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

2021 ◽  
Vol 14 (1) ◽  
pp. 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.

scholarly journals Analysis on Land-Use Change and Its Driving Mechanism in Xilingol, China, during 2000–2020 Using the Google Earth Engine

2021 ◽  
Vol 13 (24) ◽  
pp. 5134
Author(s):  
Junzhi Ye ◽  
Yunfeng Hu ◽  
Lin Zhen ◽  
Hao Wang ◽  
Yuxin Zhang
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Surface ◽  
Socioeconomic Development ◽  
Google Earth ◽  
Classification Algorithm ◽  
Driving Mechanism ◽  
Long Time Series ◽  
Long Time ◽  
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.

scholarly journals DETECÇÃO DE ÁREAS DE FLORESTAS INVARIANTES EM SÉRIES TEMPORAIS UTILIZANDO RANDOM FOREST

GEOgraphia ◽  
2021 ◽  
Vol 23 (50) ◽  
Author(s):  
Eduardo Ribeiro Lacerda ◽  
Raúl Sanchéz Vicens
Keyword(s):  
Land Use ◽  
Time Series ◽  
Random Forest ◽  
Change Detection ◽  
Vegetation Change ◽  
Google Earth ◽  
Detection Algorithms ◽  
Palabras Clave ◽  
Change Detection Algorithms ◽  
Google Earth Engine

O surgimento de algoritmos de detecção de mudanças na vegetação na última década é impressionante. Mas os resultados gerados ainda possuem ruído que precisa ser tratado com a utilização de resultados de outros mapeamentos de cobertura vegetal. Além disso, a necessidade de gerar classes de uso do solo invariantes é importante para o melhor entendimento de processos que ocorrem em áreas florestais. Pensando nisso, este trabalho busca criar uma nova forma de mapear essas áreas invariáveis que possam ser utilizadas para mascarar ruídos e também como subsídio para outros estudos de conservação e restauração. A metodologia proposta aqui usa a plataforma Google Earth Engine e um algoritmo de aprendizado de máquina: o Random Forest, para classificar áreas de floresta invariáveis usando todo o acervo de imagens da série temporal Landsat, de uma só vez. Os resultados mostraram que a nova abordagem teve melhor desempenho do que o uso de técnicas mais tradicionais como a agregação de mapeamentos de uso e cobertura anuais, com uma acurácia global de 91,7%. O trabalho busca ainda contribuir com a comunidade de sensoriamento remoto ao apresentar, após exaustivos testes, as melhores opções de variáveis a serem utilizadas neste tipo de classificação. Palavras-chave: Séries Temporais, Detecção de Mudanças, Florestas, Google Earth Engine, Random Forest.DETECTION OF INVARIANT VEGETATION AREAS IN TIME SERIES USING RANDOM FOREST ALGORITHMAbstract: The emergence of vegetation change detection algorithms in the last decade is impressive. But the results still have a lot of noise that needs to be cleaned. And the data cleaning process still uses other landcover mapping results. Besides that, the necessity to generate invariant land use classes is important to know particularly to forest areas. Thinking about that, this paper seeks to create a new form of mapping these invariant areas that can be used to mask noise and as an input on other conservation and restoration studies. The methodology proposed here uses the Google Earth Engine platform and a Random Forest algorithm to classify invariant forest areas using all the image’s collection in the time series at once. The results showed that the new approach performed better than the use of more traditional techniques such as the aggregation of annual land-use and land-cover mappings, with an overall accuracy of 91.7%. Also, this paper seeks to contribute to the remote sensing community showing after exhaustive testing, good options of variables to use on this type of work. Keywords: Time Series, Change Detection, Forests, Google Earth Engine, Random Forest.DETECCIÓN DE ÁREAS DE VEGETACIÓN INVARIANTES EN SÉRIES TEMPORALES UTILIZANDO ALGORITMO RANDOM FORESTResumen: La aparición de algoritmos de detección de cambios en la vegetación en la última década es impresionante. Pero los resultados todavía tienen muchos ruidos que deben ser eliminados. Además, el proceso de limpieza de datos se basa en otros mapas de cobertura de la tierra. Además de eso, es importante conocer la necesidad de generar clases de uso de la tierra invariables, particularmente en las áreas forestales. Pensando en eso, este artículo busca crear una nueva forma de mapear estas áreas invariantes que se pueden utilizar para enmascarar el ruido y como un aporte para otros estudios de conservación y restauración. La metodología propuesta aquí utiliza la plataforma Google Earth Engine y un algoritmo de aprendizaje de máquina: o Random Forest para clasificar áreas invariantes de bosque, utilizando a la vez todas las imágenes de la serie temporal Landsat. Los resultados encontraron que el nuevo enfoque tuvo mejor desempeño que el uso de técnicas tradicionales, con una precisión global del 91,7%. Este trabajo busca además contribuir con la comunidad de la teledetección, mostrando mediante de exhaustivas pruebas, mejores opciones de variables para utilizar en este tipo de clasificación. Palabras clave: Series de Tiempo, Detección de Cambios, Bosques, Google Earth Engine, Random Forest.

