The Identification and Geological Significance of Fault Buried in the Gasikule Salt Lake in China based on the Multi-source Remote Sensing Data

The salinity of the salt lake is an important factor to evaluate whether it contains some mineral resources or not,the fault buried in the salt lake could control the abundance of the salinity.Therefore,it is of great geological importance to identify the fault buried in the salt lake.Taking the Gasikule Salt Lake in China for example,the paper established a new method to identify the fault buried in the salt lake based on the multi-source remote sensing data including Landsat TM,SPOT-5 and ASTER data.It includes the acquisition and selection of the multi-source remote sensing data,data preprocessing,lake waterfront extraction,spectrum extraction of brine with different salinity,salinity index construction,salinity separation,analysis of the abnormal salinity and identification of the fault buried in salt lake,temperature inversion of brine and the fault verification.As a result,the study identified an important fault buried in the east of the Gasikule Salt Lake that controls the highest salinity abnormal.Because the level of the salinity is positively correlated to the mineral abundance,the result provides the important reference to identify the water body rich in mineral resources in the salt lake.

Red Tide Information Extraction Based on Multi-source Remote Sensing Data in Haizhou Bay

[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction:GF-FloodNet

Deep learning algorithms show good prospects for remote sensingflood monitoring.They mostly rely on huge amounts of labeled data.However,there is a lack of available labeled data in actual needs.In this paper,we propose a high-resolution multi-source remote sensing dataset forflood area extraction:GF-FloodNet.GF-FloodNet contains 13388 samples from Gaofen-3(GF-3)and Gaofen-2(GF-2)images.We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it.Compare with otherflood-related datasets,GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels,but also consists of multi-source remote sensing data.We thoroughly validate and evaluate the dataset using several deep learning models,including quantitative analysis,qualitative analysis,and validation on large-scale remote sensing data in real scenes.Experimental results reveal that GF-FloodNet has significant advantages by multi-source data.It can support different deep learning models for training to extractflood areas.There should be a potential optimal boundary for model training in any deep learning dataset.The boundary seems close to 4824 samples in GF-FloodNet.We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

Monitoring vegetation dynamics in East Rennell Island World Heritage Site using multi-sensor and multi-temporal remote sensing data

East Rennell of Solomon Island is the first natural site under customary law to be inscribed on UNESCO’s World Heritage List.Potential threats due to logging,mining and agriculture led to the site being declared a World Heritage in Danger in 2013.For East Rennell World Heritage Site(ERWHS)to‘shed’its‘Danger’status the management must monitor forest cover both within and outside of ERWHS.We used satellite data from multiple sources to track forest cover changes for the entire East Rennell island since 1998.95%of the island is still covered by undisturbed forests;annual average normalized difference vegetation index(NDVI)for the whole island was above 0.91 in 2015.However,vegetation cover in the island has been slowly decreasing,at a rate of–0.0011 NDVI per year between 2000 and 2015.This decrease less pronounced inside ERWHS compared to areas outside.While potential threats due to forest clearing outside ERWHS remain the forest cover change from 2000 to 2015 has been below 15%.We suggest ways in which the Government of Solomon Islands could use our data as well as unmanned air vehicles and field surveys to monitor forest cover change and ensure the future conservation of ERWHS.

Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model,in the upstream Citarum Watershed,West Java,Indonesia

In this research,the integration of remotely sensed data and Cellular Automata-Markov model(CA-Markov)have been used to analyze the dynamics of land use change and its prediction for the next year.Training phase for the CA-Markov model has been created based on the input pair of land use,which is the result of land use mapping using Maximum Likelihood(ML)algorithm.Three-map comparison has been used to evaluate process accuracy assessment of the training phase for the CA-Markov model.Furthermore,the simulation phase for the CAMarkov model can be used to predict land use map for the next year.The analyze of the dynamics of land use change and its prediction during the period 1990 to 2050 can be obtained that the land serves as a water absorbent surfaces such as primary forest,secondary forest and the mixed garden area continued to decline.Meanwhile,on build land area that can lead to reduced surface water absorbing tends to increase from year to year.The results of this research can be used as input for the next research,which aims to determine the impact of land use changes in hydrological conditions against flooding in the research area.

