Variations in the Ice Phenology and Water Level of Ayakekumu Lake,Tibetan Plateau,Derived from MODIS and Satellite Altimetry Data

The alpine lakes on the Tibetan Plateau (TP) are highly sensitive to variations in climate changes, and the lake ice phenology and water level are considered to be direct indicators of regional climate variability. In this study, we first used 14 years of moderate resolution imaging spectroradiometer snow cover products to analyse the freeze dates, ablation dates, and ice coverage durations. The lake level changes during 2002–2015 were estimated, derived from satellite altimetry and Hydroweb data. Unexpectedly, the freeze dates of lake ice greatly advanced, and the ablation dates were markedly delayed. The complete freezing duration lengthened by approximately 77 days. As a result of the warm-wet climate in the northern TP, the lake area expanded from 770 to 995 km² during 2002–2015, and the water levels rose by 4.2 m in total, at a rate of 0.3 m/year. The progressive expansion of Ayakekumu Lake profoundly affected the ice phenology. Larger water volume with larger thermal capacity likely led to the delaying of ablation dates, with the freezing point depression caused by decreasing salinity. Some new narrow and shallow bays located in southern and eastern Ayakekumu Lake were conducive to early freezing of ice. Additionally, the changes in air temperature, precipitation, potential evaporation, and sunshine duration may be related to the prolonged ice cover duration since 2002. In sum, accurate measurements of lake ice and water levels are critical for understanding the water resource balance and hydrologic cycle in arid or semi-arid regions of China.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Accuracy Assessment of Digital Elevation Model Generated by SAR Stereoscopic Technique Using COSMO-Skymed Data

Radargrammetry technique using the stereoscopic synthetic aperture radar (SAR) images is used for the generation of a digital elevation model (DEM) of a region requires only the amplitude images. SAR stereoscopic technique is analogous to the stereo-photogrammetric technique where the optical stereoscopic images are used for DEM generation. While the advantages of the SAR images are their indifference to atmospheric transparency and solar illumination conditions, the side-looking geometry of the SAR increases the complexity in the SAR stereo analysis. The availability of high spatial and temporal resolution SAR data in recent years has facilitated generation of high-resolution DEM with greater vertical accuracy using radargrammetric technique. In the present study, attempt has been made to generate the DEM of Dehra Dun region, India, from the COSMO-Skymed X-band SAR data-pair acquired at 8 days interval through the radargrammetry technique. Here, radargrammetric orientation approach has been adopted to generate the DEM and various issues and processing steps with the radargrammetry technique have been discussed. The DEM was validated with ground measured elevation values using a differential global positioning system and the root-mean-square error of the DEM was found as 7.3 m. The DEM was compared with the reference DEM of the study area generated from the Cartosat-1 stereo data with a model accuracy of 4 m.     

Study on Sampling Rule and Simplification of LiDAR Point Cloud Based on Terrain Complexity

It is difficult to obtain digital elevation model (DEM) in the mountainous regions. As an emerging technology, Light Detection and Ranging (LiDAR) is an enabling technology. However, the amount of points obtained by LiDAR is huge. When processing LiDAR point cloud, huge data will lead to a rapid decline in data processing speed, so it is necessary to thin LiDAR point cloud. In this paper, a new terrain sampling rule had been built based on the integrated terrain complexity, and then based on the rule a LiDAR point cloud simplification method, which was referred as to TCthin, had been proposed. The TCthin method was evaluated by experiments in which XUthin and Lasthin were selected as the TCthin’s comparative methods. The TCthin’s simplification degree was estimated by the simplification rate value, and the TCthin’s simplification quality was evaluated by Root Mean Square Deviation. The experimental results show that the TCthin method can thin LiDAR point cloud effectively and improve the simplification quality, and at 5 m, 10 m, 30 m scale levels, the TCthin method has a good applicability in the areas with different terrain complexity. This study has theoretical and practical value in sampling theory, thinning LiDAR point cloud, building high-precision DEM and so on.     

Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations

With the improvement in resolution, more and more useful information is contained in the space of remote sensing images, which makes the processing of remote sensing data become more complex, and it is easy to cause the curse of dimensionality and the poor recognition effect. In this paper, a remote target recognition approach named AJRC is proposed, which uses joint feature dictionary for sparse representation based on different feature information for adaptive weighting. Firstly, the features of the images are extracted to calculate the contribution weight of each eigenvalue in sparse representation, and each eigenvalue contribution weight is calculated in sparse representation. Through the adaptive method, the contribution ability of each feature value in sparse representation is strengthened, and new atoms are formed to construct feature dictionary, which makes the dictionary more discriminative. Then, the common features of each category image and the private features of a single image are extracted from the feature vector, and a joint dictionary is formed to represent the test image sparse and recognize the output of the target. Aiming at the problem that the target visual contrast difference, the low resolution and the rotation of the target with different angles, the experiment is carried out by different feature extraction methods. At the same time, we use the PCA method to reduce the feature dictionary in order to avoid dimensionality. Experiments show that compared with the existing SRC method and JSM method, this method has better recognition rate.     

