Time Series Of Landsat Imagery Research Articles

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.

A novel approach towards continuous monitoring of forest change dynamics in fragmented landscapes using time series Landsat imagery

Carbon emissions from forest ecosystems are greatly impacted by the acceleration of fragmentation and edge effects. Understanding these effects requires accurate monitoring of changes in fragmented forest landscapes. However, these changes are often low-intensity and small-scale, making it difficult to detect them using medium spatial resolution satellite images (e.g., Landsat). To address this challenge, this study developed the Pure Forest Index (PFI), which uses a combination of the existing vegetation index (VI) and spectral mixture analysis (SMA) to more effectively detect and characterize the contribution of forests to the observed spectral response of a pixel. The PFI was applied to detect forest changes in the Amazon rainforest from 1986 to 2020 using the Continuous Change Detection and Classification (CCDC) algorithm (hereafter referred to as the CCDC-PFI algorithm). The results showed reliable performance in mapping forest changes, with an overall accuracy of 0.94 (±0.03) at the spatial scale and a temporal accuracy of 91.1 % (within a two-year window). Comparison with other indices revealed that the PFI improves the ability to monitor forest dynamics with an increased overall accuracy of 0.02–0.35. The PFI also demonstrated advantages in enhancing sub-pixel forest information and suppressing non-forest backgrounds in various scenes compared to conventional VIs. The proposed approach is expected to benefit further research on forests and ecosystems.

Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region with Landsat Time Series Imagery and Random Forest Classification

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales.

Estimating fractional cover of saltmarsh vegetation species in coastal wetlands in the Yellow River Delta, China using ensemble learning model

Saltmarshes in coastal wetlands provide important ecosystem services. Satellite remote sensing has been widely used for mapping and classification of saltmarsh vegetation, however, medium-spatial-resolution satellite datasets such as Landsat-series imagery may induce mixed pixel problems over saltmarsh landscapes which are spatially heterogeneous. Sub-pixel fractional cover estimation of saltmarsh vegetation at species level are required to better understand the distribution and canopy structure of saltmarsh vegetation. In this study, we presented an approach framework for estimating and mapping the fractional cover of major saltmarsh species in the Yellow River Delta, China based on time series Landsat 8 Operational Land Imager data. To solve the problem that the coastal area is frequently covered by clouds, we adopted the recently developed virtual image-based cloud removal (VICR) algorithm to reconstruct missing image values under the cloud/cloud shadows over the time series Landsat imagery. Then, we developed an ensemble learning model (ELM), which incorporates Random Forest Regression (RFR), K-Nearest Neighbor Regression (KNNR) and Gradient Boosted Regression Tree (GBRT) based on temporal-spectral features derived from the time-series cloudless images to estimate the fractional cover of major vegetation types, i.e., Phragmites australis, Suaeda salsa and the invasive species, Spartina alterniflora. High spatial resolution imagery acquired by the Unmanned Aerial Vehicle and Gaofen-6 satellites were used for reference sample collections. The results showed that our approach successfully estimated the fractional cover of each saltmarsh species (average of R-square:0.891, RMSE: 7.48%). Through four scenarios of experiments, we found that the ELM is advantageous over each individual model. When the images during key months were absent, cloud removal for the Landsat images considerably improved the estimation accuracies. In the study area, Spartina alterniflora covers the largest area (5753.97 ha), followed by Phragmites australis with spatial extent area of 4208.4 ha and Suaeda salsa of 1984.41 ha. The average fractional cover of S. alterniflora was 58.45%, that of P. australis was 51.64% and that of S.salsa was 51.64%.

The Relative Timing of Population Growth and Land Use Change—A Case Study of North Taiwan from 1990 to 2015

Urban expansion is a form of land cover and land use change (LCLUC) that occurs globally, and population growth can be a driver of and be driven by LCLUC. Determining the cause–effect relationship is challenging because the temporal resolution of population data is limited by decadal censuses for most countries. The purpose of this study is to explore the relationship and relative timing between population change and land use change based on a case study of northern Taiwan from 1990 to 2015. A unique dataset on population was acquired from annually-updated governmental-based population registers maintained at the district level, and land-use expansion data (Residential, Employment, and Transportation Corridor categories) were derived from dense time series of Landsat imagery. Linear regression was applied to understand the general relationship between population and land use and their changes. The strongest relationships were found between population and areal extent of Residential land use, and between population change and Residential areal change. Lagged correlation analysis was implemented for identifying the time lag between population growth and land use change. Most districts exhibited Residential and Employment expansion prior to population growth, especially for districts in the periphery of metropolitan areas. Conversely, the core of metropolitan areas exhibited population growth prior to Residential and Employment expansion. Residential and Employment expansion were deemed to be drivers of population change, so population change was modeled with ordinary least square and geographically weighted regression with Residential and Employment expansion in both synchronized and time lag manners. Estimated population growth was found to be the most accurate when geographic differences and time lags from urban land use expansion were both incorporated.

