Time Series Of Landsat Images Research Articles

Tropical Forest Disturbance Monitoring Based on Multi-Source Time Series Satellite Images and the LandTrendr Algorithm

Monitoring disturbances in tropical forests is important for assessing disturbance-related greenhouse gas emissions and the ability of forests to sequester carbon, and for formulating strategies for sustainable forest management. Thanks to a long-term observation history, large spatial coverage, and support from powerful cloud platforms such as Google Earth Engine (GEE), remote sensing is increasingly used to detect forest disturbances. In this study, three types of forest disturbances (abrupt, gradual, and multiple) were identified since the late 1980s on Hainan Island, the largest tropical island in China, using an improved LandTrendr algorithm and a dense time series of Landsat and Sentinel-2 satellite images on the GEE cloud platform. Results show that: (1) the algorithm identified forest disturbances with high accuracy, with the R2 for abrupt and gradual disturbance detection reaching 0.92 and 0.83, respectively; (2) the total area in which forest disturbances occurred on Hainan Island over the past 30 years accounted for 10.84% (2.33 × 105 hm2 in total area, at 0.35% per year) of the total forest area in 2020 and peaked around 2005; (3) the areas of abrupt, gradual, and multiple disturbances were 1.21 × 105 hm2, 9.96 × 104 hm2, and 1.25 × 104 hm2, accounting for 51.93%, 42.75%, and 5.32% of the total disturbed area, respectively; and (4) most forest disturbance occurred in low-lying (<600 m elevation accounts for 97.42%) and gentle (<25° slope accounts for 94.42%) regions, and were mainly caused by the rapid expansion of rubber, eucalyptus, and tropical fruit plantations and natural disasters such as typhoons and droughts. The resulting algorithm and data products provide effective support for assessments of such things as tropical forest productivity and carbon storage on Hainan Island.

GEE-Based Spatial-Temporal Dynamics in a Ramsar Wetland, Honghe National Nature Reserve, Northeast China from 1985 to 2021

Wetlands are vital to the human living environment, and with the degradation of wetland ecosystems, it is crucial to protect and restore them. Therefore, based on the long time-series Landsat images provided by Google Earth Engine (GEE), this study obtained the landscape spatial distribution maps of the Honghe National Nature Reserve (HNNR) from 1985 to 2021, analysed the spatial and temporal dynamics of the landscape patterns of the HNNR in the past 40 years, and explored the driving factors of the evolution of the HNNR wetland. The results show that from 1985 to 2008, the HNNR wetlands continued to degrade. The area of the wetland landscape declines and converts mainly into the meadow landscape, and the meadow landscape trends upwards and then downwards and converts mainly into woodland and arable land, with increased fragmentation of wetland and meadow. From 2008 to 2021, with the recovery in hydrological conditions, the area of the wetland landscape increased and fragmentation decreased. However, the meadow landscape continued to decline and fragmentation increased, with meadow converting mainly into wetland; changes in hydrological conditions were the main drivers of the evolution of the HNNR wetlands. The results of this study enable us to better understand the dynamics of the HNNR wetland landscape over the last 40 years and provide assistance for the management of the HNNR wetland ecosystems and the ecological restoration of degraded wetlands.

A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico

With the growth of cloud computing, the use of the Google Earth Engine (GEE) platform to conduct research on water inversion, natural disaster monitoring, and land use change using long time series of Landsat images has also gradually become mainstream. Landsat images are currently one of the most important image data sources for remote sensing inversion. As a result of changes in time and weather conditions in single-view images, varying image radiances are acquired; hence, using a monthly or annual time scale to mosaic multi-view images results in strip color variation. In this study, the NDWI and MNDWI within 50 km of the coastline of the Yucatán Peninsula from 1993 to 2021 are used as the object of study on GEE platform, and mosaic areas with chromatic aberrations are reconstructed using Landsat TOA (top of atmosphere reflectance) and SR (surface reflectance) images as the study data. The DN (digital number) values and probability distributions of the reference image and the image to be restored are classified and counted independently using the random forest algorithm, and the classification results of the reference image are mapped to the area of the image to be restored in a histogram-matching manner. MODIS and Sentinel-2 NDWI products are used for comparison and validation. The results demonstrate that the restored Landsat NDWI and MNDWI images do not exhibit obvious band chromatic aberration, and the image stacking is smoother; the Landsat TOA images provide improved results for the study of water bodies, and the correlation between the restored Landsat SR and TOA images with the Sentinel-2 data is as high as 0.5358 and 0.5269, respectively. In addition, none of the existing Landsat NDWI products in the GEE platform can effectively eliminate the chromatic aberration of image bands.