Total Water Storage in Small Islands Using Multi-satellite Products: The case of Cyprus

The world’s longest international undersea water pipeline has recently been laid from southern Turkey to Northern Cyprus to address the water scarcity problems in the Cyprus Island. When completed, the project will add 19.8 million gallons of water annually for drinking and irrigation. Moreover, it will spur further development in the Turkish area divided from Greek community for four decades. Under such circumstances, satellite remote sensing provides a unique tool to evaluate the water policy for the entire island. The objective of this study is, therefore, to examine the potential of satellite-based remote sensing hydrologic models covering a small-scale Mediterranean island, which is under drought conditions. Satellite-based measurements such as GRACE (total water storage), TRMM (precipitation) and MODIS (evapotranspiration) data over a 1°–1° grid together with related information from global hydrological model, specifically WGHM and GLDAS, were collected for this purpose.     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

Sea-Level Change Over the China Sea and Its Vicinity Derived from 25-Year T/P Series Altimeter Data

The characteristics of sea-level change in the China Sea and its vicinity are studied by combining TOPEX/Poseidon (T/P), Jason-1, Jason-2, and Jason-3 altimeter data. First, the sea-surface height is computed by using monthly data via collinear adjustment, regional selection, and crossover adjustment. The sea-level anomaly (SLA) from October 1992 to July 2017 is calculated based on the difference that is obtained by the value derived from the inverse distance weighting method to interpolate the CNES_CLS15 model value at a normal point. By analyzing the satellite data at the same time in orbit, three mean bias groups over the China Sea and its vicinity are obtained: the difference between T/P and Jason-1 is ??11.76 cm, the difference between Jason-1 and Jason-2 is 9.6 cm, and the difference between Jason-2 and Jason-3 is 2.42 cm. To establish an SLA series for 25 years in the study area, the SLAs are corrected. Mean rate of sea-level rise of the Bohai Sea, Yellow Sea, East China Sea, and South China Sea of 4.87 mm/a, 2.68 mm/a, 2.88 mm/a, and 4.67 mm/a, respectively, is found by analyzing the series of SLAs.     

Gondwana-Derived Terranes Structural Mapping Using PALSAR Remote Sensing Data

Gondwana-derived terranes are now separated by major faults or suture zone, which are rarely simple and easily recognizable lineaments. Different association of ore mineral systems such as deposits of sediment-hosted/orogenic gold and granite-related minerals is discovered in collision and subduction zones of the Gondwana-derived terranes. They are associated with large-scale, terrane-bounding fault systems and broad areas of deformation. Mineralization mostly associated with structurally-controlled complex lodes, veins, sheeted veins and veinlets in diverse orientations. Recognizing the structural significance of lineaments and curvilinear is very difficult in tropical, arid and Antarctic regions due to environmental obstacles. Remote sensing data could be used to detect geological structures associated with suture zones between Gondwana-derived terranes especially for large inaccessible regions where fieldwork is limited or nonexistent. In this investigation, the Phased Array type L-band Synthetic Aperture Radar (PALSAR) satellite remote sensing data were used to map major geological structures in the Bentong-Raub Suture Zone of Peninsular Malaysia. It is one of the major structural zones in Sundaland, Southeast Asia, which forms the boundary between the Gondwana-derived the Sibumasu terrane and Sukhothai-Indochina arc. Structural features associated with sediment-hosted/orogenic gold deposits in the Central Gold Belt and tin mineralization in S-type granites in the Main Range were investigated using PALSAR data and comprehensive fieldwork. Results indicate that main faults strike along 340° to 350° that are intersected by many shear or lateral fault zones are high potential zone for gold mineralization in the Central Gold Belt. Hydrothermally alteration mineral zones and cataclastic rocks are also the other indicators of gold mineralized veins in the gold belt. High potential zones for tin mineralization are N–S strike-slip faults, fault zones and shear zones trending E–W, NE–SW and WNW–ESE in dissected crystalline granitic rocks that are associated with hydrothermal alteration zones in the Western Tin Belt. Lineament analysis using PALSAR satellite remote sensing data is a useful tool for mapping major geological structural features and detection of the boundary between the Gondwana-derived terranes and detailed structural analysis of fault systems and deformation with high potential for a variety of mineral resources, especially in tropical, arid and Antarctic regions.     

A Directed-Line-Segment-Based Photogrammetry Method to Survey the Tunneling Working Surface of Mine Roadway

Absolute orientation is a basic technical work in digital image geologic logging of underground coal mine. Traditional control-point-based absolute orientation method requires setting object space control points of the known three-dimensional coordinates, which may lead to low efficiency. Therefore, this paper proposed a point-free close-range photogrammetry absolute orientation algorithm, which utilized direction line segments including plumb line segments and line segments with known directions and lengths to identify the dimensional orientation of a stereoscopic model. Experiment results show that the precision of the orientation results is favorable. σ _X and σ _Y are as high as 0.5 mm, and σ _Z is 0.3 mm. Finally, this paper introduced the application of the proposed algorithm in rapid geological logging of coal mine roadway, which was fast and reliable, convenient and feasible.