scholarly journals Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning

2019 ◽  
Vol 11 (14) ◽  
pp. 1639 ◽  
Author(s):  
Haoyu Wang ◽  
Xiang Zhao ◽  
Xin Zhang ◽  
Donghai Wu ◽  
Xiaozheng Du
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Classification ◽  
Series Data ◽  
Long Time Series ◽  
Quantitative Remote Sensing ◽  
Long Time ◽  
Year 2000

Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China’s 1982–2017 0.05° land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.

Long term observation of crustal deformation in NE-Italy

2020 ◽  
Author(s):  
Carla Braitenberg ◽  
Barbara Grillo ◽  
Alberto Pastorutti ◽  
Tommaso Pivetta
Keyword(s):  
Time Series ◽  
Crustal Deformation ◽  
Surface Deformation ◽  
Earth Tide ◽  
Atmospheric Effects ◽  
Transfer Of Knowledge ◽  
Long Time Series ◽  
Ne Italy ◽  
Long Time

<p>The long term monitoring of crustal deformation in NE-Italy derives from tilt and strainmeter observations since 1960. The stations have been maintained by three generations of scientists starting with the geodesist Antonio Marussi, keeping the instrumentation active and up to date. The decade-long time series have given observations of rare events, as the free oscillations recorded by the largest earthquakes ever recorded (Chile 1960, Sumatra 2004, Tohoku 2011) and climatic extreme events leading to extremely intense rainfalls that generate underground flooding and surface deformation (Braitenberg et al., 2019; Braitenberg, 2018). The stations have the characteristic of being representative of geodetic monitoring in karst geologic formation, that they are placed in a seismically active area which has experienced a magnitude M 6.4 earthquake in the past (1976 Gemona), and that they are influenced by the ocean loading deformation of the Adriatic Sea. The seismic area implies that the strain accumulation is an ongoing process, presently activating the elastic energy of the next earthquake. We show some relevant observations, which could hardly have been caught without such a long time series. Between 1973 and 1976 the long base horizontal pendulums of the Grotta Gigante cave gave episodic disturbances, that seized 6 months after the Gemona main shock. The hydrology of the karst is made of an underground channel system that is completely flooded during extreme rainfall and is pressurized close to simultaneously over a distance of 30 km, leading to an observable uplift and deformation of the surface (Braitenberg et al., 2019). It has been possible to extract and model this type of deformation.</p><p>The tilt and strainmeters have high accuracies and precision in the detection of crustal deformation, with the drawback to be point measurements. InSAR acquisitions cover thousands of points on the surface, but with coarser accuracy. One major problem is in the correction of atmospheric effects in the InSAR signal, which produces apparent movement in the direction of Line of Sight, uncorrelated to the real soil movement. Our present research objective is the transfer of knowledge from the signals known in the tilt and strainmeter observations to the detection of these signals with InSAR. </p><p> </p><p>Braitenberg C. (2018). The deforming and rotating Earth - A review of the 18th International Symposium on Geodynamics and Earth Tide, Trieste 2016 , Geodesy and Geodynamics, 187-196, doi::10.1016/j.geog.2018.03.003 .</p><p>Braitenberg C., Pivetta T., Barbolla D. F., Gabrovsek F., Devoti R., Nagy I. (2019). Terrain uplift due to natural hydrologic overpressure in karstic conduits. Scientific Reports, 9:3934, 1-10, doi.:10.1038/s41598-019-38814-1.</p>

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

2021 ◽  
Vol 260 ◽  
pp. 112438
Author(s):  
Kai Yan ◽  
Jiabin Pu ◽  
Taejin Park ◽  
Baodong Xu ◽  
Yelu Zeng ◽  
...  
Keyword(s):  
Time Series ◽  
Long Time Series ◽  
Long Time
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