The main evolution of remote sensing for land resources of China in recent years

2008 ◽  
Author(s):  
Ping Wang ◽  
Jianhua Ju ◽  
Zhizhong Li ◽  
Yongjiang Wang ◽  
Dengrong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Resources

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals PEMANFAATAN INDERAJA DAN SISTEM INFORMASI GEOGRAFIS (SIG) DALAM INVENTARISASI LAHAN KRITIS DI KABUPATEN KOLAKA UTARA

PERENNIAL ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 83 ◽  
Author(s):  
. Baharuddin
Keyword(s):  
Remote Sensing ◽  
Forest Area ◽  
Director General ◽  
Social Forestry ◽  
Land Resources ◽  
Suitability Analysis ◽  
Degraded Land ◽  
Productivity Data ◽  
Land Condition ◽  
Potential Land

At this time the technology required to conduct a study of land mainly related to land change and land condition analysis. To anticiapate this need for technology Remote Sensing and Geographic Information System (GIS) that can quickly and accurately to conduct a study on land resources. Critical land is a condition of land which is the result of an error in the maintenance and land management. Kolaka Utara Regency a new district which has the problem of land mainly biophysical and social condition.In this case the methode used is to land suitability analysis approach based on FAO and determination based on the rules Director General Land Rehabilitation and Social Forestry – DEPHUT, SK.167/V-SET/2004, combined with productivity data field based on their utilization. Based on the analysis with Remote Sensing and GIS in Kolaka Utara Regency result that is dominated by protected forest area of 163.376,51 ha (53,0 %), followed by limited production forest 65.887,63 ha (21 %), cultivation area 60.977,75 ha. (19,6 %) and production forest convertion 20.258,94 ha (6.5 %). Land use and land cover largest is forest area of 177.850,02 ha (57,3 %), cocoa palantation area 91.066.80 ha (29,3 %), garden mixed area18.517,76 ha (6,0 %), shrub area 11.615,40 ha (3,7 %), and clove plantation area 4.067,93 ha ( 1,3 %). Potential land critical area is 39.040,96 ha (12,6 %), land rather critical is 13.513,43 ha (4,4 %), critical land is 47.534,21 ha, (15,3 %) and land critical immensely is 19.509,42 ha (6,3 %), and land while the rest is not critical is 190.902,81 ha (61,5 %). Degraded land spread in all areas well outside the region (cultivate area) and within region (forest area). Key words: Inderaja, SIG, critical land

scholarly journals Analytical review of remote sensing data sources suitable for mapping of land resource in Chernivtsi region

2013 ◽  
pp. 331-338
Author(s):  
J. Smirnov
Keyword(s):  
Remote Sensing ◽  
Key Words ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Remote Sensing Data ◽  
Data Sources ◽  
Land Resource ◽  
Land Resources ◽  
Sensing Data ◽  
Analytical Review

In the article described the sources of remote sensing data and analyzed their suitability for involvement in the process Chernivtsi region land resources mapping. Taken into account space surveying systems of different spatial resolution and aerial photographic surveys. As a result, have been identified the best sources of data that can be used in the Chernivtsi region land resources mapping. Key words: land resources, remote sensing, satellite imagery, mapping of land resources, sources of remote sensing data.

scholarly journals STUDY OF LAND RESOURCES AT SIWA OASIS USING REMOTE SENSING AND GIS TECHNIQUEES

2013 ◽  
Vol 4 (8) ◽  
pp. 757-773
Author(s):  
M. A. Thabet ◽  
A. A. Abdel Hady ◽  
W. A. M. Abd El Kawy ◽  
A. H. EL-Nahry
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Land Resources

scholarly journals Monitoring and Mathematical Model Analysis of Dynamic Changes in Land Resources Based on SAR Sensor Image

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ying Li
Keyword(s):  
Remote Sensing ◽  
Dynamic Change ◽  
Current Situation ◽  
Sensor Data ◽  
Land Resource ◽  
Land Resources ◽  
Dynamic Information ◽  
Dynamic Changes ◽  
Time Service ◽  
Change Monitoring

The monitoring and analysis of dynamic changes in land resources can detect the changes of land aimed at a single-band or multiband remote sensing image of multiple phases in a given region or target with image processing methods and can also extract the change information and realize remote sensing monitoring through the comprehensive analysis of multiphase remote sensing images. Synthetic aperture radar (SAR) image change monitoring technology, with the advantages of high resolution, high precision, real-time service, and rapid imaging, can achieve qualitative or quantitative analysis of targets and is gradually widely used in quarterly monitoring, emergency monitoring, postbatch verification, law-enforcement inspection and land inspection, and other remote sensing data acquisitions and analyses. Therefore, on the basis of summarizing the research results of previous research works, this paper expounded the current situation and significance of the researches on the monitoring and analysis of dynamic changes in land resources; elaborated the development background, current situation, and future challenges of SAR sensor data; introduced the methods and principles of band setting, polarization mode, geometric correction, and image filtering; proposed the status target identification of land resources; explored the dynamic information discovery of land resources; conducted the dynamic change monitoring of land resources based on SAR sensor data; analyzed the basis and characteristics of SAR sensor data; performed the generalization and optimization of land resource information; demonstrated the dynamic change analysis of land resources based on SAR sensor data; compared the acceptance ability and accuracy of SAR sensor data; and discussed the discovery and extraction of dynamic information of land resources. The results show that the SAR sensor data can monitor the characteristics of scattering points in land resource observation scenes and can obtain the change information of ground object by distance component and band component, so that the SAR system can make two-dimensional imaging of land resources directly in front of the receiving platform. Thus, the SAR data obtained by multisystem parameters shows great application potential in land resource monitoring, which provides the possibility of decoupling to remove land resources and surface roughness and thus provides possible solutions for land resource analysis in complex environment. The results of this paper provide a reference for the follow-up studies on the monitoring and analysis of dynamic changes in land resources based on SAR sensor data.

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