Spatio-temporal Variations of Temperature and Precipitation During 1951–2019 in Arid and Semiarid Region, China

Understanding the spatio-temporal variations of temperature and precipitation in the arid and semiarid region of China(ASRC)is of great significance for promoting regional eco-environmental protection and policy-making.In this study,the annual and seasonal spatio-temporal patterns of change in average temperature and precipitation and their influencing factors in the ASRC were analyzed using the Mann-Kendall test,linear tendency estimation,accumulative anomaly and the Pearson’s correlation coefficient.The results showed that both annual average temperature and average annual precipitation increased in the ASRC during 1951–2019.The temperature rose by about 1.93℃and precipitation increased by about 24 mm.The seasonal average temperature presented a significant increase trend,and the seasonal precipitation was conspicuous ascension in spring and winter.The spatio-temporal patterns of change in temperature and precipitation differed,with the southwest area showing the most obvious variation in each season.Abrupt changes in annual and seasonal average temperature and precipitation occurred mainly around the 1990 s and after 2000,respectively.Atmospheric circulation had an important effect on the trends and abrupt changes in temperature and precipitation.The East Asian summer monsoon had the largest impact on the trend of average annual temperature,as well as on the abrupt changes of annual average temperature and precipitation.Temperature and precipitation changes in the ASRC were influenced by long-term and short-term as well as direct and indirect anthropogenic and natural factors.This study identifies the characteristics of spatio-temporal variations in temperature and precipitation in the ASRC and provides a scientific reference for the formulation of climate change responses.

Generation of daily snow depth from multi-source satellite images and in situ observations

Snow depth （SD） is a key parameter for research into global climate changes and land surface processes. A method was developed to obtain daily SD images at a higher 4 km spatial resolution and higher precision with SD measurements from in situ observations and passive microwave remote sensing of Advanced Microwave Scanning Radiometer-EOS （AMSR-E） and snow cover measurements of the Interactive Multisensor Snow and Ice Mapping System （IMS）. AMSR-E SD at 25 km spatial resolution was retrieved from AMSR-E products of snow density and snow water equivalent and then corrected using the SD from in situ observations and IMS snow cover. Corrected AMSR-E SD images were then resampled to act as ＂virtual＂ in situ observations to combine with the real in situ observations to interpolate at 4 km spatial resolution SD using the Cressman method. Finally, daily SD data generation for several regions of China demonstrated that the method is well suited to the generation of higher spatial resolution SD data in regions with a lower Digital Elevation Model （DEM） but not so well suited to regions at high altitude and with an undulating terrain, such as the Tibetan Plateau. Analysis of the longer time period SD data generation for January between 2003 and 2010 in northern Xinjiang also demonstrated the feasibility of the method.

基于多源遥感数据的玛纳斯河流域冰川物质平衡变化

The glacier mass balance(GMB)is an important link between climate and water resources and has remarkable regulatory functions in river runoff.To simulate changes of the GMB and to analyze the recharge rates of glacier meltwater to runoff in the Manas River Basin(MRB)during 2000-2016,MOD11C3,TRMM 3B43 and other multi-source remote sensing data were used to drive the degree-day model.The results showed that:(1)the accuracy of the remote sensing meteorological data can be corrected effectively by constructing the temperature and precipitation inversion models,and the characteristics of glacial climate can be finely described through downscaling.The average annual temperature was-7.57°C and the annual precipitation was 410.71 mm in the glacier area of the MRB.The zone at an altitude of about 4200 m was a severe climate change zone,and above and below that zone,the temperature drop rates were-0.03°C/100 m and-0.57°C/100 m,respectively,while precipitation gradients were-2.66 mm/100 m and 4.89 mm/100 m,respectively.(2)The overall GMB was negative with a cumulative GMB of up to-9811.19 mm w.e.and the average annual GMB fluctuated between-464.85 and-632.19 mm w.e.Besides,the glacier melted slowly during 2000-2002 and 2008-2010,but rapidly for 2002-2008 and 2010-2016,while the most serious loss of the glacier occurred in 2005-2009.Moreover,the vertical changes of the GMB increased at 244.83 mm w.e./100 m in the ablation zone but only at 18.77 mm w.e./100 m in the accumulation zone.(3)The intraannual runoff strongly responded to the change of the GMB especially in July and August when the loss of the GMB accounted for 75.4%of the annual loss,and when runoff accounted for 55.1%of the annual total.Due to differences in the annual precipitation and snow meltwater outside the glacier,the interannual glacier meltwater recharge rates fluctuated between 19%and 31%.The recharge rate of glacier meltwater to runoff in the MRB was close to that for other basins in the Tianshan Mountains,which may be used as a basis to confirm the reliability of the estimated GMB results.Furthermore,based on the present findings,it is recommended that the research community pursue studies on the GMB in other alpine river basins.