A forest type-specific threshold method for improving forest disturbance and agent attribution mapping

ABSTRACT Detecting forest disturbances and attributing their contributing agents with Landsat time series (LTS) images has advanced substantially in recent years; however, whether different forest types require individual disturbance indices or specific disturbance thresholds to accurately map forest disturbances and their causes over a vast region remains limited known. This study investigated the effectiveness of six spectral indices (SIs) and two threshold methods (a forest-specific threshold and a common threshold) for detecting forest disturbances among four forest types, namely, evergreen broad-leaved forests (EBFs), cold-temperate evergreen needle-leaved forests (CENFs), and subtropical evergreen needle-leaved forests mainly dominated by Pinus yunnanensis (SENF1) or Pinus kesiya (SENF2), across Yunnan Province with the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and yearly Landsat time series (LTS) recorded from 1990 to 2020. A random forest (RF) model was applied to classify the forest disturbance agents from aggregated patches of disturbed forest pixels. The results indicated that the normalized burn ratio (NBR) outperformed the five other SIs and achieved consistently high overall accuracies (OAs; 93.04%±0.17% to 96.09%±0.28%) when mapping forest disturbances across all four forest types. The forest-specific NBR disturbance thresholds led to considerable increases in overall (0.14–3.92%), producer (0.25–13.47%) and user accuracies (0.88–3.01%). The total mapped area of disturbed forest was 9831.48 km2 (5.31%), of which approximately 79% occurred in the EBF and SENF1 distribution areas. Forest disturbances were predominantly caused by wildfires in CENF and SENF1 and by commodity-driven plantations in EBF and SENF2; these two agents together contributed approximately 93.15% of the forest disturbances in Yunnan province. These findings highlight that the optimal selection of SIs and forest-specific disturbance thresholds can significantly improve forest disturbance detection performance.

Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform

A pixel-based algorithm for multi-temporal Landsat (TM/ETM+/OLI/OLI-2) imagery between 1990 and 2022 monitored mangrove dynamics and detected their changes in the three provinces (i.e., Thai Binh, Nam Dinh and Hai Phong), which are located on the Northern coast of Vietnam, through the Google Earth Engine (GEE) cloud computing platform. Results showed that the mangrove area in the study area decreased from 2960 ha in 1990 to 2408 ha in 1995 and then significantly increased to 4435 ha in 2000 but later declined to 3502 ha in 2005. The mangrove areas experienced an increase from 4706 ha in 2010 to 10,125 ha in 2020 and reached a highest peak of 10,630 ha in 2022. In 2022, Hai Phong province had the largest area of mangrove (3934 ha), followed by Nam Dinh (3501 ha) and Thai Binh (3195 ha) provinces. The overall accuracies for 2020 and 2022 were 94.94% and 91.98%, while the Kappa coefficients were 0.90 and 0.84, respectively. The mangrove restoration programs and policies by the Vietnamese government and local governments are the key drivers of this increase in mangroves in the three provinces from 1990 to 2022. The results also demonstrated that the combination of Landsat time series images, a pixel-based algorithm, and the GEE platform has a high potential for monitoring long-term change of mangrove forests during 32 years in the tropics. Moreover, the obtained mangrove forest maps at a 30-m spatial resolution can serve as a useful and up-to-date dataset for sustainable management and conservation of these mangrove forests in the Red River Delta, Vietnam.

