scholarly journals Hierarchical classification approach for mapping rubber tree growth using per-pixel and object-oriented classifiers with SPOT-5 imagery

2017 ◽  
Vol 20 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Hayder Dibs ◽  
Mohammed Oludare Idrees ◽  
Goma Bedawi Ahmed Alsalhin
Keyword(s):  
Tree Growth ◽  
Rubber Tree ◽  
Hierarchical Classification ◽  
Object Oriented ◽  
Classification Approach ◽  
Spot 5

scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

scholarly journals Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

2020 ◽  
Vol 26 (4) ◽  
pp. 405-425
Author(s):  
Javed Miandad ◽  
Margaret M. Darrow ◽  
Michael D. Hendricks ◽  
Ronald P. Daanen
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Support Vector ◽  
Classification Approach ◽  
Resolution Image ◽  
Kappa Value ◽  
The Individual ◽  
Segmentation Image

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

scholarly journals Growth prediction for rubber tree and intercropped forest trees to facilitate environmental services valuation in South Thailand

2020 ◽  
Vol 21 (5) ◽  
Author(s):  
Narun Nattharom ◽  
SAOWALAK ROONGTAWANREONGSRI ◽  
SARA BUMRUNGSRI
Keyword(s):  
Regression Analysis ◽  
Tree Growth ◽  
Growth Parameters ◽  
Growth Models ◽  
Rubber Tree ◽  
Environmental Services ◽  
Forest Trees ◽  
Growth Prediction ◽  
Dipterocarpus Alatus ◽  
Set Of Points

Abstract. Nattharom N, Roongtawanreongsri S, Bumrungsri S. 2020. Growth prediction for rubber trees and intercropped forest trees to facilitate environmental services valuation in South Thailand. Biodiversitas 21: 2019-2034.  Tree growth parameters are necessary for valuing ecological services of trees in both natural forest and agroforest. These parameters are difficult to measure annually, and thus often lack the information needed in valuation. This study aimed to use regression analysis to create growth models for diameter at breast height (DBH), total height (TH), and merchantable height (MH) of Hevea brasiliensis Mull-Arg. (rubber tree) and five economic forest trees that are preferred by rubber farmers for intercropping, including Hopea odorata Roxb., Shorea roxburghii G.Don., Swietenia macrophylla King., Dipterocarpus alatus Roxb., and Azadirachta excelsa (Jack) Jacobs. Data were collected from 39 rubber plantations that contain rubber trees and the intercropped tree species at different ages in three provinces in South Thailand. The data were modelled using regression analysis with curve fitting to find the best-fitted curve to a given set of points by minimizing the sum of the squares of the residuals and standard error of the regression of the points from the curve. The results arrived at 21 models for the DBH, TH, and MH growth of rubber and the intercropped trees, in the forms of, power, sigmoid and exponential trends that vary according to the type of trees. The models can be used to predict tree growth parameters, which are useful for determining the value of ecosystem services such as carbon dioxide sequestration, oxygen production, and timber production.

A Comparison of Two Object-Oriented Methods for Land-Use/Cover Change Detection with SPOT 5 Imagery

2012 ◽  
Vol 10 (1) ◽  
pp. 415-424 ◽  
Author(s):  
Jie Liang ◽  
Jianyu Yang ◽  
Chao Zhang ◽  
Xuejiao Du ◽  
Anzhi Yue ◽  
...  
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Object Oriented ◽  
Object Oriented Methods ◽  
Spot 5

scholarly journals Deciphering Rubber Tree Growth Using Network-Based Multi Omics Approaches

2021 ◽  
Author(s):  
Felipe Roberto Francisco ◽  
Alexandre Hild Aono ◽  
Carla Cristina da Silva ◽  
Paulo de Souza Gon&ccedilalves ◽  
Erivaldo Jos&eacute Scaloppi J&uacutenior ◽  
...  
Keyword(s):  
Plant Growth ◽  
Tree Growth ◽  
Association Studies ◽  
Rubber Tree ◽  
Genotyping By Sequencing ◽  
Tree Breeding ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Large Tree ◽  
Improvement Programs

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

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