Mapping the annual dynamics of cultivated land in typical area of the Middle-lower Yangtze plain using long time-series of Landsat images based on Google Earth Engine

Large-scale, long time-series, and high-precision land-use mapping is the basis for assessing the evolution and sustainability of ecosystems in Xilingol, the Inner Mongolia Autonomous Region, China. Based on Google Earth Engine (GEE) and Landsat satellite remote-sensing images, the random forest (RF) classification algorithm was applied to create a yearly land-use/land-cover change (LULC) dataset in Xilingol during the past 20 years (2000–2020) and to examine the spatiotemporal characteristics, dynamic changes, and driving mechanisms of LULC using principal component analysis and multiple linear stepwise regression methods. The main findings are summarized as follows. (1) The RF classification algorithm supported by the GEE platform enables fast and accurate acquisition of the LULC dataset, and the overall accuracy is 0.88 ± 0.01. (2) The ecological condition across Xilingol has improved significantly in the last 20 years (2000–2020), and the area of vegetation (grassland and woodland) has increased. Specifically, the area of high-coverage grass and woodland increases (+13.26%, +1.19%), while the area of water and moderate- and low-coverage grass decreases (−15.96%, −7.23%, and −3.27%). Cropland increases first and then decreases (−34.85%) and is mainly distributed in the southeast. The area of deserted land decreases in the south and increases in the center and north, but the total area still decreases (−13.74%). The built-up land expands rapidly (+108.45%). (3) In addition, our results suggest that regional socioeconomic development factors are the primary causes of changes in built-up land, and climate-related factors are the primary causes of water changes, but the correlations between other land-use types and relevant factors are not significant (cropland and grassland). We conclude that the GEE+RF method is capable of automated, long time-series, and high-accuracy land-use mapping, and further changes in climatic, environmental, and socioeconomic development factors, i.e., climate warming and rotational grazing, might have significant implications on regional land surface morphology and landscape dynamics.

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Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.03.014 ◽

2020 ◽

Vol 163 ◽

pp. 312-326 ◽

Cited By ~ 9

Author(s):

Xinxin Wang ◽

Xiangming Xiao ◽

Zhenhua Zou ◽

Luyao Hou ◽

Yuanwei Qin ◽

...

Keyword(s):

Time Series ◽

Coastal Wetlands ◽

Google Earth ◽

Landsat Images ◽

Google Earth Engine

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Analysis of the Spatial and Temporal Pattern of Changes in Abandoned Farmland Based on Long Time Series of Remote Sensing Data

Remote Sensing ◽

10.3390/rs13132549 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2549

Author(s):

Zhonghui Wei ◽

Xiaohe Gu ◽

Qian Sun ◽

Xueqian Hu ◽

Yunbing Gao

Keyword(s):

Remote Sensing ◽

Time Series ◽

Vegetation Index ◽

Average Rate ◽

Landsat 8 ◽

Cultivated Land ◽

Farmland Abandonment ◽

Abandoned Farmland ◽

Long Time Series ◽

Long Time

With the rapid increase in the costs of rural labour and the adjustment of planting structures, the phenomenon of farmland abandonment has appeared in China. It is of great significance to promptly and accurately grasp the information on dynamic temporal and spatial changes in abandoned farmland to ensure national food security and the sustainable use of cultivated land. Luquan District in Hebei, China was selected as the research area based on multispectral images from Sentinel-2A, Landsat-7, and Landsat-8 combined with methods of random forest (RF) classification and vegetation index change detection. Rules for the identification of abandoned farmland were also developed, and remote sensing monitoring of the abandonment status of the cultivated land was also carried out in the study area. We also obtained the spatial distribution of abandoned and reclaimed farmland and analysed the frequency of farmland abandonment. The results show that the overall accuracy of the land-use time-series map ranged from 90.20% to 96.92% for the study period of 2010–2020. The average rate of farmland abandonment in the study area was 10.62%, with the lowest rate (5.83%) in 2020 and the highest (14.09%) in 2012. From 2011 to 2020, the maximum farmland abandonment area was 3906.02 hm2, and the minimum area was 1618.74 hm2. The farmland abandonment area showed a trend of first increasing and then decreasing. From 2012 to 2020, the maximum area of reclaimed farmland was 291.49 hm2, and the highest rate of reclamation was 14.26%. The overall reclamation rate was low. The abandonment frequency of most of the abandoned farmland was 1–3 years, covering an area of 8193.73 hm2, which comprised 79% of the total area of abandoned farmland. The frequency of abandonment was inversely proportional to the area of abandoned farmland. Farmland abandonment mainly occurred in hilly areas. We expect that our results can provide case studies for long time series in farmland abandonment research and can provide a reference for studying the driving factors, risk assessment, and policymaking with respect to abandoned farmland.