Mapping Dynamic Turbidity Maximum Zone of the Yellow River Estuary from 38 Years of Landsat Imagery

Turbidity maximum zone (TMZ) plays a crucial role in estuarine ecosystems, exerting effects on erosion, environment evolution and socioeconomic activities in the coastal area. However, the long-term understanding of the TMZ in large river estuary such as the Yellow River estuary is still lacking. In this study, we focus on the TMZ distribution, variation and regulation mechanisms in the Yellow River estuary from different time scales. Based on time series Landsat images during the period 1984 to 2021 and Google Earth Engine (GEE), we proposed a TMZ extracting method in the Yellow River estuary to generate 322 TMZ maps. The overall accuracy of our algorithm reached 97.4%. The results show that there are clear decadal and seasonal TMZ variations during the 38-year period in the Yellow River estuary. Morphology, currents and wind speeds combined with seawater stratification have direct effects on TMZ at different time scales, while the direct impacts of tides and fluvial output of the Yellow River on TMZ are limited. In this article, the highly robust method provides a cost-effective alternative to accurately map the TMZ in global large river estuaries and systematically reveals the spatiotemporal evolution of TMZ, shedding light on the response mechanism of coastal geomorphology, marine ecological environment and biogeochemical cycle.

Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul

Many studies have reported that urbanization leads to a decrease in the normalized difference vegetation index (NDVI) due to the expansion of impervious cover. Some studies, however, have reported positive NDVI trends in urban areas due to warming and CO2 fertilization effects, as well as the creation of green space. Thus, we examined spatial and temporal variations of the growing season maximum NDVI in a megacity, Seoul, which has rapidly urbanized over the past decades, by analyzing a 32-year time series (1987 – 2018) of Landsat satellite images in Google Earth Engine. Continuous change detection and classification and random forest algorithms were integrated to classify Seoul land cover types annually. We found an overall increasing NDVI trend at the city scale (0.002 yr−1). Significant NDVI trends were found for approximately 46 % of Seoul, with greening and browning trends accounting for 39 % and 7 %, respectively. Greening pixels appeared mainly on impervious (23 % with a significant NDVI trend), deciduous (10 %), and evergreen (3 %) land cover as of 2018. Stable impervious, deciduous, and evergreen land cover pixels showed a greening trend over the 32 years (0.002 yr−1), which stemmed from the planting of trees in areas with impervious cover, such as streets and residential areas, and vegetation growth in forest areas. Disturbed area pixels showed fluctuating NDVI values, but there were more greening pixels (20 %) than browning pixels (5 %). Our findings indicate that a detailed knowledge of land use and land cover changes is required to understand NDVI trends in urban areas.

Recognition and Changes Analysis of Complex Planting Patterns Based Time Series Landsat and Sentinel-2 Images in Jianghan Plain, China

Accurate and timely information on crop planting patterns is crucial for research on sustainable agriculture, regional resources, and food security. However, existing spatial datasets have few high-precision and wide-range planting pattern maps. The production may be limited by the unbalanced spatiotemporal resolution, insufficient massive field sample data, low local computer processing speed, and other factors. To overcome these limitations, we proposed semi-automatic expansion and spatiotemporal migration strategies for sample points and performed a pixel-and-phenology-based random forest algorithm on the Google Earth Engine platform to generate crop planting pattern maps at high spatiotemporal resolution by integrating Landsat-8 and Sentinel-2 time series image data. In this study, we report planting pattern maps for 2017–2021 at a 10-m spatial resolution of the Jianghan Plain, including six crops and nine planting patterns, with an overall accuracy of 84–94% and a kappa coefficient of 0.80–0.93. The spatiotemporal distribution is driven by multiple factors, such as subjectivity and social economy. This research indicates that the proposed approach is effective for mapping large-scale planting patterns and can be readily applied to other regions.

