Identification of Canopy Species in Tropical Forests Using Hyperspectral Data

2011 ◽  
pp. 423-446
Author(s):  
Matthew Clark
Keyword(s):  
Tropical Forests ◽  
Hyperspectral Data ◽  
Canopy Species

scholarly journals Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2774
Author(s):  
Chris J. Chandler ◽  
Geertje M. F. van der Heijden ◽  
Doreen S. Boyd ◽  
Giles M. Foody
Keyword(s):  
Neural Network ◽  
Tropical Forests ◽  
Temporal Patterns ◽  
Climatic Conditions ◽  
Hyperspectral Data ◽  
Forest Types ◽  
Spatial And Temporal Patterns ◽  
Neural Network Classification ◽  
Closed Canopy ◽  
Over Time

Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally.

scholarly journals ON THE USE OF SHORTWAVE INFRARED FOR TREE SPECIES DISCRIMINATION IN TROPICAL SEMIDECIDUOUS FOREST

2015 ◽  
Vol XL-3/W3 ◽  
pp. 473-476 ◽  
Author(s):  
M. P. Ferreira ◽  
M. Zortea ◽  
D. C. Zanotta ◽  
J. B. Féret ◽  
Y. E. Shimabukuro ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Tree Species ◽  
Forest Restoration ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Semideciduous Forest ◽  
Linear Discriminant ◽  
Average Accuracy ◽  
Shortwave Infrared

Tree species mapping in tropical forests provides valuable insights for forest managers. Keystone species can be located for collection of seeds for forest restoration, reducing fieldwork costs. However, mapping of tree species in tropical forests using remote sensing data is a challenge due to high floristic and spectral diversity. Little is known about the use of different spectral regions as most of studies performed so far used visible/near-infrared (390-1000 nm) features. In this paper we show the contribution of shortwave infrared (SWIR, 1045-2395 nm) for tree species discrimination in a tropical semideciduous forest. Using high-resolution hyperspectral data we also simulated WorldView-3 (WV-3) multispectral bands for classification purposes. Three machine learning methods were tested to discriminate species at the pixel-level: Linear Discriminant Analysis (LDA), Support Vector Machines with Linear (L-SVM) and Radial Basis Function (RBF-SVM) kernels, and Random Forest (RF). Experiments were performed using all and selected features from the VNIR individually and combined with SWIR. Feature selection was applied to evaluate the effects of dimensionality reduction and identify potential wavelengths that may optimize species discrimination. Using VNIR hyperspectral bands, RBF-SVM achieved the highest average accuracy (77.4%). Inclusion of the SWIR increased accuracy to 85% with LDA. The same pattern was also observed when WV-3 simulated channels were used to classify the species. The VNIR bands provided and accuracy of 64.2% for LDA, which was increased to 79.8 % using the new SWIR bands that are operationally available in this platform. Results show that incorporating SWIR bands increased significantly average accuracy for both the hyperspectral data and WorldView-3 simulated bands.

Tropical Forests

1921 ◽  
Vol 3 (3supp) ◽  
pp. 267-270
Author(s):  
Vernon Kellogg ◽  
R. M. Yerkes ◽  
H. E. Howe
Keyword(s):  
Tropical Forests

Automatic tumor tissue classification of oncological esophageal resectates based on hyperspectral data

2019 ◽  
Author(s):  
M Maktabi ◽  
H Köhler ◽  
R Thieme ◽  
JP Takoh ◽  
SM Rabe ◽  
...  
Keyword(s):  
Tumor Tissue ◽  
Hyperspectral Data ◽  
Tissue Classification

Valuing Tropical Forests

1995 ◽  
Author(s):  
Randall A. Kramer ◽  
narendra Sharma ◽  
Mohan Munasinghe
Keyword(s):  
Tropical Forests

AN AUTOMATIC APPROACH TO LOSSY COMPRESSION OF AVIRIS HYPERSPECTRAL DATA

2010 ◽  
Vol 69 (6) ◽  
pp. 537-563 ◽  
Author(s):  
N. N. Ponomarenko ◽  
M. S. Zriakhov ◽  
A. Kaarna
Keyword(s):  
Lossy Compression ◽  
Hyperspectral Data

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

FRUGIVORIA E DISPERSÃO DE SEMENTES POR PEIXES

2008 ◽  
Vol 1 (1) ◽  
pp. 7-18
Author(s):  
Luciane Lopes de Souza
Keyword(s):  
Sustainable Development ◽  
Seed Dispersal ◽  
Tropical Forests ◽  
Natural Regeneration ◽  
Main Species ◽  
Fishing Gears ◽  
Solimões River

Biotic or abiotic processes of seed dispersal are important for the maintenance of the diversity, and for the natural regeneration in tropical forests. Ichthyochory is one of the fundamental mechanisms for seed dispersal in flooded environments, as the “igapó” forests. A study on the ichthyochory of the igapós was conducted at Amanã Sustainable Development Reserve, in the middle Solimões river, from June 2002 to September 2004. Monthly samples of frugivorous fish were taken, with the main fishing gears used locally. Guts of 1,688 fish caught were examined. The main species were Myloplus rubripinnis (29.21%), Hemiodus immaculatus (18.96%),Colossoma macropom um (16.23%) and Mylossoma duriventre (16.05%). The diet was made of vegetables (fruits, leave and flowers), and animals (arthropods). 53.02% of all fish caught ingested fruits. The total number of intact seeds in the stomachs and intestines were 8,069 and 5,763 respectively. About 61.9% of the Brycon melanopterus (matrinchão), 46.34% of the Brycon amazonicus (mamuri) and 30.22% of M . rubripinnis (parum ) analysed had intact seeds in their guts. Seeds of Nectandra amazonum and Genipa spruceana ingested proved to be more viable than those non-ingested by fish. The high rates of frugivory, the presence of intact seeds in the guts of fish and the greater viability of ingested seeds all suggest that these animals are important seed dispersors in the “igapó” forests of Amanã Reserve.

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