Time Series Of Landsat Images Research Articles

Using river restoration model to control riverbank erosion in the Old Brahmaputra river of Bengal Basin, Bangladesh

River meandering causes riverbank erosion, which triggers socioeconomic and environmental issues in the riparian region and raises river sedimentation, thus, needing river restoration. This research depicts a channel restoration model to control river bank erosion and susceptible resource management along the Old Brahmaputra River course based on channel characteristics from 1980 to 2021. The secondary data of Landsat satellite time series and SRTM images were collected from the USGS website, while we used Google Earth images to verify the morphological features. Satellite images were on-screen visualized by ArcMap (10.3) to extract the river channels and measure the erosion and accretion areas. Results showed that the Old Brahmaputra River channel shifted its position due to erosion and accretion. Over the last forty-one years, ∼ 52 km2 area was eroded while ∼ 49 km2 area was accreted, and the average shifting of the river channel was 1.3 km2 per year. Accordingly, the river channel has become more sinuous decade by decade and threatens nearby settlements. Hence, this study proposed a river channel based on previous river channels, where the base channel is from the 2000s, which was more stable and flowed through the middle of the floodplain. Parts of the 1980, 2000, and 2021 river channels were also selected, which are relatively far from the susceptible settlements, to draw the proposed restoring channel. This model is likely to aid in developing government strategies for protecting riverbank erosion and river restoration in the forthcoming period. Finally, this study recommends that this newly proposed channel will be conducive to reducing riverbank erosion and the extreme threat to the settlements and their livelihoods.

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Leaf area index (LAI) is an important variable for characterizing vegetation structure. Contemporary satellite-based LAI products with moderate spatial resolution, such as those derived from the MODIS observations, offer unique opportunities for large-scale monitoring but are insufficient for resolving heterogeneous landscapes. Although high-resolution satellite observations can derive detailed LAI maps, the low revisit frequency and the presence of cloud cover disrupt the temporal continuity of these high-resolution data, thereby leading to the fact that most current LAI inversion models for high spatial resolution data are based on single pixel and date without fully utilizing temporal information. Moreover, LAI estimation models trained solely using satellite products or simulations are also impeded by the inconsistencies between field and satellite LAI exist owing to local atmospheric, soil, and canopy conditions, while in-situ LAI measurements are sparse and insufficient for large-scale missions. To address these challenges, this study proposed a new framework based on deep transfer learning, which includes three key features that contribute to its high performance. Firstly, a Bi-directional Long Short-Term Memory (Bi-LSTM) model is pre-trained using MODIS reflectance and MODIS LAI products to capture the general non-linear relationship between reflectance and LAI and incorporate temporal dependencies as prior information to reduce uncertainties associated with ill-posed inversion problems and noises. Secondly, this pre-trained Bi-LSTM is transferred from satellite to field by fine-tuning with sparse in-situ LAI measurements to overcome issues arising from local inconsistencies. Thirdly, reconstructed Landsat time-series images via fusing MODIS and Landsat reflectance images are used as inputs of the Bi-LSTM to generate high-quality Landsat LAI products at both high spatial and temporal resolutions. To validate the proposed approach, field LAI measurements were collected at nine locations across the contiguous U.S. from 2000 to 2018, including three land cover types: croplands, grasslands, and forest. Quantitative assessments demonstrate that the Bi-LSTM outperforms three benchmarks, including a PROSAIL-based Look-Up Table (LUT) method, a random forest-based LAI retrieval and MODIS LAI (MCD15A3H), exhibiting lower RMSE and higher R2 in most cases. Additionally, the Bi-LSTM predictions yield lower random fluctuations than estimations from the LUT·, random forest and MODIS LAI, indicating the higher robustness of the proposed framework. The findings of this study highlight the value of transfer learning in estimation of vegetation biophysical parameters, which involves pre-training using sufficient existing satellite products to produce a generalized model and transferring knowledge from in-situ measurements to bridge gaps between satellite and field. By leveraging advanced transfer learning techniques and multi-source and multi-scale data, the proposed framework enables the production of long-term LAI maps at fine resolutions, facilitating downstream applications in regions characterized by high spatial heterogeneity.

A Transformer Model for Coastline Prediction in Weitou Bay, China

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management.

Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images

Farmland shelterbelt, as a category of shelterbelt in forestry ecological engineering, has an important influence on agricultural sustainability in agricultural systems. Timely and accurate acquisition of farmland shelterbelt age is not only essential to understanding their shelter effects but also directly relates to the adjustment of subsequent shelterbelt projects. In this study, we developed an age identification method using growth pattern to extract the age of shelterbelt (i.e., years after planting) based on Landsat time series images. This method was applied to a typical area of shelterbelt construction in the north of Changchun, China. The results indicated that the accuracy of age identification reached a stable situation when the permissible age error exceeded 3 years, achieving an accuracy of approximately 90%. Moreover, the accuracy at different growth phases (1–3 years, 4–15 years, 16–30 years, and >30 years) decreased with increasing age, and the accuracy of each growth phase can reach more than 80% when the permissible age error is beyond 7 years. Compared to building the typically weak statistical relationship between the shelterbelt age and remote sensing characteristic information to derive age, this method presented a direct age identification method for fine-scale age extraction of the shelterbelt. It introduced a novel perspective for shelterbelt age identification and the assessment of shelterbelt project advancement on the regional scale.

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

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

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

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

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Mapping 30m China annual impervious surface from 1992 to 2021 via a multi-level classification approach

ABSTRACT Accurately mapping impervious surface has great significance and application for urbanization monitoring, urban sustainable planning, ecosystem protection, etc. In this study, an automatic and high-performance multi-level classification approach based on time-series similarity, spectral feature, and logical reasoning was proposed to accurately map China's annual impervious surface dataset (CAID) from 1992 to 2021 using Landsat time-series images. Firstly, the synthesized image of 30-year night-time light maximum value was used to mask the areas where humans are inactive, which will preliminarily screen out the impervious surface and mitigate the computational volume. Then, similarity measurement based on dynamic time warping was performed, respectively, for periodic index series, which were weighted by an amplitude scale factor. Furthermore, similarity measurement, spectral features, and logical reasoning of long-time-series land cover type were combined to construct a multi-level approach to mapping impervious surface. Finally, for the computational efficiency of pixel-level time-series classification, we built a distributed parallel high-performance computing framework to fully utilize local computing resources for efficiently developing CAID. Using 58,839 validation samples based on stratified random sampling, the overall accuracy and F1-score of CAID were 90.05% and 88.49%. A comparison with five existing 30 m impervious surface products further indicated that our CAID achieved better impervious surface extraction accuracy and was capable of finely delineating impervious surface landscapes. Therefore, it was concluded that our CAID is an accurate and credible product that can provide an important data base for urbanization and urban-related research in China.

Characterising cropland fragmentation in post-Soviet Central Asia, using Landsat remote-sensing time series data

Following the dissolution of the Soviet Union, agricultural reforms in Central Asia often translated into the fragmentation of large fields into smaller shares. Most remote-sensing-based land use classification approaches are categorical in nature, and thus they are unable to capture these changes. We have developed an approach to detecting the timing of land fragmentation based on textural information from a time series of Landsat images for four Central Asian countries (Uzbekistan, Turkmenistan, Kyrgyzstan and Tajikistan) between 1990 and 2019. Our results showed that detected fragmentation events correspond well with documented agrarian reform processes in the different countries, and validation yielded maximum overall accuracies between 67 and 73%. In a case study of a former collective farm in Kyrgyzstan, we demonstrate how our method can accurately detect changes on the local scale. Texture time series data have great potential for analysing the trajectories of cropland fragmentation, in particular in regions where additional information on land use is limited.

Long-Term Change of Coastline Length along Selected Coastal Countries of Eurasia and African Continents

The acquisition of dynamic coastline change at fine spatial and temporal resolution is essential for enhancing sustainable coastal economic development and coastal environmental conservation. Port construction, land reclamation, urban development, and sediment deposition have resulted in extensive coastline change. In this study, the coastlines along the 56 coastal countries in 1990, 2000, 2010, 2015, and 2020 were delineated and classified into six categories using Landsat time–series images. Five relevant indices, i.e., the length, length ratio, length change rate, index of coastline utilization degree (ICUD), and fractal dimension (FD), were calculated to analyze and explore the spatiotemporal pattern of the coastlines. The results indicate that: (1) The overall length of the coastlines has increased from 3.45 × 105 km to 3.48 × 105 km in the past 30 years, with a net increase of nearly 3904 km. Between 1990 and 2020, the length of the artificial coastline increased by about 13,835 km (4.9~8.8%), while the length of the natural coastline decreased by 9932 km (95.1~91.2%). The increase in artificial coastline is concentrated in Southeast Asia and South Asia. (2) The coastline fractal dimensions (FDs) of countries and continents show that the average FD values of countries in South Asia (1.3~1.4) and Southeast Asia (1.2~1.3) were higher than other countries in the study regions, meaning that the coastlines in South Asia and Southeast Asia are more complex and curved. (3) The value of the ICUD index increased consistently between 1990 and 2015 (177.7~186.6) but decreased sharply between 2015 and 2020 (186.6~162.4), implying that the impact of human activities on the coastline continued to increase until 2015 and began to decrease after 2015. Our study examined the changes in various types of coastlines, which could be significant for sustainable development and environmental protection in coastal areas.