Recent Recovery of the World’s Rarest Primate Is Not Directly Linked to Increasing Habitat Quality

Due to habitat loss and hunting, the Hainan gibbon (Nomascus hainanus), the world’s rarest primate, was reduced to only two social groups and seven known individuals in 1978. Following the establishment of Bawangling National Natural Reserve (BNNR), gibbon forest habitat increased within this landscape from 56 km2 in 1980 to 300 km2, and the species had increased to five groups and 35 individuals by 2021. It is important to assess whether the large increase in habitat area was responsible for gibbon population increase, or whether gibbon recovery was associated with other factors. Here we use a 21-year longitudinal dataset of Hainan gibbon population change and habitat change, combined with vegetation survey plot data for 2021, to establish an accurate distribution baseline for natural tropical broadleaf forest across the BNNR landscape from 400 to 1300 m (the elevational range of gibbons at BNNR) and within the home range for each of the five Hainan gibbon social groups. We then utilized Landsat time-series images and analysis to compute non-linear causal relationships between forest dynamics and gibbon population growth from 2000 to 2021, both across BNNR and within each gibbon group home range. Metrics of forest dynamics include change in total forest area and forest fragmentation, and metrics of gibbon population dynamics include variation in total number of individuals for the entire population and within each social group, and variation in total number of groups. Our results demonstrate that overall gibbon population growth shows a positive relationship with improved habitat quality, with a one-year time lag of population response. However, changes in numbers of individuals within social groups do not show a similar relationship with improving habitat quality, suggesting that increasing forest cover and connectivity within the BNNR landscape are not direct determinants of Hainan gibbon recovery and that other environmental and/or anthropogenic factors are likely to be involved.

The dynamics of tea plantation encroachment into forests and effect on forest landscape pattern during 1991–2021 through time series Landsat images

Tea plantations encroachment into forests has occurred in the major tea planting area of Yunnan province, China in the past decades. However, the dynamics of tea plantation encroachment into forests and the effect on forest landscape pattern is not clear. In this study, we proposed a method to evaluate the dynamic effects of tea plantation encroachment on forest landscape pattern change by using time series Landsat 5/7/8 data from 1991 to 2021 on the Google Earth Engine (GEE) platform. First, a pixel- and phenology-based algorithm was applied to generate tea plantations encroachment into forests maps for seven historical periods. Second, three indices including the tea plantations encroachment index (TPEI), the growth area of tea plantations encroachment into forests (GATEF), and the growth index of tea plantations encroachment into forests (GITEF) were used to quantify the tea plantations encroachment into forests of seven historical periods. Third, the effects of forests landscape pattern resulting from tea plantations encroachment were evaluated in class metrics and landscape metrics respectively. Our results show a total of 13,721 ha of GATEF from the early 1990s to the early 2020s. The GITEF increased from the early 1990s to the early 2010s and decreased from the early 2010s to the early 2020s in our study area. And the TPEI indicates that the edge encroachment mode dominated in our study period. Correlation analysis indicates that tea plantations encroachment level had significant correlation coefficients with landscape pattern indices (such as CONTAG (r = −0.91), ED (r = 0.95), DIVISION (r = 0.85), and SHEI (r = 0.86)), which indicates that the level of tea plantations encroachment affects significantly the forest landscape pattern. This study provides a promising approach to quantifying the effect of forest landscape pattern results from tea plantations encroachment.

Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine

Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban wetland extent and change is thus critical for improving urban planning toward sustainability. Here, we have developed a rapid method for continuous mapping of urban wetlands (MUW) by combining automatic sample migration and the random forest algorithm (SM&RF), the so-called MUW_SM&RF. Using time series Landsat images, annual training samples were generated through spectral angular distance (SAD) and time series analysis. Combined with the RF algorithm, annual wetland maps in urban areas were derived. Employing the Google Earth Engine platform (GEE), the MUW_SM&RF was evaluated in four metropolitan areas in different geographical and climatic regions of China from 1990 to 2020, including Tianjin, Hangzhou, Guangzhou, and Wuhan. In all four study areas, the generated annual wetland maps had an overall accuracy of over 87% and a Kappa coefficient above 0.815. Compared with previously published datasets, the urban wetland areas derived using the MUW_SM&RF approach achieved improved accuracy and thus demonstrated its robustness for rapid mapping of urban wetlands. Urban wetlands in all four cities had variable distribution patterns and showed significantly decreased trends in the past three decades. The annual urban wetland data product generated by the MUW_SM&RF can provide invaluable information for sustainable urban planning and management, so as for assessment related to the United Nation’s sustainable development goals.

