Advances in remote sensing technologies for forest surveys and management

1990 ◽  
Vol 20 (4) ◽  
pp. 464-483 ◽  
Author(s):  
Donald G. Leckie
Keyword(s):  
Remote Sensing ◽  
Forest Management ◽  
Damage Assessment ◽  
Forest Type ◽  
New Technology ◽  
Remote Sensing Data ◽  
Forest Damage ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Techniques

Canadian forest management has had a long history of developing and implementing remote sensing technology and is a major user of remote sensing. Despite difficulties in developing and implementing new digital remote sensing techniques, several key developments in Canadian forest management and in remote sensing and computer technology make the development and implementation of new remote sensing techniques at this time feasible and appropriate. Integration of different remote sensing technologies, remote sensing data with other information sources through geographic information systems, and remote sensing interpretations with forest management systems and practices are critical. Current capabilities and new advances in remote sensing technology for forest survey (excluding forest damage assessment) are discussed. Satellite imagery is a cost-effective tool for broad forest type mapping. New satellite systems improve this capability, but their major impact will be in inventories for new clear-cut and burned areas. Advances in linear array imager technology and lidar systems may lead to development of an end to end inventory mapping system. This system would provide an alternative to aerial photography and current mapping methods and could revolutionize the way forests are inventoried. Imaging spectrometry is a new technology with applications in damage assessment, but as yet has limited potential for assisting in other forest surveys. Spaceborne imaging radar systems are being developed for the 1990s. These systems can produce imagery under cloudy conditions. Their major impact on forestry will be to provide an alternative to visible-infrared satellite data for inventory update.

scholarly journals Forest Damage Assessment Using Deep Learning on High Resolution Remote Sensing Data

2019 ◽  
Vol 11 (17) ◽  
pp. 1976 ◽  
Author(s):  
Zayd Mahmoud Hamdi ◽  
Melanie Brandmeier ◽  
Christoph Straub
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Forest Management ◽  
High Resolution ◽  
Damage Assessment ◽  
Remote Sensing Data ◽  
Forest Area ◽  
Forest Damage ◽  
Sensing Data

Storms can cause significant damage to forest areas, affecting biodiversity and infrastructure and leading to economic loss. Thus, rapid detection and mapping of windthrows are crucially important for forest management. Recent advances in computer vision have led to highly-accurate image classification algorithms such as Convolutional Neural Network (CNN) architectures. In this study, we tested and implemented an algorithm based on CNNs in an ArcGIS environment for automatic detection and mapping of damaged areas. The algorithm was trained and tested on a forest area in Bavaria, Germany. . It is a based on a modified U-Net architecture that was optimized for the pixelwise classification of multispectral aerial remote sensing data. The neural network was trained on labeled damaged areas from after-storm aerial orthophotos of a ca. 109 k m 2 forest area with RGB and NIR bands and 0.2-m spatial resolution. Around 10 7 pixels of labeled data were used in the process. Once the network is trained, predictions on further datasets can be computed within seconds, depending on the size of the input raster and the computational power used. The overall accuracy on our test dataset was 92 % . During visual validation, labeling errors were found in the reference data that somewhat biased the results because the algorithm in some instance performed better than the human labeling procedure, while missing areas affected by shadows. Our results are very good in terms of precision, and the methods introduced in this paper have several additional advantages compared to traditional methods: CNNs automatically detect high- and low-level features in the data, leading to high classification accuracies, while only one after-storm image is needed in comparison to two images for approaches based on change detection. Furthermore, flight parameters do not affect the results in the same way as for approaches that require DSMs and DTMs as the classification is only based on the image data themselves, and errors occurring in the computation of DSMs and DTMs do not affect the results with respect to the z component. The integration into the ArcGIS Platform allows a streamlined workflow for forest management, as the results can be accessed by mobile devices in the field to allow for high-accuracy ground-truthing and additional mapping that can be synchronized back into the database. Our results and the provided automatic workflow highlight the potential of deep learning on high-resolution imagery and GIS for fast and efficient post-disaster damage assessment as a first step of disaster management.

