three multitemporal classification products and the time-series dynamical land-cover dataset, the novel GLC_FCS30D was developed, which contained 35 fine land-cover types. Lastly, using the global 84526 validation points in 2020, the GLC_FCS30D was validated to show the great performance with an overall accuracy of 80.88%, and had obvious advantages over other global land-cover products in diversity of land-cover types and mapping accuracy.

Assessing the Impacts of Landuse-Landcover (LULC) Dynamics on Groundwater Depletion in Kabul, Afghanistan’s Capital (2000–2022): A Geospatial Technology-Driven Investigation

Land use/land cover (LULC) changes significantly impact spatiotemporal groundwater levels, posing a challenge for sustainable water resource management. This study investigates the long-term (2000–2022) influence of LULC dynamics, particularly urbanization, on groundwater depletion in Kabul, Afghanistan, using geospatial techniques. A time series of Landsat imagery (Landsat 5, 7 ETM+, and 8 OLI/TIRS) was employed to generate LULC maps for five key years (2000, 2005, 2010, 2015, and 2022) using a supervised classification algorithm based on Support Vector Machines (SVMs). Our analysis revealed a significant expansion of urban areas (70%) across Kabul City between 2000 and 2022, particularly concentrated in Districts 5, 6, 7, 11, 12, 13, 15, 17, and 22. Urbanization likely contributes to groundwater depletion through increased population growth, reduced infiltration of precipitation, and potential overexploitation of groundwater resources. The CA-Markov model further predicts continued expansion in built-up areas over the next two decades (2030s and 2040s), potentially leading to water scarcity, land subsidence, and environmental degradation in Kabul City. The periodic assessment of urbanization dynamics and prediction of future trends are considered the novelty of this study. The accuracy of the generated LULC maps was assessed for each year (2000, 2005, 2010, 2015, and 2022), achieving overall accuracy values of 95%, 93.8%, 85%, 95.6%, and 93%, respectively. These findings provide a valuable foundation for the development of sustainable management strategies for Kabul’s surface water and groundwater resources, while also guiding future research efforts.

A 30 m annual cropland dataset of China from 1986 to 2021

A 30 m annual cropland dataset of China from 1986 to 2021

REDD implementation for greenhouse gas reduction and climate change mitigation in Hyrcanian forests: a case study of the Kojoor Watershed, Northern Iran

Reducing emissions from deforestation and forest degradation (REDD) is a specific strategy for combating deforestation and forest degradation to alleviate the effects of climate change. In this study, the potential greenhouse gas (GHG) emission reduction resulting from the implementation of a REDD project is estimated. Changes in forest cover throughout the years 1985, 1990, 1995, 2000, 2010, 2015, and 2020 were analyzed using time-series Landsat imagery (TM, ETM + , and OLI) and a random forest algorithm. Multilayer perceptron neural networks were used to model the transition potential of the forest cover, which were then predicted via Markov chain analysis. The change detection analysis revealed two discernible patterns in forest cover dynamics. Between 1985 and 2000, a notable decrease in forest cover was seen, whereas from 2000 to 2020, it significantly increased. The results suggested that the absence of REDD implementation would result in the deforestation of approximately 199,569 hectares of forest cover between 2020 and 2050, leading to the release of 1,995,695 tCO2e of emissions into the atmosphere. However, with the implementation of REDD, these emissions would be reduced to 405,512 tCO2e, effectively preventing the release of 1,590,183 tCO2e of emissions into the upper atmosphere. This study demonstrates that the implementation of REDD projects can be an effective strategy for reducing GHG emissions and mitigating climate change in the Hyrcanian forests.