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

Remote-Sensing Monitoring of Postfire Vegetation Dynamics in the Greater Hinggan Mountain Range Based on Long Time-Series Data: Analysis of the Effects of Six Topographic and Climatic Factors

The frequency of forest fires is increasing under global climate change, and forest fires can cause devastating disturbances to forest systems and varying degrees of recovery of forest ecosystems after a disaster. Due to the different intensity of forest fires and forest systems, and in particular the fact that forest ecological recovery is influenced by many topographical and climatic factors, the process of postfire vegetation recovery is unclear and must be studied in depth. In this study, the Greater Hinggan Mountain Range was taken as the study area. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat time-series images acquired from 2000 to 2018, this study used the spatiotemporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after forest fires. The vegetation restoration was characterized by disturbance index (DI) values, which eliminated phenological influence. Six types of topography and climatic factors (elevation, aspect, slope; temperature, precipitation, and wind speed) were coupled with DI. Through single-factor analysis of variance and multiple comparison statistical methods, it was found that there was a significant relationship between the six factors and DI, which indicated those factors had a significant impact on the restoration of forest vegetation in burned areas. The results will be useful as a reference for future monitoring and management of forest resources.

Characteristics of Greening along Altitudinal Gradients on the Qinghai–Tibet Plateau Based on Time-Series Landsat Images

The Qinghai–Tibet Plateau (QTP) is ecologically fragile and is especially sensitive to climate change. Previous studies have shown that the vegetation on the QTP is undergoing overall greening with variations along altitudinal gradients. However, the mechanisms that cause the differences in the spatiotemporal patterns of vegetation greening among different types of terrain and vegetation have not received sufficient attention. Therefore, in this study, we used a Landsat NDVI time-series for the period 1992–2020 and climate data to observe the effects of terrain and vegetation types on the spatiotemporal patterns in vegetation greening on the QTP and to analyze the factors driving this greening using the geographical detector and the velocity of the vertical movement of vegetation greenness isolines. The results showed the following: (1) The vertical movement of the vegetation greenness isolines was affected by the temperature and precipitation at all elevations. The precipitation had a more substantial effect than the temperature below 3000 m. In contrast, above 3000 m, the temperature had a greater effect than the precipitation. (2) The velocity of the vertical movement of the vegetation greenness isolines of woody plants was higher than that of herbaceous plants. (3) The influence of slope on the vertical movement of vegetation greenness isolines was more significant than that of the aspect. The results of this study provided details of the spatiotemporal differences in vegetation greening between different types of terrain and vegetation at a 30-m scale as well as of the underlying factors driving this greening. These results will help to support ecological protection policies on the QTP.

Detecting abrupt change in land cover in the eastern Hindu Kush region using Landsat time series (1988–2020)