Spatio-Temporal Changes of Mangrove-Covered Tidal Flats over 35 Years Using Satellite Remote Sensing Imageries: A Case Study of Beibu Gulf, China

Tidal flats provide ecosystem services to billions of people worldwide; however, their changing status is largely unknown. Several challenges in the fine extraction of tidal flats using remote sensing techniques, including tide-level and water-edge line changes, exist at present, especially regarding the spatial and temporal distribution of mangroves. This study proposed a tidal flats extraction method using a combination of threshold segmentation and tidal-level correction, considering the influence of mangrove changes. We extracted the spatial distribution of tidal flats in Beibu Gulf, Southwest China, from 1987 to 2021 using time-series Landsat and Sentinel-2 images, and further analyzed the dynamic variation characteristics of the total tidal flats, each coastal segment, and the range of erosion and silting. To quantitatively investigate the interaction between tidal flats and mangroves, this study established a regression model based on multi-temporal tidal flats and mangrove data. The results indicated that the overall accuracy of the tidal flat extraction results was 93.9%, and the kappa coefficient was 0.82. The total area of tidal flats in Beibu Gulf decreased by 130 km2 from 1987 to 2021, with an average annual change of −3.7 km2/a. In addition, a negative correlation between the tidal flat change area and mangrove change area in Shankou, Maowei Sea, and Pearl Bay was observed, with correlation coefficients of −0.28, −0.30 and −0.64, respectively. These results demonstrate that the distribution of tidal flats provides a good environment and expansion space for the rapid growth of mangroves. These results can provide references for tidal flats’ resource conservation, ecological health assessment, and vegetation changes in coastal wetlands in China and other countries in Southeast Asia.

Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks

Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by mIOU scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves mIOU scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision.

Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images

An efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and developed an optimized forest fire risk model (fire-potential-index slope, FPIS). Combined with Landsat 8 satellite images, the study retrieved and analyzed the variations of forest fire risk in Zhaoqing City, Guangdong province, for four consecutive periods in the dry season, 2019. It was found that the high forest fire risk area was mainly distributed in the valley plains of Huaiji district, Fengkai district and Guangning district, the depressions of the Sihui district, and mountain-edge areas of Dinghu district and Gaoyao district, and accounted for 8.9% on 20 October but expanded to 19.89% on 7 December 2019. However, the further trend analysis indicated that the forest fire risk with significant increasing trend only accounted for 6.42% in Zhaoqing. Compared to the single high forest fire risk results, the changing trend results effectively narrowed the key areas for forest fire prevention (2.48%–12.47%) given the actual forest fires in the city. For the four forest fire events (Lingshan mountain, Hukeng industrial area, Xiangang county and Huangniuling ridge forest fires), it was found that the forest fire risk with significant increasing trend in these regions accounted for 26.63%, 35.84%, 54.6% and 73.47%, respectively, which further proved that the forest fire risk changing trend had a better indicated significance for real forest fire events than the high forest fire risk results itself (1.89%–71.69%). This study suggested that the forest fire risk increasing trend could be well used to reduce the probability of misjudgment and improve the accuracy of the early-warning areas when predicting forest fires.