Global annual wetland dataset at 30 m with a fine classification system from 2000 to 2022

Wetlands play a key role in maintaining ecological balance and climate regulation. However, due to the complex and variable spectral characteristics of wetlands, there are no publicly available global 30-meter time-series wetland dynamic datasets at present. In this study, we present novel global 30 m annual wetland maps (GWL_FCS30D) using time-series Landsat imagery on the Google Earth Engine platform, covering the period of 2000–2022 and containing eight wetland subcategories. Specifically, we make full use of our prior globally distributed wetland training sample pool, and adopt the local adaptive classification and spatiotemporal consistency checking algorithm to generate annual wetland maps. The GWL_FCS30D maps were found to achieve an overall accuracy and Kappa coefficient of 86.95 ± 0.44% and 0.822, respectively, in 2020, and show great temporal variability in the United States and the European Union. We expect the dataset would provide vital support for wetland ecosystems protection and sustainable development.

Long-Term Monitoring of Surface Water Dynamics and Analysis of Its Driving Mechanism: A Case Study of the Yangtze River Basin

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data-filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall classification accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Examining the Effects of Soil and Water Conservation Measures on Patterns and Magnitudes of Vegetation Cover Change in a Subtropical Region Using Time Series Landsat Imagery

Soil and water erosion has long been regarded as a serious environmental problem in the world. Thus, research on reducing soil erosion has received continuous attention. Different conservation measures such as restoring low-function forests, closing hillsides for afforestation, planting trees and grass, and constructing terraces on slope land have been implemented for controlling soil erosion problems and promoting vegetation cover change. One important task is to understand the effects of different conservation measures on reducing water and soil erosion problems. However, directly conducting the evaluation of soil erosion reduction is difficult. One solution is to evaluate the patterns and magnitudes of vegetation cover change due to implementing these measures. Therefore, this research selected Changting County, Fujian Province as a case study to examine the effects of implementing conservation measures on vegetation cover change based on time series Landsat images and field survey data. Landsat images between 1986 and 2021 were used to produce time series vegetation cover data using the Google Earth Engine. Sentinel-2 images acquired in 2021 and Landsat images in 2010 were separately used to develop land cover maps using the random forest method. The spatial distribution of different conservation measures was linked to annual vegetation cover and land cover change data to examine the effects on the change in vegetation cover. The results showed a significant reduction in bare lands and increase in pine forests. The vegetation coverage increased from 42% in 1986 to 79% in 2021 in the conservation region compared with an increase from 73% to 87% in the non-conservation region during the same period. Of the different conservation measures, the change magnitude was 0.44 for restoring low-function forests and closing hillsides for afforestation and 0.65 for multiple control measures. This research provides new insights in terms of understanding the effects of taking proper measures for reducing soil and water erosion problems and provides scientific results for decisionmaking for soil erosion controls. The strategy and method used in this research are valuable for other regions in understanding the roles of different conservation measures on vegetation cover change and soil erosion reduction through employing remote sensing technologies.

Spatiotemporal disturbances and attribution analysis of mangrove in southern China from 1986 to 2020 based on time-series Landsat imagery

As one of the most productive ecosystems in the world, mangrove has a critical role to play in both the natural ecosystem and the human economic and social society. However, two thirds of the world's mangrove have been irreversibly damaged over the past 100 years, as a result of ongoing human activities and climate change. In this paper, adopting Landsat for the past 36 years as the data source, the detection of spatiotemporal changes of mangrove in southern China was carried out based on the Google Earth Engine (GEE) cloud platform using the LandTrendr algorithm. In addition, the attribution of mangrove disturbances was analyzed by a random forest algorithm. The results indicated the area of mangrove recovery (5174.64 hm2) was much larger than the area of mangrove disturbances (1625.40 hm2) over the 35-year period in the study area. The disturbances of mangrove in southern China were dominated by low and low-to-medium-level disturbances, with an area of 1009.89 hm2, accounting for 57.50 % of the total disturbances. The mangrove recovery was also dominated by low and low-to-medium-level recovery, with an area of 3239.19 hm2, accounting for 62.61 % of the total recovery area. Both human and natural factors interacted and influenced each other, together causing spatiotemporal disturbances of mangrove in southern China during 1986–2020. The mangrove disturbances in the Phase I (1986–2000) and Phase III (2011–2020) were characterized by human-induced (50.74 % and 58.86 %), such as construction of roads and aquaculture ponds. The mangrove disturbances in the Phase II (2001–2010) were dominated by natural factors (55.73 %), such as tides, flooding, and species invasions. It was also observed that the area of mangrove recovery in southern China increased dramatically from 1986 to 2020 due to the promulgation and implementation of the Chinese government's policy on mangrove protection, as well as increased human awareness of mangrove wetland protection.

