Glacier Variation in Response to Climate Change in Chinese Tianshan Mountains from 1989 to 2012

Based on the 1990, 2000 and 2011 Landsat TM/ETM+ remote sensing data, glacier information of three periods in the Chinese Tianshan Mountains were extracted by using ratio threshold method(TM3/TM5) and visual interpretation, combined with digital processing of satellite images and analysis in GIS. The climate data in the surrounding area were analyzed by using linear regression, Mann-Kendall abrupt test, and Morlet wavelet analysis. Study results showed that: over the 23 years investigation, the glacier areas have markedly decreased. In the last 12 years(2000 to 2011), the rate of retreat has begun to accelerate. The most dramatic glacier shrinkage occurred in the central region, the lowest in the eastern region. The mean summer temperature and warm precipitation in Chinese Tianshan Mountains had an increasing trend, with rates of 0.22°C /10 a and 5.1mm/10 a from 1960 to 2011, respectively. Mean summer temperature have experienced a strong increase in 1998. The analysis of the results showed that the rise of mean summer temperature was the main factor that contributed to glacier shrinkage. Regional differences of glacier area changes were investigated by analyzing glacier behavior in five study sub-regions; regional differences are related to local climate, to the relative proportion of glaciers in different size classes, altitudinal and aspect distribution of glaciated areas. In addition, the lag theory indicated that glaciers may accelerate the retreat in the next decade, considering climate trends recognized for the period 2000-2011.

Estimations of Net Primary Productivity and Evapotranspiration Based on HJ-1A/B Data in Jinggangshan City, China

Net primary productivity （NPP） and evapotranspiration （ET） are two key variables in the carbon and water cycles of terrestrial ecosystems. In this study, to test a newly developed NPP algorithm designed for H J-1 A/B data and to evaluate the usage of HJ-1 A/B data in the quantitative assessment of environments, NPP and ET in Jinggangshan city, Jiangxi province, are calculated using H J-1 A/B data. The results illustrate the following： （1） The NPP and ET in Jinggangshan city in 2olo both show obvious seasonal variation, with the highest values in summer and the lowest values in winter, and relatively higher values were observed in autumn than in spring. （2） The spatial pattern indicates that the annual NPP is high in the southern area in Jinggangshan city and low in the northern area. Additionally, high NPP is distributed in forests located in areas with high elevation, and low NPP is found in croplands at low elevations. ET has no significant north-south difference, with high values in the southeast and northwest and low values in the southwest, and high ET is distributed in forests at low elevations in contrast to low ET in forests in high-elevation areas and in cropland and shrub grassland in low-elevation areas. （3） Compared to the MODIS product, the range of H J-1 NPP is larger, and the spatial pattern is more coincident with the topography. The range of H J-1 ET is smaller than that of the MODIS product, and ET is underestimated to some extent but can reflect the effect of topography. This study suggests that the algorithm can be used to estimate NPP and ET in a subtropical monsoon climate if remotely sensed images with high spatial resolution are available.

Application of Remote Sensing and GIS Technology to the Study of Desertification of Arable Lands in North Shaanxi, China

The policy of the Chinese government concerning the horizontal expansion of the cultivated land through the reclamation of desert soils result in a total increase of 665.985 km 2 during the period 1987\|1999 in North Shaanxi. This increase is less than the loss in arable land by urbanization. The accelerated rate of change in agricultural areas calls for more rapid surveys of urbanization and loss of arable land. Remote sensing has a number of advantages over ground\|based methods for such surveys. The multi\|scale concept of remote sensing data help us study the problem in four towns. Several maps were produced to analyze the situation of urban coverage in different times. The evaluation of the status, rate and risk of urbanization are based on an accepted average of urban increase as 2% of population growth per year.

Simulating Alpine Vegetation Net Primary Productivity by Remote Sensing in Qinghai Province,China

Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity （NPP） of ecological system for several years. In this paper, the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine （SVM） algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data. Finally, spatial distribution and monthly variation characteristics of NPP in Qinghai Province detail. The result showed in 2006 were analyzed in that NPP of vegetation in Qinghai Province in 2006 ranged from o to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest （314 gC/m2/a）, and sparse shrub was the smallest （101 gC/m2/a）. NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period （from April to September） with better moist and heat conditions. In July, the average NPP of vegetation reached the maximum value （43 gC/m2）. In our model, the advantage of traditional LUE models was adopted, and our study fully considered typicalcharacteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau, exactly monitoring its NPP value by remote sensing is an effective protection measure.