Land cover change in the semi-arid environment of the eastern Hindu Kush region is driven by anthropogenic activities and environmental change impacts. Natural hazards, such as floods presumably influenced by climatic change, cause abrupt change of land cover. So far, little research has been conducted to investigate the spatiotemporal aspects of this abrupt change in the valleys. In order to explore the abrupt change in land cover and floods as its possible drivers in the eastern Hindu Kush, a semi-arid mountain region characterized by complex terrain, vegetation variation, and precipitation seasonality, we analyzed long-term Landsat image time series from 1988 to 2020 using Breaks For Additive Seasonal and Trend (BFAST). Overall, BFAST effectively detected abrupt change by using Landsat-derived Modified Soil Adjusted Vegetation Index (MSAVI). The results of our study indicate that approximately 95% of the study area experienced at least one abrupt change during 1988–2020. The years 1991, 1995, 1998, 2007, and 2016 were detected as the peak years, with the peaks occurring in different seasons. The annual trend of abrupt change is decreasing for the study area. The seasonality of abrupt change at the catchment level shows an increasing trend in the spring season for the southern catchments of Panjkora and Swat. The spatial distribution patterns show that abrupt change is primarily concentrated in the floodplains indicating that flooding is the primary driver of the land cover change in the region. We also demonstrated the accurate detection of past flood events (2015) based on the two case examples of Ayun, Rumbur, and Kalash valleys. The detection of the flood events was verified by fieldwork and historical high-resolution Google Earth imagery. Finally, our study provides an example of applying Landsat time series in a dry mountain region to detect abrupt changes in land cover and analyze impact of natural hazards such as floods.

Assessing degradation of lake wetlands in Bashang Plateau, China based on long-term time series Landsat images using wetland degradation index

Continuous monitoring of wetland dynamics and regional-scale assessment of wetland degradation are important to understand wetland ecosystem processes and to formulate restoration measures. The lake wetlands in Bashang Plateau play vital roles in providing essential resources and maintaining biodiversity in North China, while they were not fully investigated in previous studies. In addition, previous studies on wetland degradation assessment mostly relied on wetland area change every five or ten years. Continuous dynamics of wetlands was rarely considered. In this study, we developed a new wetland degradation index (WDI) to assess lake wetland degradation in Bashang Plateau based on time series Landsat imagery from 1985 to 2020 on Google Earth Engine platform. WDI integrates three indicators, namely lake shrinkage index (LSI), vegetation degradation index (VDI) and soil degradation index (SDI), which represents hydrodynamics of wetland inundation, vegetation, and soil conditions of wetlands, respectively. Different from previous research that utilized remotely sensed image to extract wetland every five or ten years, the LSI considers time-series continuous dynamics of lake water inundations and the transitions between water and non-water on a pixel-by-pixel basis. Our results showed that small lake wetlands with area less than 8 ha are dominant in Bashang Plateau. Both the number and the area of lake wetlands showed large interannual variations. The number (area) of lake wetlands reached 1715 (31,419 ha) in 1995–1996 and were only 493 (7,717 ha) in 2009–2010. The total area of the lake wetlands in 2019–2020 was only around 1/3 of that in 1995–1996. The LSI, VDI, SDI, as well as WDI demonstrate large spatial variations among the lake wetlands and within the lake wetlands. Wetlands in the western and central subregions experienced more severe degradation than those in the eastern subregion, and the edge of the wetlands normally showed more severe degradation. The resulted degradation grades are consistent with our field investigations. Among the 11 counties in Bashang Plateau, Zhangbei (ZB) county in the western subregion has the largest area with severe and very severe degradation grades. Degradation of lake wetlands was mainly associated with groundwater level declination which was induced by anthropogenic activities and economic development. The proposed WDI in this study can be widely applicable for regional-scale wetland degradation assessment using remote sensing techniques and thus further help decision making in lake wetland conservation and restoration.

High-Resolution Mapping of Winter Cereals in Europe by Time Series Landsat and Sentinel Images for 2016–2020

Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016–2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer’s and user’s accuracies are 91% ± 7.8% and 89% ± 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R2 as 0.77 ± 0.15 for 2016–2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository.