Bridging the Gap: Comprehensive Boreal Forest Complexity Mapping through LVIS Full-Waveform LiDAR, Single-Year and Time Series Landsat Imagery

The extrapolation of forest structural attributes from LiDAR has traditionally been restricted to local or regional scales, hindering a thorough assessment of single-year versus time series predictors across expansive spatial scales. We extrapolated the vertical complexity captured by the Land, Vegetation, and Ice Sensor (LVIS) full-wave form LiDAR of boreal forests in the Alaska–Yukon–Northwest Territories region, utilizing predictors from Landsat images from 1989 to 2019. This included both single-year and long-term estimates of vegetation indices, alongside constant factors like terrain slope and location. Random forest regression models comparing the single-year and 15-year and 30-year time series models were applied. Additionally, the potential of estimating horizontal forest complexity from vertical complexity was explored using a moving window approach in the Kluane Valley. While the extended time series marginally enhanced model accuracy, a fine-tuned single-year model proved superior (R2 = 0.84, relative RRMSE = 8.4%). In estimating the horizontal complexity, the variance in a 5 × 5 moving window displayed the most promising results, aligning with traditional horizontal structure measures. Single-year Landsat models could potentially surpass time series models in predicting forest vertical complexity, with the added capability to estimate horizontal complexity using variance in a moving window approach.

Assessment of peatland burning in Scotland during 1985–2022 using Landsat imagery

Abstract In the Scottish uplands, prescribed burning of moorland vegetation is widely practised either to boost gamebird numbers for recreational shooting or to improve livestock grazing. In recent years, this system of land management has become controversial due to concerns over the potential impacts on ecosystem services. However, there are limited data on the extent, distribution or frequency of burning and it is unclear whether there are long‐term trends in burning. Crucially, the extent of burning on peat soils is not well known. We used a time series of Landsat imagery covering 7750 km2 of moorland in Eastern Scotland to detect annual variation in area burnt from 1985 to 2022. Burnt areas were detected using annual changes in Normalised Burn Ratio. An accuracy evaluation conducted over eight sites covering 415 km2 using a combination of Google Earth imagery, and field studies suggested a user's accuracy of 90% and a producer's accuracy of 77%. We estimate an average annual mean area burnt of 61 km2 with large interannual variability and no significant change in area burnt over the 38‐year study period. We estimate that 32% of burning (19 km2 year−1) occurred on deep peat soils with no reduction in burning on deep peat after the revision of national guidelines (the Muirburn Code) in 2017 recommended ceasing this practice. We find that in Eastern Scotland there has been no significant change in moorland area burnt over the last four decades. The fractional area burnt that is on deep peat is a matter of management concern.

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.

Dynamic monitoring of surface water areas of nine plateau lakes in Yunnan Province using long time-series Landsat imagery based on the Google Earth Engine platform

Lakes are sensitive to both climate change and human activities, especially in the Yungui Plateau zone. As the main supply of water sources, surface water has a significant impact on environment, ecological balance and socioeconomic development. However, it is unclear how the surface water areas (SWAs) of the nine Yunnan Plateau lakes (NYPLs) have changed over previous decades. In this study, the SWAs of the NYPLs were first extracted using the optimal water index (WI) based on Landsat remote sensing images and the Google Earth Engine (GEE). Then, a spatiotemporal variation analysis of the SWAs was carried out for the NYPLs from 1988 to 2021. Finally, the correlations between the SWA changes in each lake, climatic factors and human activities were analysed. We found that the total SWAs of the NYPL have remained stable over the past 30 years, reaching a maximum of 1,012.51 km2 in 2000 and a minimum of 972.94 km2 in 2015. From 1988 to 2015, the overall SWAs of the NYPLs fluctuated steadily within 1%. However, Lakes Qilu and Yilong shrunk significantly in 2015. Generally, the SWAs of the NYPLs during the rainy season were larger than during the dry season; however, there were some differences between each period. The seasonal change in the SWAs was approximately 10 km2 in 1991, 1994, and 2003, but was relatively less in other years. Precipitation was a direct factor, whereas temperature and evaporation were indirect factors influencing the intra- and inter-annual SWAs of the NYPLs. The changes in the SWAs of the NYPLs were consistent with the precipitation trend and were negatively correlated with changes in temperature and evaporation. Evaporation had more of an influence on shallow lakes than on deep-water lakes. Human activities led to significant changes in individual lakes (such as lakes Dianchi, Yilong, and Qilu). The NYPLs had a spatially heterogeneous response to regional climate change caused by both global climate change and human activities. The results of this study can provide a data reference and decision support for the scientific management of the ecological environment of the Yunnan Plateau lakes.