GIS-based spatial and temporal changes of land occupation caused by mining activities-a study in eastern part of Hubei Province

By using multi-source and multi-temporal high resolution remote sensing data and related techniques of remote sensing and geographic information systems, this paper analyzes the spatial and temporal changes of land occupation caused by mine development in four mining areas of eastern Hubei Province from 2011 to 2014, including Chengchao-Tieshan iron-copper polymetallic deposit area, Daye-Yangxin iron-copper polymetallic deposit area, E-Nan mining area, and Wuxue-Yangxin non-metallic mining area along the Yangtze River. The results show that: In the research area, land occupation of energy mine exploitation is small and in scattered distribution, with coal mine occupying the largest area, showing a downward trend in four years; land occupation of metal mines is large and in centralized distribution, with iron mine and copper mine occupying the largest area, showing a downward trend in four years; non-metallic mines are large and in great quantity, with mines of limestone for building and limestone occupying the largest area, showing a upward trend in four years.

Earth Observations in China and the World: History and Development in 50 Years

Remote sensing, which came into being at the first International Symposium on Remote Sensing of Environment (ISRSE) 50 years ago, has enabled people to obtain objecive and realistic spatial and temporal information through the application of Earth observation technologies and analyze and understand the macro-level changes of the Earth system from a spaial view. The technology of Earth observaion from space has incomparable advantages in the study of the Earth. This aricle introduces the 50-year development of Earth observaion in the world and the 30-year development of Earth observaion in China and reflects on the building of China's Earth observaion system.

Luojia-HSSR:A high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet

High Spatial and Spectral Resolution(HSSR)remote-sensing images can provide rich spectral bands and detailed ground information,but there is a relative lack of research on this new type of remote-sensing data.Although there are already some HSSR datasets for deep learning model training and testing,the data volume of these datasets is small,resulting in low classification accuracy and weak generalization ability of the trained models.In this paper,an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China.To our knowledge,it is the largest HSSR dataset to date,with 6438 pairs of 256×256 sized samples(including 3480 pairs in the training set,2209 pairs in the test set,and 749 pairs in the validation set),covering area of 161 km2 with spatial resolution 0.75 m,249 Visible and Near-Infrared(VNIR)spectral bands,and corresponding to 23 classes of field-validated ground coverage.It is an ideal experimental data for spatial-spectral feature extraction.Furthermore,a new deep learning model 3D-HRNet for interpreting HSSR images is proposed.The conv-neck in HRNet is modified to better mine the spatial information of the images.Then,a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously.Subsequently,the 3D convolution is inserted into the HRNet to optimize the performance.The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union(FWIoU)reaching 80.54%,indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images.

Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data

Net primary productivity （NPP） is an important component of the terrestrial carbon cycle. Accurately mapping the spatial-temporal variations of NPP in China is crucial for global carbon cycling study. In this study the process-based Boreal Ecosystem Productivity Simulator （BEPS） was employed to study the changes of NPP in China＇s ecosystems for the period from 2000 to 2010. The BEPS model was first validated using gross primary productivity （GPP） measured at typical flux sites and forest NPP measured at different regions. Then it was driven with leaf area index （LAI） inversed from the Moderate Resolution Imaging Spectroradiometer （MODIS） reflec- tance and land cover products and meteorological data interpolated from observations at753 national basic meteorological stations to simulate NPP at daily time steps and a spatial resolution of 500m from January 1, 2000 to December 31, 2010. Validations show that BEPS is able to capture the seasonal variations of tower-based GPP and the spatial variability of forest NPP in different regions of China. Estimated national total of annual NPP varied from 2.63 to 2.84Pg C.yr-1, averaging 2.74Pg C.yr-1 during the study period. Simulated terrestrial NPP shows spatial patterns decreasing from the east to the west and from the south to the north, in association with land cover types and climate. South-west China makes the largest contribution to the national total of NPP while NPP in the North-west account for only 3.97% of the national total. During the recent 11 years, the temporal changes of NPP were heterogamous. NPP increased in 63.8% of China＇s landmass, mainly in areas north of the Yangtze River and decreased in most areas of southern China, owing to the low temperature freezing in early 2008 and the severe drought in late 2009.

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform （SIFT） algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval （CBIR） applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis （LDA） is used to transform the local features to global features while reducing di- mensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

Conversion of savanna rangelands to bush dominated landscape in Borana,Southern Ethiopia