Information Extraction and Metallogenic Prediction of Qiangduo Area in Tibet Based on Multi-Source Remote Sensing Data

2014 ◽  
Vol 962-965 ◽  
pp. 127-131
Author(s):  
Xin Xing Liu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Geological Mapping ◽  
Advanced Technology ◽  
Data Types ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Metallogenic Prediction

Remote sensing technology as a kind of new and advanced technology has been playing an important role in geological mapping and prospecting. A single kind of remote sensing data always has both advantages and disadvantages. And with multispectral remote sensing data types increasing, the integrated application of multi-source remote sensing data will be one of the development trend of remote sensing geology. In this paper, comprehensive utilization of multi-source remote sensing data such as ETM+, ASTER, Worldview-II and DEM, lithology and geological structure of Qiangduo area in Tibet were interpreted in different levels and mineralized alteration information also was extracted. Then on the basis of modern metallogenic theory, analyzed the multiple mineralization favorite information, established the remote sensing prediction model, and on the GIS platform, carried out metallogenic prediction of the study area. The field validation shows that the results of the prediction are relatively accurate and remote sensing technology can improve the efficiency of geological work.

Assessment on 2000-2010 Ecosystem Function Changes of Yiwulvshan National Nature Scenic Area

2014 ◽  
Vol 1051 ◽  
pp. 489-494
Author(s):  
Xiao Chen Wang ◽  
Jing Hai Zhu ◽  
Yuan Man Hu ◽  
Wei Ling Liu
Keyword(s):  
Remote Sensing ◽  
Ecosystem Function ◽  
Primary Productivity ◽  
Water Conservation ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
The Past ◽  
Remote Sensing Technology ◽  
Sensing Technology

Based on the remote-sensing data and ground data, this study is conducted on the ecosystem function of Yiwulvshan National Nature Scenic Area (hereinafter as “Yiwulvshan Scenic Area”) from 2000 to 2010 with the GIS (geographic information system) and RS (remote sensing) technology, so as to provide reference for better environmental protection of the scenic area. It is shown from the results that there is no obvious change of land use in Yiwulvshan Scenic Area; while the capacity for soil and water conservation is slightly improved mainly due to increase of vegetation coverage; the vegetation net primary productivity declines somewhat about 5.27% in past 10 years; and biodiversity is slightly increased. As a whole, the ecosystem function of Yiwulvshan Scenic Area basically kept stable in the past 10 years, which indicated that the existing regulations can effectively protect the ecological function of the Scenic Area.

A METHODOLOGY FOR MAPPING AND MONITORING CROPLAND SOIL EROSION

1987 ◽  
Vol 67 (3) ◽  
pp. 433-444 ◽  
Author(s):  
JOSEF CIHLAR
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Satellite Remote Sensing ◽  
Field Evaluation ◽  
Rill Erosion ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Long Term Trends ◽  
Remote Sensing Techniques

A methodology is described for mapping and monitoring the erosion of soil by water, using remote sensing techniques and the universal soil loss equation as the primary tools. Four aspects are covered: mapping baseline sheet and rill erosion, monitoring actual rill and gully erosion, estimating changes in potential sheet and rill erosion, and determining long-term trends. A successful field evaluation of the methodology was undertaken in a potato-growing area of New Brunswick. The implementation of the procedure using state-of-the-art microcomputer and satellite remote sensing technology is proposed. Key words: Soil erosion, remote sensing, geographic information systems

Remote sensing technology for postdisaster building damage assessment

2022 ◽  
pp. 509-521
Author(s):  
Mohammad Kakooei ◽  
Arsalan Ghorbanian ◽  
Yasser Baleghi ◽  
Meisam Amani ◽  
Andrea Nascetti
Keyword(s):  
Remote Sensing ◽  
Damage Assessment ◽  
Building Damage ◽  
Remote Sensing Technology ◽  
Sensing Technology

scholarly journals Mangrove Conservation Strategy in Bedono Village, Sayung District, Demak Regency Based on Remote Sensing Satellite Data