Carbon uptake of the sugarcane agroecosystem is profoundly impacted by climate variations due to seasonality and topography

Sugarcane is an economically important crop whose growth and carbon uptake play vital roles in the carbon cycle, food security, and global trade development. However, the response of the carbon uptake of sugarcane to climate variations and the influencing factors are still unclear. In this study, seven sugarcane plantation distribution maps during 2000 and 2018 in Guangxi, China, were generated using time series Landsat and Sentinel-2 imagery, and these maps were used to produce stable sugarcane sites by identifying pixels without land cover transformation. Time series gross primary production (GPP) and climate datasets were used to analyze the response of annual GPP to temperature, precipitation, and their interacting effects under different landforms (rugged ground and flat ground) and growing periods. The results showed a significantly positive correlation between annual GPP change and temperature variations. Extreme precipitation, including extreme drought and extreme wetness, caused the annual GPP to decrease sharply, except for extreme wetness in the early growing period. The decrease in annual GPP mainly appeared when extreme drought and hot conditions or extreme wet and cold conditions coexisted, ranging from −20.37% to −0.23%, while the increase in annual GPP especially appeared when non-extreme precipitation and high temperature co-occurred, ranging from 0.02% to 8.44%. In addition, the responses of sugarcane annual GPP change to extreme temperature and precipitation were significantly different between rugged and flat ground. Overall, this study indicated that spatiotemporal heterogeneities existed in the response of sugarcane carbon uptake to climate variations. In the future, effective measures should be adopted in sugarcane management and cultivation to reduce the negative effects of climate change on crop production. • Sugarcane’s carbon uptake response discrepantly to heat and water stresses. • Impacts of climate variations on sugarcane GPP varied with growing stages. • Rugged grounds change the response of sugarcane GPP to extreme climates.

Spatiotemporal Characterization and Analysis of River Morphology Using Long-Term Landsat Imagery and Stream Power

Meandering rivers are among the most dynamic Earth-surface systems, which generally appear in fertile valleys, the most valuable lands for agriculture and human settlement. Landsat time series and morphological parameters are complementary tools for exploring river dynamics. Karun River is the most effluent and largest meandering river in Iran, which keeps the Karun’s basin economy, agriculture, and industrial sections alive; hence, investigating morphological changes in this river is essential. The morphological characteristics of Karun have undergone considerable changes over time due to several tectonic, hydrological, hydraulic, and anthropogenic factors. This study has identified and analyzed morphological changes in Karun River using a time series of Landsat imagery from 1985–2015. On that basis, morphological dynamics, including the river’s active channel width, meander’s neck length, water flow length, sinuosity index, and Cornice central angle, were quantitatively investigated. Additionally, the correlation between the stream power and morphological factors was explored using the data adopted from the hydrometric stations. The results show that the dominant pattern of the Karun River, due to the sinuosity coefficient, is meandering, and the majority of the river falls in the category of developed meander rivers. Moreover, the number of arteries reduced in an anabranch pattern, and the river has been migrating towards the downstream and eastern sides since 1985. This phenomenon disposes a change in the future that can be hazardous to the croplands and demands specific considerations for catchment management.

Developing seagrass index for long term monitoring of Zostera japonica seagrass bed: A case study in Yellow River Delta, China

Seagrass beds offer unique and vital ecological services as an important blue carbon ecosystem in coastal wetlands. Zostera japonica is an intertidal seagrass species native to eastern Asia and is one of the most widely distributed seagrass species in China. However, little is known on the long-term variations of Z. japonica extents. Automatic mapping method for Z. japonica seagrass beds is in urgent need to fill this knowledge gap. In this study, we proposed a new SeaGrass Index (SGI) for automatic and rapid mapping of Z. japonica based on time-series Landsat satellite imagery, aiming to alleviate the influence of tidal inundation and enhance the separability from other coastal cover types. The SGI considers both spectral and phenological characteristics of Z. japonica, as well as the spatial location of Z. japonica. We took the Yellow River Delta (YRD), China as our study area, where Z. japonica was first discovered and reported in 2015. Based on SGI, Z. japonica extents during 1985–2018 were extracted using multi-Otsu thresholding algorithm. Accuracy assessments based on field investigations and high-resolution imagery showed that SGI successfully separated seagrass beds from other cover types, especially intertidal salt marshes, with overall accuracies > 95%, producer’s accuracies > 90% and user’s accuracies > 94%. Our study provides the first long-term maps of seagrass beds in YRD. The area of Z. japonica showed large variations during 1985–2018, ranging from 149 ha in 2005–2006 to 1302.9 ha in 2011–2012. The spatial distribution of Z. japonica varied with the morphological change of the estuary caused by river channel shifts. Since 2011, Z. japonica seagrass beds have undergone area degradation due to the invasion of S. alterniflora. The area was only 332.3 ha in 2017–2018. Coastal erosion and extreme climate events such as drought and typhoon might also explain degradation of seagrass beds in YRD. We expect that the SGI will advance automatic and rapid mapping methods for intertidal seagrass beds, and the Z. japonica maps will provide a baseline data for restoration and management of seagrasses at regional scale.