Introduction:Analyzing trends of land use systems and the changes occurred overtime is an effective way of assessing the impacts of land use/land cover(LULC)changes on ecosystem function.It provides important insights for understanding the spatial patterns of land use processes.The rangelands of southern Ethiopia are adversely affected by increased human population pressure,encroachment of crop cultivation,and bush encroachment.Hence,it is vital to understand the trends of rangeland vegetation cover dynamics.Methods:This paper evaluates land use/land cover changes and spatial patterns between 1987 and 2003 in Yabelo(5426 km2),Borena rangelands of southern Ethiopia.We used a combination of three Landsat Thematic Mapper(TM)1987,Landsat TM 1995 and Enhanced Thematic Mapper Plus(ETM+)2003,and local perceptions.A pixelbased supervised classification with maximum likelihood classifier was used to classify images.The accuracy of classification was assessed for 1987(81.8%),1995(84.6%),and 2003(81.3%).Results:The results showed that the Borana rangelands had undergone substantial changes during the last 16 years.Between 1987 and 2003,we observed a considerable increase in woodland cover(11.7%),bushland cover(17%),cultivated land(72.5%),and settlements(79.8%).The results showed a rapid decline in grassland cover(7.7%),shrubby grassland cover(86%),and bareland(0.7%).The spatial pattern analysis indicate that the Borana rangeland was fragmented and characterized by the proliferation of large numbers of patches with a decline in patch index,increased patch density,and irregular shape of patches within a landscape.Local communities’perceptions indicate that recurrent drought,increased human population size,and expansion of cultivation were largely responsible for the observed LULC changes in the study area.Conclusions:LULC changes contribute to rangeland degradation and weaken the traditional practices of rangeland management.We suggest appropriate management measures to halt the impact of disturbances on LULC dynamics and its implication on the livelihoods of the Borana pastoralists.

Spatial distribution of an ancient agricultural oasis in Juyan, northwestern China

Activities related to agricultural cultivation are some of the major human drivers of landscape change on the Earth＇s surface. Archaeological remains can provide qualitative evidence for studies of past agricultural development and environmental conditions. The ancient Juyan Oasis, which once flourished along the historic Silk Road, was a typical oasis of downstream inland river basins in the arid zone of northwestern China. Historical records and archaeological discoveries have qualitatively shown that the oasis supported extensive agricultural activities in this historical period from the Hart Dynasty to the early Ming Dynasty （B.C. 202-A.D. 1375）, which can be traced back to 2,000 years ago. In this study, different types of archaeological remains （including archaeological sites, ground surface artifacts, ancient cultivation ruins, and agricultural irrigation canals） that were obtained and identified from previous archaeological reports, field inspections, and remote sensing imagery were used to determine the spatial extent of the agricultural oasis in the historical period of interest. Our approach used multiple data sources in order to increase the accuracy and reliability of the results compared to previous studies. Our results distinctly suggested that much of the oasis was cultivated during the historical periods considered. Additionally, the arable land area in the historical period considered was roughly estimated to be approximately （3.39-4.75） × 10^4 ha. These findings regarding the spatial distribution of this ancient agricultural oasis and its arable land were reasonably determined to represent the ancient agricultural development that occurred in the Juyan Oasis better than results obtained from single sources of data.

Effects of ultraviolet radiation on marine primary production with reference to satellite remote sensing

Incubation experiments have shown that ultra- violet radiation （UVR） has significant influences on marine primary production （MPP）. However, existing satellite remote sensing models of MPP only consider the effects of visible light radiation, ignoring the UVR. Additionally, the ocean color satellite data currently used for MPP estimation contain no UV bands. To better understand the mechanism of MPP model development with reference to satellite remote sensing, including UVR＇s effects, we first reviewed recent studies of UVR＇s effects on phytoplankton and MPP, which highlights the need for improved satellite remote sensing of MPP. Then, based on current MPP models using visible radiation, we discussed the quantitative methods used to implement three key model variables related to UVR： the UVR intensity at the sea surface, the attenuation of UVR in the euphotic layer, and the maximum or optimal photosynthetic rate, con- sidering the effects of UVR. The implementation of these UVR-related variables could be useful in further assessing UVR＇s effects on the remote sensing of MPP, and in re- evaluating our existing knowledge of MPP estimation at large spatial scales and long-time scales related to global change.

An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations

An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.

A remote-sensing image-retrieval model based on an ensemble neural networks

With the rapid development of remote-sensing technology and the increasing number of Earth observation satellites,the volume of image datasets is growing exponentially.The management of big Earth data is also becoming increasingly complex and difficult,with the result that it can be hard for users to access the imagery that they are interested in quickly,efficiently and intelligently.To address these challenges,this paper proposes a remote-sensing image-retrieval model based on an ensemble neural networks.This model can make full use of existing training data to improve the efficiency and accuracy of the initial retrieval of remotesensing images and keep model simple.The retrieval of aerial images using the proposed model is compared with the results obtained using ten individual neural networks and two ensemble neural networks and the results show that the proposed approach has a high degree of precision.In addition,the coverage rate and mean precision show a dramatic improvement of more than 40%compared with existing methods based on normal way.And,the coverage ratio gets 86%for the top 10 return results.