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Monitoring spatial and temporal patterns of rubber plantation dynamics using time-series Landsat images and Google Earth Engine

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3110763 ◽

2021 ◽

pp. 1-1

Author(s):

Yuchen Li ◽

Chenli Liu ◽

Jun Zhang ◽

Ping Zhang ◽

Yufei Xue

Keyword(s):

Time Series ◽

Temporal Patterns ◽

Google Earth ◽

Rubber Plantation ◽

Spatial And Temporal Patterns ◽

Landsat Images ◽

Google Earth Engine

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Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine

Remote Sensing ◽

10.3390/rs12233942 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3942

Author(s):

Mitchell T. Bonney ◽

Yuhong He ◽

Soe W. Myint

Keyword(s):

Time Series ◽

Vegetation Change ◽

Fire History ◽

South Australia ◽

Fire Frequency ◽

Google Earth ◽

Bivariate Analysis ◽

Landsat Images ◽

Google Earth Engine ◽

Landscape Level

The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.

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30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Remote Sensing ◽

10.3390/rs11050489 ◽

2019 ◽

Vol 11 (5) ◽

pp. 489 ◽

Cited By ~ 25

Author(s):

Tengfei Long ◽

Zhaoming Zhang ◽

Guojin He ◽

Weili Jiao ◽

Chao Tang ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Google Earth ◽

Burned Area ◽

Landsat 8 ◽

Landsat Images ◽

Burned Areas ◽

Random Decision Forests ◽

Burned Area Mapping ◽

Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

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Assessment of the environmental changes in arid and semiarid mining areas using long time-series Landsat images

Environmental Science and Pollution Research ◽

10.1007/s11356-021-14417-0 ◽

2021 ◽

Author(s):

Zhen Yang ◽

Yingying Shen ◽

Huawei Jiang ◽

Feisheng Feng ◽

Qianlin Dong

Keyword(s):

Time Series ◽

Environmental Changes ◽

Landsat Images ◽

Mining Areas ◽

Long Time Series ◽

Long Time ◽

Arid And Semiarid

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Lake Area Analysis Using Exponential Smoothing Model and Long Time-Series Landsat Images in Wuhan, China

Sustainability ◽

10.3390/su10010149 ◽

2018 ◽

Vol 10 (2) ◽

pp. 149 ◽

Cited By ~ 8

Author(s):

Gonghao Duan ◽

Ruiqing Niu

Keyword(s):

Time Series ◽

Exponential Smoothing ◽

Lake Area ◽

Landsat Images ◽

Long Time Series ◽

Long Time

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Long Time-Series Mapping and Change Detection of Coastal Zone Land Use Based on Google Earth Engine and Multi-Source Data Fusion

Remote Sensing ◽

10.3390/rs14010001 ◽

2021 ◽

Vol 14 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Dong Chen ◽

Yafei Wang ◽

Zhenyu Shen ◽

Jinfeng Liao ◽

Jiezhi Chen ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Change Detection ◽

Coastal Zone ◽

Google Earth ◽

Coastal Zones ◽

Long Time Series ◽

Coastal Aquaculture ◽

Long Time

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

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