Mapping Three Decades of Changes in the Tropical Andean Glaciers Using Landsat Data Processed in the Earth Engine

The fast retreat of the tropical Andean glaciers (TAGs) is considered an important indicator of climate change impact on the tropics, since the TAGs provide resources to highly vulnerable mountain populations. This study aims to reconstruct the glacier coverage of the TAGs, using Landsat time-series images from 1985 to 2020, by digitally processing and classifying satellite images in the Google Earth Engine platform. We used annual reductions of the Normalized Difference Snow Index (NDSI) and spectral bands to capture the pixels with minimum snow cover. We also implemented temporal and spatial filters to have comparable maps at a multitemporal level and reduce noise and temporal inconsistencies. The results of the multitemporal analysis of this study confirm the recent and dramatic recession of the TAGs in the last three decades, in base to physical and statistical significance. The TAGs reduced from 2429.38 km2 to 1409.11 km2 between 1990 and 2020, representing a loss of 42% of the total glacier area. In addition, the time-series analysis showed more significant losses at altitudes below 5000 masl, and differentiated changes by slope, latitude, and longitude. We found a more significant percentage loss of glacier areas in countries with less coverage. The multiannual validation showed accuracy values of 92.81%, 96.32%, 90.32%, 97.56%, and 88.54% for the metrics F1 score, accuracy, kappa, precision, and recall, respectively. The results are an essential contribution to understanding the TAGs and guiding policies to mitigate climate change and the potential negative impact of freshwater shortage on the inhabitants and food production in the Andean region.

Annual variation of the landscape pattern in the Liao River Delta wetland from 1976 to 2020

The Liao River Delta wetland is an important breeding base for rare waterbirds in the world. In recent years, with the immense disturbance of human activities, the wetland has degraded. It is a prerequisite for wetland conservation and restoration to accurately extract and monitor changes in wetland area and spatial distribution. Here, based on Landsat time-series images from 1976 to 2020 in the Liao River Delta wetland, we used a synthesized upon object-oriented classification and hierarchical classification method to detect the wetland changes. We analyzed the driving factors of these change. The study found that the total wetland area have reduced by 188.00 km2 by human activities during the 45 years, among which the decrease was the most drastic from 2009 to 2012. Natural wetland area have decreased by 414.90 km2, with a large reduction in intertidal muds, mainly due to the increase in human activities after the establishment of the city in 1984. Human-made wetland area have increased by 226.90 km2, mainly influenced by changes in the transfer of intertidal muds to aquaculture ponds and paddy fields. A significant increase in the area of construction land among non-wetlands, mainly caused by the completion of Panjin New Port in 2009. Human activities were the main factors causing the extravagant changes in the Liao River Delta wetland.

Agents of Forest Disturbance in the Argentine Dry Chaco

Forest degradation in the tropics is a widespread, yet poorly understood phenomenon. This is particularly true for tropical and subtropical dry forests, where a variety of disturbances, both natural and anthropogenic, affect forest canopies. Addressing forest degradation thus requires a spatially-explicit understanding of the causes of disturbances. Here, we apply an approach for attributing agents of forest disturbance across large areas of tropical dry forests, based on the Landsat image time series. Focusing on the 489,000 km2 Argentine Dry Chaco, we derived metrics on the spectral characteristics and shape of disturbance patches. We then used these metrics in a random forests classification framework to estimate the area of logging, fire, partial clearing, riparian changes and drought. Our results highlight that partial clearing was the most widespread type of forest disturbance from 1990–to 2017, extending over 5520 km2 (±407 km2), followed by fire (4562 ± 388 km2) and logging (3891 ± 341 km2). Our analyses also reveal marked trends over time, with partial clearing generally becoming more prevalent, whereas fires declined. Comparing the spatial patterns of different disturbance types against accessibility indicators showed that fire and logging prevalence was higher closer to fields, while smallholder homesteads were associated with less burning. Roads were, surprisingly, not associated with clear trends in disturbance prevalence. To our knowledge, this is the first attribution of disturbance agents in tropical dry forests based on satellite-based indicators. While our study reveals remaining uncertainties in this attribution process, our framework has considerable potential for monitoring tropical dry forest disturbances at scale. Tropical dry forests in South America, Africa and Southeast Asia are some of the fastest disappearing ecosystems on the planet, and more robust monitoring of forest degradation in these regions is urgently needed.