A novel framework for vegetation change characterization from time series landsat images

Understanding terrestrial ecosystem dynamics requires a comprehensive examination of vegetation changes. Remote sensing technology has been established as an effective approach to reconstructing vegetation change history, investigating change properties, and evaluating the ecological effects. However, current remote sensing techniques are primarily focused on break detection but ignore long-term trend analysis. In this study, we proposed a novel framework based on a change detection algorithm and a trend analysis method that could integrate both short-term disturbance detection and long-term trends to comprehensively assess vegetation change. With this framework, we characterized the vegetation changes in Zhejiang Province from 1990 to 2020 using Landsat and landcover data. Benefiting from combining break detection and long-term trend analysis, the framework showcased its capability of capturing a variety of dynamics and trends of vegetation. The results show that the vegetation was browning in the plains while greening in the mountains, and the overall vegetation was gradually greening during the study period. By comparison, detected vegetation disturbances covered 57.71% of the province's land areas (accounting for 66.92% of the vegetated region) which were mainly distributed around the built-up areas, and most disturbances (94%) occurred in forest and cropland. There were two peak timings in the frequency of vegetation disturbances: around 2003 and around 2014, and the proportions of more than twice disturbances in a single location were low. The results illustrate that this framework is promising for the characterization of regional vegetation growth, including long-term trends and short-term features. The proposed framework enlightens a new direction for the continuous monitoring of vegetation dynamics.

Spatiotemporal variations of tidal flat landscape patterns and driving forces in the Yangtze River Delta, China

As a crucial coastal wetland habitat in the transition zone between land and sea, global tidal flats have severely declined by 16% over the last two decades under the dual threats of intense human activities and climate change. The Yangtze River Delta of China, the largest estuary in the western Pacific Ocean, has abundant mudflat resources and a dense human population. It also has some of the most prominent conflicts between economic development and ecological conservation. The current lack of understanding of landscape patterns and influencing factors of the Yangtze River Delta mudflats has severely hampered the region’s ecological conservation and restoration efforts. Based on Landsat time-series images, this study generated a 30-m spatial resolution map of mudflats in the Yangtze River Delta, which shrank by 47% during 1990–2020, with a higher density of mudflat loss in Yancheng and Nantong cities of the Jiangsu province and Hangzhou, Shaoxing, and Ningbo cities of the Zhejiang province. Landscape indices, such as the patch density of tidal flats, have gradually changed since 2000, with most of them showing significant changes in 2010. Mudflats in Lianyungang, northwestern Yancheng, Nanhui, Jiaxing, and Hangzhou showed sharp negative changes in landscape characteristics. Natural and anthropogenic factors had synergistic effects on the above changes in mudflat landscape patterns in the Yangtze River Delta. Mudflat landscape features were mainly influenced by population growth, economic development, reclamation, sediment discharge, and air temperature. Based on the evolving characteristics of mudflat landscape patterns, we recommend improving mudflat landscape management and planning by strengthening mudflat policies, laws, and regulations, developing countermeasures against threats from major stressors, and enhancing the effectiveness of nature reserves for mudflat protection.

Remote Sensing Monitoring of Pine Wilt Disease Based on Time-Series Remote Sensing Index

Under the strong influence of climate change and human activities, the frequency and intensity of disturbance events in the forest ecosystem both show significant increasing trends. Pine wood nematode (Bursapherenchus xylophilus, PWN) is one of the major alien invasive species in China, which has rapidly infected the forest and spread. In recent years, its tendency has been to spread from south to north, causing serious losses to Pinus and non-Pinus coniferous forests. It is urgent to carry out remote sensing monitoring and prediction of pine wilt disease (PWD). Taking Anhui Province as the study area, we applied ground survey, satellite-borne optical remote sensing imagery and environmental factor statistics, relying on the Google Earth Engine (GEE) platform to build a new vegetation index NDFI based on time-series Landsat images to extract coniferous forest information and used a random forest classification algorithm to build a monitoring model of the PWD infection stage. The results show that the proposed NDFI differentiation threshold classification method can accurately extract the coniferous forest range, with the overall accuracy of 87.75%. The overall accuracy of the PWD monitoring model based on random forest classification reaches 81.67%, and the kappa coefficient is 0.622. High temperature and low humidity are conducive to the survival of PWN, which aggravates the occurrence of PWD. Under the background of global warming, the degree of PWD in Anhui Province has gradually increased, and has transferred from the southwest and south to the middle and northeast. Our results show that PWD monitoring and prediction at a regional scale can be realized by using long time-series multi-source remote sensing data, NDFI index can accurately extract coniferous forest information and grasp disease information in a timely manner, which is crucial for effective monitoring and control of PWD.

Erosion of four Brazilian coastal deltas: how dam construction is changing the natural pattern of coastal sedimentary systems.

