Polarimetric observation of forest cover in radar remote sensing

1996 ◽  
Vol 51 (11-12) ◽  
pp. 641-655
Author(s):  
Zied Belhadj ◽  
Safwan El Assad ◽  
Joseph Saillard
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Polarimetric Observation ◽  
Radar Remote Sensing

scholarly journals Radar remote sensing for the delineation of forest cover maps and the detection of deforestation

2006 ◽  
Vol 79 (5) ◽  
pp. 589-597 ◽  
Author(s):  
C. Thiel ◽  
P. Drezet ◽  
C. Weise ◽  
S. Quegan ◽  
C. Schmullius
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Radar Remote Sensing

scholarly journals Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0133583 ◽  
Author(s):  
John Devaney ◽  
Brian Barrett ◽  
Frank Barrett ◽  
John Redmond ◽  
John O`Halloran
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Forest Cover ◽  
Radar Remote Sensing

scholarly journals Identifying mangrove forests using radar remote sensing data

2016 ◽  
Vol 19 (2) ◽  
pp. 113-121
Author(s):  
Phung Phi Hoang ◽  
Nguyen Dao Lam ◽  
Viet Bach Pham
Keyword(s):  
Remote Sensing ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Mangrove Forests ◽  
Dual Polarization ◽  
Alos Palsar ◽  
Radar Remote Sensing ◽  
Envisat Asar ◽  
Cover Density ◽  
Level Identification

Mangrove is one of the ecologically significant ecosystems in coastal areas, both on environment and biological resources. Radar remote sensing demonstrates a high potential in detecting, identifying, mapping and monitoring mangrove forests. Advantages of radar remote sensing are that almost unaffected by the weather phenomena in the atmosphere, e.g. clouds so that it can acquire images at day and night times. This study considers possibilities of ALOS PALSAR (L-band) and ENVISAT ASAR APP (C-band) for identifying mangrove forests. Results show that using single-date data of ENVISAT ASAR APP including dual polarization HH&HV are difficult to classify mangrove objects; whilst single-date data of ALOS PALSAR with dual polarization HH&HV have a better classification for tree density but at species level identification (e.g. Avicenna or Rhizophora) is more difficult. Results classified according to forest cover density data with overall accuracy of 81.91.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

scholarly journals Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

2021 ◽  
Vol 13 (11) ◽  
pp. 2131
Author(s):  
Jamon Van Den Hoek ◽  
Alexander C. Smith ◽  
Kaspar Hurni ◽  
Sumeet Saksena ◽  
Jefferson Fox
Keyword(s):  
Remote Sensing ◽  
Geographic Distribution ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Forest Growth ◽  
Forest Cover Change ◽  
Scale Evaluation ◽  
Beneficial Effects ◽  
Affect Detection

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

scholarly journals Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

2021 ◽  
Vol 205 ◽  
pp. 76-92
Author(s):  
Clara Simón de Blas ◽  
Rubén Valcarce-Diñeiro ◽  
Ana E. Sipols ◽  
Nilda Sánchez Martín ◽  
Benjamín Arias-Pérez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Panel Data ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery

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.

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