2019 ◽  
Vol 125 ◽  
pp. 02010
Author(s):  
Atmari ◽  
Denny Nugroho Sugianto ◽  
Fuad Muhammad
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Satellite Data ◽  
Remote Sensing Data ◽  
Mangrove Ecosystem ◽  
Conservation Strategy ◽  
Government Agencies ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Mangrove Conservation

The mangrove ecosystem is an ecosystem unit in the form of a stretch containing biological natural resources dominated by trees that grow in coastal areas and river estuaries and is influenced by tides. The purpose of this study was to determine the vegetation in Bedono Village, Sayung District, Demak Regency by using remote sensing technology for conservation. Remote sensing technology has recently been used by government agencies or non-government agencies because it is considered more effective and efficient. Based on remote sensing data, the mangrove ecosystem in Bedono Village, Sayung Subdistrict, Demak Regency experienced an increase in the category of moderate and heavy mangroves in 2004-2009.

scholarly journals Remote Sensing Imagery Sensors and Image Interpretation

2021 ◽  
Vol 9 (9) ◽  
pp. 606-607
Author(s):  
Smriti Khare
Keyword(s):  
Remote Sensing ◽  
Image Interpretation ◽  
Remote Sensing Data ◽  
Basic Feature ◽  
Geographical Information ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Remote Sensing Technology ◽  
The Earth ◽  
Sensing Technology

Abstract: Remote sensing a universal term that represents the activity of gaining data of an object with a sensor that is genuinely away from the item from an aircraft or satellite. Special cameras are used to gather remotely sensed picture which help the analyst to sense the things about the earth. Remote sensing makes it probable to assemble data of risky or unapproachable zones. Remote sensing data allows researchers to examine the biosphere's biotic and abiotic segments. Remote sensing is used in various fields to acquire the data which is widely used in Geographical Information System. Image interpretation is most basic feature of remote sensing technology. Image interpretation is a process of recognizing the images and collect information for multiple uses. The photographs are usually taken by satellite or aircrafts. Keywords: Image interpretation, image interpretation devices, sensor, remote sensing, data analysis.

scholarly journals VEGETATION MONITORING BY REMOTE SENSING TECHNOLOGY FOR UNINHABITED ISLANDS OF THE XISHA ISLANDS

2018 ◽  
Vol XLII-3 ◽  
pp. 853-856
Author(s):  
L. Li ◽  
Y. Guo ◽  
X. Wu
Keyword(s):  
Remote Sensing ◽  
Vegetation Change ◽  
Remote Sensing Data ◽  
Vegetation Monitoring ◽  
Xisha Islands ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Tropical Coral ◽  
South Sea ◽  
Stable Growth

The Xisha islands are tropical coral islands in the south sea of China, with special ecological environment. As far away from the inland, they are more sensitive to climate change than inland, and are looked as the window to reflect global environment changes. Since Sansha city established, some of islands were developed. The uninhabited islands are decreasing. To discover the changes of uninhabited islands become more impending. In order to find out the natural status of uninhabited islands, monitoring four years vegetation change of 2002, 2010, 2013 and 2016. In addition, monitoring the typical uninhabited island and sandbar vegetation by making the most of existed high resolution remote sensing data, nine years from 2002 to 2013 and six months in 2012. The results show that the sandbars are in stable growth stage, especially after 2010, the vegetation start appeared. Meanwhile, analysis the vegetation variation of the uninhabited islands and sandbars.

scholarly journals Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

2021 ◽  
Vol 887 (1) ◽  
pp. 012004
Author(s):  
A. K. Hayati ◽  
Y.F. Hestrio ◽  
N. Cendiana ◽  
K. Kustiyo
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multitemporal Remote Sensing ◽  
Multitemporal Data

Abstract Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

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