Forest Canopy Density and ASTER DEM based Study for Dense Forest Investigation using Remote Sensing and GIS Techniques around East Singhbhum in Jharkhand, India

2015 ◽  
Vol 4 (1) ◽  
pp. 1026-1032 ◽  
Author(s):  
Jai Kumar ◽  
◽  
Paras Talwar ◽  
Krishna A.P. ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Remote Sensing And Gis ◽  
Canopy Density ◽  
Gis Techniques ◽  
Dense Forest ◽  
Forest Canopy Density

scholarly journals COMPARISON OF MODEL ACCURACY IN TREE CANOPY DENSITY ESTIMATION USING SINGLE BAND, VEGETATION INDICES AND FOREST CANOPY DENSITY (FCD) BASED ON LANDSAT-8 IMAGERY (CASE STUDY: PEAT SWAMP FOREST IN RIAU PROVINCE)

2018 ◽  
Vol 15 (1) ◽  
pp. 81 ◽  
Author(s):  
Faisal Ashaari ◽  
Muhammad Kamal ◽  
Dede Dirgahayu
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Single Band ◽  
Tree Canopy ◽  
Landsat 8 ◽  
Swamp Forest ◽  
Canopy Density ◽  
Forest Canopy Density

Identification of a tree canopy density information may use remote sensing data such as Landsat-8 imagery. Remote sensing technology such as digital image processing methods could be used to estimate the tree canopy density. The purpose of this research was to compare the results of accuracy of each method for estimating the tree canopy density and determine the best method for mapping the tree canopy density at the site of research. The methods used in the estimation of the tree canopy density are Single band (green, red, and near-infrared band), vegetation indices (NDVI, SAVI, and MSARVI), and Forest Canopy Density (FCD) model. The test results showed that the accuracy of each method: green 73.66%, red 75.63%, near-infrared 75.26%, NDVI 79.42%, SAVI 82.01%, MSARVI 82.65%, and FCD model 81.27%. Comparison of the accuracy results from the seventh methods indicated that MSARVI is the best method to estimate tree canopy density based on Landsat-8 at the site of research. Estimation tree canopy density with MSARVI method showed that the canopy density at the site of research predominantly 60-70% which spread evenly.

scholarly journals Application of Forest Canopy Density (FCD) Model for the Hotspot Monitoring of Crown Fire in Tebo, Jambi Province

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Muhammad Attorik Falensky ◽  
Anggieani Laras Sulti ◽  
Ranggas Dhuha Putra ◽  
Kuswantoro Marko
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Forest Canopy ◽  
Remote Sensing Data ◽  
Forest Area ◽  
Landsat 8 ◽  
Crown Fire ◽  
Canopy Density ◽  
Sensing Data ◽  
Forest Canopy Density

<p><em>Indonesia is one of the owners of the 9th largest forest area in the world. Forest area in Indonesia reaches 884,950 km<sup>2</sup>. Tebo Regency is a regency in Jambi Province which has a wide forest area of 628,003 Ha. However, this forest area has been reduced due to the conversion of functions of Industrial Plantation Forests (HTI), oil palm plantations, and forest clearing activities for both settlements and plantations which led to the phenomenon of forest and land fires (karhutla). This study aims to get a better knowledge of crowns of fire potential locations in forest areas using remote sensing technology. Remote sensing data used in this study is from the satellite imagery </em><em>of </em><em>Landsat 8 OLI - TIRS in 2019. Remote sensing data is used to produce a Forest Canopy Density (FCD) model that can be overlap</em><em>ped with</em><em> a hotspot location, so the crown fire potential locations will be explored in the forest area of Tebo Regency, Jambi Province. Identification of hotspot patterns in Forest Areas was analyzed using spatial analysis. The results of this study are useful for the government as the information of the hotspot area as the cause of fires in the Forest Region of Tebo Regency Jambi Province.</em></p><strong><em>Keywords</em></strong><em>: Spatial Analysis, Forest Cover Density (FCD), Hotspots, Forest Areas, Remote Sensing</em>

scholarly journals Forest canopy density assessment using different approaches – Review

