Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

Coral Reefs ◽  
2013 ◽  
Vol 32 (3) ◽  
pp. 825-834 ◽  
Author(s):  
T. Caras ◽  
A. Karnieli
2015 ◽  
Vol 7 (6) ◽  
pp. 7521-7544 ◽  
Author(s):  
Tamir Caras ◽  
Arnon Karnieli

Author(s):  
Arief Reza Fahlevi ◽  
Takahiro Osawa ◽  
I Wayan Arthana

This study aims to investigate the feasibility of Landsat 7 ETM+ to identify coral reefs and shallow water benthic at Nusa Penida district in 2009 and 2012, comparison with the Reef Health Monitoring (RHM) results conducted by the Coral Triangle Center (CTC)  using the Point Intercept Transect (PIT) method in the same periods. This study also aims to determine the changes of coral reefs and shallow water benthic cover during this period and the distribution at Nusa Penida districk. Shallow water benthic classification refers to English et al. (1997), with a modified by the addition of vegetation classes (seagrass and seaweed). The classification result using confusion matrix between the Reef Health Monitoring (RHM) with the classification of satellite image in 2009 obtained an accuracy rate of 65.85% with a kappa coefficient value of 0.525, while in 2012 the accuracy value obtained of 74.81% with kappa coefficient value of 0.650, which shows the results of that classification of satellite images of Landsat 7 ETM+ with the results of in-situ research is in a moderate level.


CERNE ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 61-68
Author(s):  
Diamantis Bountis ◽  
Elias Milios

ABSTRACT The present study was conducted in Pinus brutia Ten. stands which were created after the forest fire of 1989, in the island of Thassos. In 2008, 45 plots of 5 x 5 m were randomly established in three site types. In each plot, the trees were counted, while the breast height diameter of trees was measured. All trees were classified as dominant, codominant, intermediate or suppressed. In each plot, one tree from each crown class was selected (a total of 160 trees) and were cut down. From each tree a cross-sectional disc was cut from the ground level and the number of annual growth rings was counted. A few years of age difference between trees in post fire establishment determined the crown class of a tree. The age difference and the number of trees were reduced from the less productive site type to the more productive site type. Codominant trees were (or will become) the crown class with the most numerous trees in the main canopy. Dominant trees were one of the most, if not the most, significant elements of stand structure and production regarding basal area. This was not the case in low productivity site type as a result of delayed dimension differentiation. Analysis of young P. brutia stands, through the classification of trees into crown classes, increased the amount of attained information, since it provided an improved insight in the competition regime.


2004 ◽  
Vol 22 (1) ◽  
pp. 1 ◽  
Author(s):  
Bruce E Davis ◽  
Norman Quinn

In this study a GIS approach was developed to provide ground-level classification of mangrove communities and their impact by human. Mangroves around Suva are declining due to peripheral pressures from expanding land use and interior pressures of increased resource utilization. Increasing urbanisation, particularly growth of industrialisation and squatter settlements, has resulted in greater utilisation of mangrove communities (Rhizophora - Bruguiera). Better information is needed if sustainable environmental management practices are to succeed. Remote sensing is unable to provide the detail and scale of data that is required, but in situ field work, combined with GIS approaches, offers an enhanced methodology. This project examines the mangroves of the Suva peninsula using a geographical information system (GIS) approach in order to derive better techniques for monitoring, analysing and managing these deteriorating environments.


Author(s):  
M. Gopi ◽  
J. Joyson Joe Jeevamani ◽  
S. Goutham ◽  
Nina Tabitha Simon ◽  
V. Deepak Samuel ◽  
...  

AK FfGgmI1,294 0,718 0,241 0,662 0,419 II 0,801 0,754 0,264 0,774 0,369 III-1 0,640 0,784 0,215 0,885 0,282 III-2 0,659 0,807 0,165 0,996 0,223 IV 0,876 0,823 0,127 1,108 0,205 V 1,503 0,833 0,151 1,219 0,089 Ak F F0 f G Go 9m50,25 194 0,72 0,69 48 0,59 0,40 4 1,19 354 0,67 0,60 42 0,74 0,28 3 0,82 198 0,74 0,09 12 1,11 0,22 2 0,94 168 0,76 0,08 12 1,28 0,11 Table 2. Coefficients of diffusivity dependent on stability classes after Klug and Turner; m stands for the exponent in the power law of wind velocity. However, we must keep in mind the limitations of this approach, especially the transfer of consistent sets of dispersion parameters to the propagation of air pollution in the vicinity of a source. The Gaussian plume formula should be used only for those downwind distances for which the empirical diffusion coefficients have been determined by standard diffusion experiments. Because we are interested in emissions near ground level and immissions nearby the source, we use those diffusion parameters which are based on the classification of Klug /12/ and Turner /13/. The parameters are expressible as power functions, Oy(x) = F xf and az(x) = G x9 after Klug (3.6a,b), tfy(x) = (F + Fx)f and az(x) = (GQ + Gx)9 after Turner (3.7a,b). The parameter classification after Klug is determined by six stability classes (with the German abbreviation AK for Ausbreitungsklasse), reaching from extreme stable (AK I) to extreme labile TAK V). In tRe Turner stability scheme AK 5 denotes extreme stable, AK 2 extreme labile, see table 2. An estimate of the stability can be made from synoptical observa­ tions of solar radiation, cloud cover and wind velocity /14/. With the parameters after Klug equation (3.4) becomes C(x,y,z) = ax"(f+9^exp(-bx"2f) [exp(-d0x"2g)+exp(-d1x"2g)] (3.8), wherein - - C0V k ya w (z-H)2 ^ (z+H) a ' TrTOFE • b ■ ■JT • do = -Z IP '- • d1 = ~75*~


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