Satellite Data Reveal Concerns Regarding Mangrove Restoration Efforts in Southern China

Mangrove restoration projects are often evaluated based on the increase in mangrove forest (MF) area, but the reliability of this indicator as a measure of successful restoration is questionable. Considering both numerical and quality dimensions, this study assessed mangrove restoration efforts in the Leizhou Peninsula and Beibu Gulf (LP-BG) in China. The hypothesis was that due to the limited social capital investment in mangrove restoration, there exist hierarchical differences in the urgency of mangrove restoration. Time-series Landsat imagery from the Google Earth Engine platform was used to analyze the MF distribution from 2000 to 2020. A resilience indicator, incorporating resistance and adaptive capacity, was constructed to assess MF quality within identified mangrove boundaries. The results revealed an increase in MF area from 6655.87 ha in 2000 to 14,607.93 ha in 2020. However, the majority (79.6%) of MF patches exhibited low resilience (values < 3), with only a minority (51 patches) demonstrating high resilience. Interestingly, MFs within the mangrove reserve displayed higher resilience, but these areas did not exhibit a significant spatial expansion of MFs. These findings highlight the limitation of relying solely on the net growth in the MF area as an indicator of successful restoration. Instead, an ecologically optimal solution is recommended, focusing on expanding conservation boundaries to include remnant MFs outside protected areas, rather than creating new planting areas. This study provides an assessment framework to evaluate the efficiency of mangrove restoration efforts and offers insights for local decision makers to guide future restoration endeavors.

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.

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.

Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

Numbers and sizes of photovoltaic solar power plants have grown unprecedentedly over the last few years in China, which aims to achieve a carbon emission peak by 2030 and carbon neutrality by 2060. Thus, timely and accurate monitoring of photovoltaic solar power plants is crucial to the design and management of renewable electricity systems in China. Random forest algorithm has been used to map photovoltaic solar power plants at multiple scales, however, it always causes several salt-and-pepper noises, limiting its application at larger spatial scales. Here we first develop a photovoltaic solar power plant mapping method through integrating time series Landsat imagery, random forest, and morphological characteristics. Then we apply this method in Gansu Province, which has abundant solar and wind energy resources and provide large amounts of potential lands for photovoltaic development, and generate the annual photovoltaic maps from 2015 to 2020. We further analyze the spatial-temporal dynamics of sizes and areas of photovoltaic solar power plants and major land cover conversion of expansive photovoltaic regions. Finally, we discuss the reliability, uncertainties, implications, and future development of our improved methods. We find our photovoltaic mapping method can remove most of salt-and-pepper noises effectively, and the resultant maps in Gansu for 2020 have very high accuracies with user's and producer's accuracies of 97.57% and 99.22%, respectively. There are 165.29 km2 photovoltaic solar power plants in Gansu for 2020, and most of which are located in the northwestern Gansu. In addition, the photovoltaic with patch size > 1 km2 and ≤ 2 km2 (53.4 km2, 32.3%) has largest patch number (39, 15.7%). The improved photovoltaic mapping methods and further analysis in this study provide critical information for accurate and automatic classification of photovoltaic solar power plants in the future, as well as the environmental and sustainable development of solar energy in China.