2017 ◽  
Vol 63 (No. 3) ◽  
pp. 107-116 ◽  
Author(s):  
Abdollahnejad Azadeh ◽  
Panagiotidis Dimitrios ◽  
Surový Peter
Keyword(s):  
Remote Sensing ◽  
Cost Saving ◽  
Forest Canopy ◽  
Soil Conditions ◽  
Canopy Density ◽  
Different Types ◽  
Forest Canopy Density ◽  
Satellite Sensors ◽  
Remote Sensing Methods ◽  
Density Assessment

Crown canopy is a significant regulator of forest, affecting microclimate, soil conditions and having an undeniable role in a forest ecosystem. Among the different materials and approaches that have been used for the estimation of crown canopy, satellite based methods are among the most successful methods regarding cost-saving efforts and different kinds of options for measuring the crown canopy. Different types of satellite sensors can result in different outputs due to their various spectral and spatial resolution, even when using the same methodologies. The aim of this review is to assess different remote sensing methods for forest crown canopy density assessment.

scholarly journals Pemanfaatan Citra Landsat 8 Multitemporal dan Model Forest Canopy Density (FCD) untuk Analisis Perubahan Kerapatan Kanopi Hutan di Kawasan Fakultas Geografi Universitas Gadjah Mada Gunung Kelud, Jawa Timur

2017 ◽  
Vol 31 (1) ◽  
pp. 65
Author(s):  
Shafira Himayah ◽  
Hartono Hartono ◽  
Projo Danoedoro
Keyword(s):  
Remote Sensing ◽  
Forest Canopy ◽  
Landsat 8 ◽  
Canopy Density ◽  
Protection Forest ◽  
Image Recording ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Before And After ◽  
Forest Canopy Density

Penginderaan jauh memiliki keunggulan dalam hal resolusi temporal yang dapat dimanfaatkan untuk meneliti perubahan suatu obyek dalam waktu yang berbeda. Hutan Gunung Kelud mengalami perubahan setelah erupsi tahun 2014. Perubahan tersebut dapat dianalisis dengan memanfaatkan teknologi penginderaan jauh melalui citra multitemporal. Penelitian ini bertujuan untuk mengkaji kemampuan citra Landsat 8 multitemporal dan Forest Canopy Density (FCD) untuk perubahan kerapatan kanopi di Hutan Lindung Gunung Kelud sebelum dan sesudah erupsi tahun 2014.Citra penginderaan jauh yang digunakan adalah citra Landsat 8 perekaman 26 Juni 2013 dan 4 September 2015. Metode yang digunakan adalah pemodelan FCD yang menghasilkan kerapatan kanopi per piksel. Hasil pemodelan FCD kemudian digunakan untuk menganalisis perubahan kerapatan kanopi setelah erupsi. Berdasarkan penelitan ini didapatkan hasil bahwa citra Landsat 8 dapat dipergunakan untuk mengetahui kerapatan kanopi Hutan Lindung Gunung Kelud sebelum dan setelah erupsi dengan masing-masing akurasi sebesar 83,73% dan 81,14%. Terjadi perubahan luas kerapatan kanopi setelah erupsi, dimana terdapat 8833,95 Ha hutan yang mengalami penurunan kerapatan kanopi, sedangkan hutan dengan kerapatan kanopi yang tetap adalah seluas 2149,38 Ha, dan hutan yang mengalami peningkatan kerapatan kanopi adalah seluas 1643,31 Ha. Remote sensing has an advantage in terms of temporal resolution that can be exploited to examine the changes of an object in different times. Gunung Kelud Forest is changing after the eruption in 2014. The changes can be analyzed by utilizing remote sensing technology through multitemporal imagery. This study aims to examine the capabilities of Landsat 8 multitemporal and Forest Canopy Density (FCD) images for changes in canopy density in Kelud Protection Forest before and after the eruption in 2014. Remote sensing imagery used is Landsat 8 image recording June 26, 2013, and September 4, 2015, The method used is FCD modeling that produces a density of the canopy per pixel. FCD modeling results are then used to analyze changes in density of the canopy after the eruption. Based on this research, it can be concluded that Landsat 8 image can be used to determine the density of canopy of Kelud Protection Forest before and after eruption with 83.73% and 81.14% accuracy respectively. There was a change in the area of the canopy density after the eruption, where there was 8833.95 ha of forest that experienced a decrease in canopy density, whereas forests with fixed canopy densities were 2149.38 Ha, and forests with an increase in canopy density were 1643.31 Ha.

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