Remote Sensing Computing Model for Forest Monitoring in Cloud

2018 ◽  
Author(s):  
Adriana Tufa ◽  
Ionut Boicu ◽  
Ion-Dorinel Filip ◽  
Catalin Negru ◽  
Florin Pop
Keyword(s):  
Remote Sensing ◽  
Forest Monitoring ◽  
Computing Model

scholarly journals Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 327 ◽  
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Relevant Information ◽  
Forest Monitoring ◽  
Machine Learning Techniques ◽  
Planted Forests ◽  
Systematic Analysis ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

scholarly journals Patterns and Drivers of Deforestation and Forest Degradation in Myanmar

2021 ◽  
Vol 13 (14) ◽  
pp. 7539
Author(s):  
Zaw Naing Tun ◽  
Paul Dargusch ◽  
DJ McMoran ◽  
Clive McAlpine ◽  
Genia Hill
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Local Governments ◽  
Forest Canopy ◽  
Forest Cover ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Map Algebra ◽  
Change Detection Analysis ◽  
Detection Analysis

Myanmar is one of the most forested countries of mainland Southeast Asia and is a globally important biodiversity hotspot. However, forest cover has declined from 58% in 1990 to 44% in 2015. The aim of this paper was to understand the patterns and drivers of deforestation and forest degradation in Myanmar since 2005, and to identify possible policy interventions for improving Myanmar’s forest management. Remote sensing derived land cover maps of 2005, 2010 and 2015 were accessed from the Forest Department, Myanmar. Post-classification change detection analysis and cross tabulation were completed using spatial analyst and map algebra tools in ArcGIS (10.6) software. The results showed the overall annual rate of forest cover loss was 2.58% between 2005 and 2010, but declined to 0.97% between 2010 and 2015. The change detection analysis showed that deforestation in Myanmar occurred mainly through the degradation of forest canopy associated with logging rather than forest clearing. We propose that strengthening the protected area system in Myanmar, and community participation in forest conservation and management. There needs to be a reduction in centralisation of forestry management by sharing responsibilities with local governments and the movement away from corruption in the timber trading industry through the formation of local-based small and medium enterprises. We also recommend the development of a forest monitoring program using advanced remote sensing and GIS technologies.

SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system

2015 ◽  
Vol 156 ◽  
pp. 335-348 ◽  
Author(s):  
Eric A. Lehmann ◽  
Peter Caccetta ◽  
Kim Lowell ◽  
Anthea Mitchell ◽  
Zheng-Shu Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Monitoring System ◽  
Large Scale ◽  
Forest Monitoring ◽  
Optical Remote Sensing

scholarly journals Monitoring coniferous forests using remote sensing data (on the example of Tukhlyanske forestry)

2021 ◽  
Vol 42 (II) ◽  
pp. 99-108
Author(s):  
Kh. BURSHTYNSKA ◽  
◽  
Y. DEKALIUK ◽  
Keyword(s):  
Remote Sensing ◽  
Biological Diversity ◽  
Remote Sensing Data ◽  
Forest Monitoring ◽  
Practical Significance ◽  
Coniferous Forests ◽  
Maximum Probability ◽  
Structure Detection ◽  
Uav Images

The purpose of the work is to consider the state of coniferous forests of the Tukhlyanske forestry of the Precarpathian region. Changes in land cover, pollution of air, water and soil, and deterioration of their quality, loss of biological diversity occur for forest ecosystems at the regional and global levels. Climate change, rising temperatures and declining rainfall are provoking the development of pests that are most common in coniferous forests. Remote sensing technologies allow to create forest monitoring systems, including determination of plantation structure, detection of changes in forests due to fires, deforestation, environmental problems, in particular forest drying. The method of detecting changes in forests is based on the use of high-resolution space imagery and on the processing of images obtained from unmanned aerial vehicles to identify healthy, dried and partially damaged by drying conifers in test areas. The result of the study is an image obtained by the method of controlled classification. The accuracy of the classification depends on the choice of signatures, and for that the UAV images are used. Scientific novelty and practical significance. A method for the identification of different states of coniferous forests using the method of controlled classification by the algorithm of maximum probability is proposed. The choice of class signatures is fundamental to solving the problem. The technique can be applied in various structures of forestry

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals Forest Cover Change Analysis Using Remote Sensing Techniques in Madhupur Sal Forest of Bangladesh

2018 ◽  
Vol 10 (2) ◽  
pp. 73-78
Author(s):  
MA Salam ◽  
MAT Pramanik
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Driving Forces ◽  
Forest Resources ◽  
Forest Monitoring ◽  
Forest Damage ◽  
Landsat 8 ◽  
Forest Cover Change ◽  
Change Analysis ◽  
Sal Forest

Deforestation, degradation, damages, transformation and over exploitation of forests are the common problem in different parts of the world. Timely monitoring and assessment of forest resources may help to address and identify the above mentioned problems and thus proper guidance may be given the forest resources manager for rational planning and management of forests. Apart from the conventional methods of forest monitoring, remote sensing with its unique capability of synoptic viewing, real time and repetitive nature offers a potential tool for monitoring and evaluation of forest resources and hence remote sensing technology has been successfully used in various studies like forest inventory, monitoring of forest cover changes and forest damage assessment. In the present research forest cover change analysis in ‘Madhupur Sal Forest’ located in central part of Bangladesh has been investigated using satellite remote sensing data and spatial analysis. Transformation of ‘Sal forest’ to other landuse has been studied using the Landsat MSS (Multi Spectral Scanner) data of 1973 and Landsat 8 OLI (Operational Land Imager) data of 2015. Driving forces behind the transformation of ‘Sal forest’ has also been investigated through GPS (Global Positioning System) based ground verification and interview with the people living in the locality.J. Environ. Sci. & Natural Resources, 10(2): 73-78 2017

LiDAR remote sensing of forest structure

2003 ◽  
Vol 27 (1) ◽  
pp. 88-106 ◽  
Author(s):  
Kevin Lim ◽  
Paul Treitz ◽  
Michael Wulder ◽  
Benoît St-Onge ◽  
Martin Flood
Keyword(s):  
Remote Sensing ◽  
Forest Structure ◽  
Forest Ecosystems ◽  
Canopy Height ◽  
Forest Monitoring ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Canopy Volume

Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above-ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

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