scholarly journals Object-based classification of semi-arid vegetation to support mine rehabilitation and monitoring

2014 ◽  
Vol 8 (1) ◽  
pp. 083564 ◽  
Author(s):  
Nisha Bao ◽  
Alex M. Lechner ◽  
Kasper Johansen ◽  
Baoying Ye
Keyword(s):  
Mine Rehabilitation ◽  
Object Based ◽  
Semi Arid ◽  
Arid Vegetation

Object-based classification of semi-arid wetlands

2011 ◽  
Vol 5 (1) ◽  
pp. 053511 ◽  
Author(s):  
Meghan Halabisky
Keyword(s):  
Object Based ◽  
Semi Arid

THE CLASSIFICATION OF LAND ACCORDING TO THE DEGREE OF RABBIT INFESTATION IN CENTRAL OTAGO

1987 ◽  
pp. 65-70
Author(s):  
I.G.C. Kerr ◽  
J.M. Williams ◽  
W.D. Ross ◽  
J.M. Pollard
Keyword(s):  
Carrying Capacity ◽  
Mediterranean Climate ◽  
Natural Habitat ◽  
Public Funding ◽  
Soil Survey ◽  
European Rabbit ◽  
Cultural Modification ◽  
Classification Information ◽  
Semi Arid

The European rabbit (Oryctolagus cuniculus) introduced into New Zealand in the 183Os, has consistently flourished in Central Otago, the upper Waitaki, and inland Marlborough, all areas of mediterranean climate. It has proved difficult to manage in these habitats. The 'rabbit problem' is largely confined to 105,000 ha of low producing land mostly in semi arid areas of Central Otago. No field scale modifications of the natural habitat have been successful in limiting rabbit numbers. The costs of control exceed the revenue from the land and continued public funding for control operations appears necessary. A system for classifying land according to the degree of rabbit proneness is described. Soil survey and land classification information for Central Otago is related to the distribution and density of rabbits. This intormation can be used as a basis for defining rabbit carrying capacity and consequent land use constraints and management needs. It is concluded that the natural rabbit carrying capacity of land can be defined by reference to soil survey information and cultural modification to the natural vegetation. Classification of land according to rabbit proneness is proposed as a means of identifying the need for, and allocation of, public funding tor rabbit management. Keywords: Rabbit habitat, rabbit proneness, use of rabbit prone land.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Identification of the degree of impact of dams on the hydrologic regime of semi-arid rivers: an evaluation of the DHRAM method

RBRH ◽  
2017 ◽  
Vol 22 (0) ◽  
Author(s):  
Naiah Caroline Rodrigues de Souza ◽  
◽  
Andrea Sousa Fontes ◽  
Lafayette Dantas da Luz ◽  
Sandra Maria Conceição Pinheiro ◽  
...  
Keyword(s):  
Hydrological Regime ◽  
Flow Regulation ◽  
Hydrologic Regime ◽  
Local Conditions ◽  
Operating Rules ◽  
Hydrologic Variability ◽  
High Degree ◽  
The Impact ◽  
Semi Arid

ABSTRACT The flow regulation that results from the implantation of dams causes consequences to the river ecosystems due to the modification on the characteristics of the hydrologic regime. The investigation of these changes become relevant, mainly in semi-arid regions where there is a great amount of these hydraulic structures and lack of such analyzes. Considering the above, this paper aims to evaluate the Dundee Hydrological Regime Alteration Method (DHRAM) through the classification of the degree of impact of dams located on rivers Itapicuru, Paraguaçu and their tributaries, verifying the adequacy of its use to represent the semi-arid hydrologic regime. Thereby, the DHRAM was applied in three versions: considering the thresholds that define the scores to classify the degree of impact in its original set (accordingly to Black et al. (2005)); with the adjustment of those thresholds to local conditions; and, with the regrouping of variables and adjustment of thresholds. The results showed that the method in its original set is applicable to semi-arid rivers, however it tends to be very restrictive against the high natural hydrologic variability characteristic of these rivers, and it ends up pointing to a high degree of alteration for dams that are known for not causing a very siginifcant flow regulation. The DHRAM with the regrouping of variables and the adjustment of thresholds presented the classification that approached the most to the known characteristics of the studied dams, being useful for the evaluation of the impact of dams still in project, and also to guide the adoption of operating rules that minimize the most significant hydrologic alterations that are identified.

Analysing Temporal Effects on Classification of SAR and Optical Images

2021 ◽  
Author(s):  
Ahmet Batuhan Polat ◽  
Ozgun Akcay ◽  
Fusun Balik Sanli
Keyword(s):  
Rural Areas ◽  
Classification Accuracy ◽  
Winter Season ◽  
Support Vector ◽  
Classification Algorithms ◽  
Sar Images ◽  
Optical Images ◽  
Object Based ◽  
Training Samples

<p>Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.</p>

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