Reservoir-building on rivers is still "the Brazilian energy culture", such that many other dams are being constructed and planned, with many other rivers potentially suffering the same impact in their sediment flux. In this study, we evaluated the impacts of reservoir-building on the erosion of the São Francisco, Jequitinhonha, Doce, and Paraíba do Sul river deltas. A time series of Landsat images from 1973 to 2020 was analyzed, with three key highlighted moments (1973, 1997 and 2020) that summarize the erosion processes on these deltas. In addition to the images, continuous river water discharge, sediment discharge and basin rainfall data were analyzed between 1940 and 2020, providing river parameters over a period exceeding that of the satellite data. The findings suggest that coastal erosion has progressed in the four deltas, with higher estimated losses in the São Francisco and Paraíba do Sul over the 47 years of available satellite images. However, despite minimal overall estimated gains, the Jequitinhonha and Doce experienced high erosion at their river mouths, as in the other two rivers, compensated by accretion in distal coastal areas. These results can be explained by reductions in river flux and consequent sediment transport capacity due to reservoirs.

The Status and Influencing Factors of Surface Water Dynamics on the Qinghai-Tibet Plateau During 2000–2020

The Qinghai–Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai–Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai–Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km2, and there was a significant increasing trend (=322 km2/year, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.01$ </tex-math></inline-formula> ) during 2000–2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.

Vegetation disturbances characterization in the Tibetan Plateau from 1986 to 2018 using Landsat time series and field observations

Disturbances in vegetated land could dramatically affect the process of vegetation growth and reshape the land cover state. The overall greenup of vegetation on the Tibetan Plateau (TP) has almost served as a consensus to date. However, we still lack consistent acquisitions on the timing, the spatial patterns, and the temporal frequency of vegetation disturbance over the TP, limiting the capacity for planning land management strategies. Therefore, we explored the spatiotemporal pattern and variation of vegetation disturbances across the TP during the past decades and analyzed the disturbance agents. We utilized 37-year Landsat time series images and field observations coupled with a temporal segmentation approach to characterize the spatiotemporal pattern of vegetation disturbances across the TP for the period 1986–2018. The results from this study revealed that 75.71 M ha (accounting for 29.34% of TP’s area) vegetation area underwent at least one disturbance, of which 8.44 M ha area ever experienced large-scale disturbances (disturbance area greater than 0.9 ha and disturbance magnitude (the difference between the spectral value of pre-disturbance and that of post-disturbance) over 0.2). Further, the spatial distributions of these large-scale disturbances varied over time: before 2002, the disturbed sites were evenly distributed over the southeast part of the TP probably induced by overgrazing and unscientific livestock management, while after 2002, most disturbances were concentrated in the south of the Yarlung Tsangpo, mainly caused by anthropogenic activities, such as urban area, roadways, railway, and water control projects. This study presents an effort to characterize vegetation disturbances and their variations over the past decades on the TP, which provides crucial insights toward a complete understanding of vegetation dynamics and its causal relationship with human activities.

Monitoring and analysis of abandoned cropland in the Karst Plateau of eastern Yunnan, China based on Landsat time series images

The Karst Plateau in eastern Yunnan, China (KPEYC) is densely populated. In this area, rocks are widely exposed, and soil erosion and rock desertification are serious issues. Furthermore, the conflict between humans and land is prominent. It is important to monitor abandoned croplands to ensure regional food security, preserve the ecological environment, and promote sustainable regional development. Regarding the lack of historical data and inaccurate identification of abandoned cropland, this study proposed a monitoring method for abandoned croplands based on Landsat time series remote sensing images. First, a decision tree was used to generate constant samples from 2000 to 2020. Thereafter, land use in KPEYC was classified year-by-year based on the random forest method and abandoned cropland pixels were identified based on land use data. Finally, the spatial and temporal characteristics of abandoned cropland in the KPEYC were analysed. Based on the results, the overall accuracy of the method was 0.75. Cropland abandonment in the eastern Yunnan Karst Plateau displayed a rapid growth trend and a slow decrease during the study period, with a significant growth rate from 2001 to 2010. The abandoned cropland was mainly concentrated in southwestern Zhaotong City, the entire territory of Qujing City, and northwestern Wenshan Prefecture, among which Fuyuan County, Luoping County, and Huize County repeatedly experienced more serious cropland abandonment. In addition to social factors, topography had a greater influence on cropland abandonment in the KPEYC, and the greater the slope or altitude, the more severe the cropland abandonment.