scholarly journals FOREST COVER MAPPING USING LANDSAT-BASED SEASONAL COMPOSITED MOSAICS

2018 ◽  
Vol 28 (1) ◽  
pp. 28-33 ◽  
Author(s):  
V. V. Myroniuk
Keyword(s):  
Forest Cover ◽  
Forest Cover Mapping

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 A Simple Approach for Mapping Forest Cover from Time Series of Satellite Data

2020 ◽  
Vol 12 (18) ◽  
pp. 2918
Author(s):  
Yang Liu ◽  
Ronggao Liu
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Forest Cover ◽  
Google Earth ◽  
Satellite Observations ◽  
Training Data ◽  
Herbaceous Vegetation ◽  
Forest Mapping ◽  
Maturity Period ◽  
Forest Cover Mapping

Forest cover mapping based on multi-temporal satellite observations usually uses dozens of features as inputs, which requires huge training data and leads to many ill effects. In this paper, a simple but efficient approach was proposed to map forest cover from time series of satellite observations without using classifiers and training data. This method focuses on the key step of forest mapping, i.e., separation of forests from herbaceous vegetation, considering that the non-vegetated area can be easily identified by the annual maximum vegetation index. We found that the greenness of forests is generally stable during the maturity period, but a similar greenness plateau does not exist for herbaceous vegetation. It means that the mean greenness during the vegetation maturity period of forests should be larger than that of herbaceous vegetation, while its standard deviation should be smaller. A combination of these two features could identify forests with several thresholds. The proposed approach was demonstrated for mapping the extents of different forest types with MODIS observations. The results show that the overall accuracy ranges 91.92–95.34% and the Kappa coefficient is 0.84–0.91 when compared with the reference datasets generated from fine-resolution imagery of Google Earth. The proposed approach can greatly simplify the procedures of forest cover mapping.

scholarly journals Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1062 ◽  
Author(s):  
Kay Khaing Lwin ◽  
Tetsuji Ota ◽  
Katsuto Shimizu ◽  
Nobuya Mizoue
Keyword(s):  
Large Scale ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Tree Cover ◽  
Tropical Rainforests ◽  
Forest Change ◽  
Tropical Regions ◽  
Ecological Zones ◽  
Optimal Tree ◽  
Forest Cover Mapping

Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10% to 90% were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50%. The appropriate threshold for tropical rainforests was 80%. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.

Integration of digital satellite data and forest inventory data for forest cover mapping in Korea

2010 ◽  
Vol 6 (2) ◽  
pp. 87-96 ◽  
Author(s):  
Jong‐Su Yim ◽  
Christoph Kleinn ◽  
Hyun‐Kook Cho ◽  
Man‐Yong Shin
Keyword(s):  
Satellite Data ◽  
Forest Inventory ◽  
Forest Cover ◽  
Inventory Data ◽  
Forest Inventory Data ◽  
Forest Cover Mapping

C- and L-band SAR interoperability: Filling the gaps in continuous forest cover mapping in Tasmania

2014 ◽  
Vol 155 ◽  
pp. 58-68 ◽  
Author(s):  
Anthea L. Mitchell ◽  
Ian Tapley ◽  
Anthony K. Milne ◽  
Mark L. Williams ◽  
Zheng-Shu Zhou ◽  
...  
Keyword(s):  
Forest Cover ◽  
Continuous Forest ◽  
L Band ◽  
Forest Cover Mapping

scholarly journals Fractional forest cover mapping in the Brazilian Amazon with a combination of MODIS and TM images

2011 ◽  
Vol 32 (22) ◽  
pp. 7131-7149 ◽  
Author(s):  
Dengsheng Lu ◽  
Mateus Batistella ◽  
Emilio Moran ◽  
Scott Hetrick ◽  
Diogenes Alves ◽  
...  
Keyword(s):  
Forest Cover ◽  
Brazilian Amazon ◽  
Forest Cover Mapping
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