Advanced land observing satellite data to identify ground vegetation in a juniper forest,northeast Iran

Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.

Channel modeling and influenced factor analysis of the broadband dual-orthogonal polarized MIMO land mobile satellite channel

An accurate, complete and realistic channel model is re- quired to accurately analyze the system performance of a multiple input multiple output （MIMO） broadband satellite mobile commu- nication system with dual-orthogonal polarized antennas （DPAs）. In most current studies, the channel characteristic matrix （CCM） is always formed by an independent identical distribution （i.i.d） model of Rayleigh or Rice distribution and nevertheless incomplete and inaccurate to describe a broadband dual-orthogonal polarized MIMO land mobile satellite （BDM-LMS） channel. This paper fo- cuses on establishing the BDM-LMS channel statistical model, which combines the 4-state broadband LMS channel model, the time selective fading features, the channel covariance information （CCI） channel model and polarization correlations between an- tennas. The modeling steps of the channel model are introduced. The main emphasis is placed on the effects of the factors, such as antenna numbers, temporal correlations, terminal environments, elevation angles and polarization correlations between the DPAs, on the channel capacity in the BDM-LMS system. Many simulation results are provided to illustrate the effects of these factors through comparisons of the transmit rate, ergodic capacity and outage capacity with different factor values. Besides, the MIMO outage capacity advantages, which indicate the benefits of MIMO com- pared with a single input single output （SISO） system under the same channel condition, are also studied under i.i.d or BDM-LMS channel.

LM-2D Successfully Launched Land Survey Satellites

LM-2D carrier rockets placed two land survey satellites into their preset orbits from the Jiuquan Satellite Launch Center at 12：11 Beijing time on December 3 and 12：14 Beijing time on December 23 respectively.The missions were the 257th and the 259th flights of the LM series launch vehicle respectively.The two land survey satellites were developed by DFH Satellite Co.,Ltd.under the China Academy of Space Technology（CAST）.

Land Surface Albedo Variations in Sanjiang Plain from 1982 to 2015: Assessing with GLASS Data

As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

Dynamics of land use change in a mining area:a case study of Nadowli District,Ghana

In a developing country like Ghana, the study of land use and land cover change(LULCC) based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Existing researches are skewed towards the southerly part of Ghana thereby leaving the Northern sectors uncovered. The maximum likelihood classification(MLC) algorithm was employed for the LULCC between 2000 and 2014 in Nadowli: an area characterized by an upsurge in mining in the Northern belt of Ghana. A spatial-social approach was utilized combining both satellite imagery and socio economic data. Land use transition matrix, land use integrated index/degree indices was used to depict the characters of the change. A semi structured interview, pair wise ranking and key informant interviews were used to correlate the socio economic impact of the different LULC. Overall changes in the landscape showed an increase in bare ground by 19.22%, open savannah by 16.8% whereas closed savanna decreased by 50%. Land use change matrix showed increasing trends of bare ground at the expense of vegetation. The integrated land use index highlighted the bare ground and built up areas rising with a decreasing closed vegetation woodlot. Large farm size are shrinking whiles majority of the people view mining as the main socio economic activity affecting the environment and the reduction in vegetation. This study therefore provides a strategic guide and a baseline data for land use policy actors in the Northern belt of Ghana. This will aid in developing models for future land use change implications in surrounding areas where mining is on the rise.

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery(Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.

Development,application,and prospects for Chinese land observation satellites

The launching of CBERS-01(China Brazil Earth Resource Satellite)in 1999,China’s first land observation satellite,signifies an unprecedented milestone in Chinese satellite remote sensing history.Since then,a large number of applications have been developed that drew upon solely CBERS-01 and other Chinese land observation satellites.The application development evolves from one satellite to multiple satellites,from one series of satellites to multiple series,from scientific research to industrial applications.Six aspects of the Chinese land observation satellite program are discussed in this paper:development status,data sharing and distribution,satellite calibration,industrial data applications,future prospects,and conclusion.

The Austrian node of the natural resources satellite remote sensing cloud service platform:examples of Sino-Austrian cooperation

The Austrian node of the Natural Resources Satellite Remote Sensing Cloud Service Platform was established in 2016 through a cooperation agreement between the Land Satellite Remote Sensing Application Center(LASAC),Ministry of Natural Resources of the Peoples Republic of China and the University of Vienna,Austria.Under this agreement panchromatic and multi-spectral data of the Chinese ZY-3 satellite are pushed to the server at the University of Vienna for use in education and research.So far,nearly 500 GB of data have been uploaded to the server.This technical note briefly introduces the ZY-3 system and illustrates the implementation of the agreement by the first China-Sat Workshop and several case studies.Some of them are already completed,others are still ongoing.They include a geometric accuracy validation of ZY-3 data,an animated visualization of image quick views on a spherical display to demonstrate the time series of the image coverage for Austria and Laos,and the use of ZY-3 data to study the spread of bark beetle in the province of Lower Austria.An accuracy study of DTMs from ZY-3 stereo data,as well as a land cover analysis and comparison of Austria with ZY-3 and other sensors are still ongoing.

The application of China’s land observation satellites within the framework of Digital Earth and its key technologies

Digital Earth is an interdisciplinary field involving space technology,information technology,and geoscience.This article introduces the land observation satellite system of China and discusses the requirements for satellite payloads in terms of spatial,temporal,and spectral resolution to establish a Digital Earth.The applications of land satellites under the framework of Digital Earth are introduced from the perspectives of data support,special subject services,and integrated information services.It is concluded that China’s land observation satellites and ground processing systems will be fundamental components of the Digital Earth system,in which satellite data and their derived information will serve as the principal information source and greatly promote applications of Digital Earth.

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).

Estimating global land surface broadband thermal-infrared emissivityusing advanced very high resolution radiometer optical data

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE)from advanced very high resolution radiometer (AVHRR) visible and nearinfrared data from 1981 through 1999 was presented. Land surface was dividedinto three types according to its normalized difference vegetation index (NDVI)values: bare soil, vegetated area, and transition zone. For each type, BBE at813.5 mm was formulated as a nonlinear function of AVHRR reflectance forChannels 1 and 2. Given difficulties in validating coarse emissivity products withground measurements, the algorithm was cross-validated by comparing retrievedBBE with BBE derived through different methods. Retrieved BBE was initiallycompared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square errorwere less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 fortransition zones, and 0.002 and 0.005 for vegetated areas. Retrieved BBE wasalso compared with BBE obtained through the NDVI threshold method. Theproposed algorithm was better than the NDVI threshold method, particularly forbare soil. Finally, retrieved BBE and BBE derived from MODIS data wereconsistent, as were the two BBE values.