Integrating Remote Sensing and Wetland Ecology: a Case Study on South African Wetlands

Roeck, Els De; Miya, M.; Verhoesf, N.; Batelaan, Okke; Brendonck, Luc

doi:10.1109/multitemp.2007.4293033

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Most of them have used aerial images or satellite images for evaluation. One of the main studies in this case is titled "trend of ecosystems changes in general and ponds changes in particular" [11]. In this research, the author believes that understanding of the trend of changes in natural ecosystems and especially ponds can be useful in prediction of future status of them.…”

Section: Introductionmentioning

confidence: 98%

“…In this research, the author believes that understanding of the trend of changes in natural ecosystems and especially ponds can be useful in prediction of future status of them. Another research tried to investigate the trend of changes in South African ponds and then identify factors which affect these changes and interactions between the factors using satellite images and geoGraphicalInformation Systems (GIS) [11]. Prediction of natural ecosystems changes in Iran started when Urmia Lake went under crisis and many studies dealt with the reduction of the volume and area of UrmiaLake using visual analysis of satellite images and meteorological data [9-12-13].…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Miandehi

Zidehsaraei²,

Doostdar³

2014

IJIIS

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Abstract:Iranian ponds and water ecosystems are valuable assets which play decisive roles in economic, social, security and political affairs. Within the past few years, many Iranian water ecosystems such asUrmia Lake, Karoun River and Anzali Pond have been under disappearance threat. Ponds are habitats which cannot be replaced and this makes it necessary to investigate their changes in order to save these valuable ecosystems. The present research aims to investigate and evaluate the trend of variations in Anzali Pond using meteorological data between 1991-2010 by means of GMDH, which is based upon genetic algorithm and is a powerful technique in modeling complex dynamic non-linear systems, and linear regression technique. Input variables of both methodsinclude all factors (inside system and outside system factors) which affect variations in Anzali Pond. Exactness of linear regression method was 78% and exactness of GMDH neural network method was more than 97%. As as result, exactness of GMDH neural network method is significantly better than regression model.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Miandehi

Zidehsaraei²,

Doostdar³

2014

IJIIS

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“…Several studies have been conducted about the Urmia lake in Iran about determination of influential factors on the reduction of volume and area of the Urmia Lake using visual analysis of satellite imageries and meteorological data [2]. In similar research studies, the environmental risk is determined through characterization of texture of the lake using image processing techniques [3]. In 2007, a study was done on the pond that utilized satellite imageries for estimating the masses collected on the pond surface [4].…”

Section: Introductionmentioning

confidence: 99%

Area and Depth Investigation of Anzali Pond Using Satellite Imageries and Group Method of Data Handling Neural Network

Miandehi¹,

Shahbahrami

2014

IJIIS

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Analysis of changes in natural resources is one of the fundamental issues in remote sensing. Several research studies regarding the process of changes in natural resources using satellite imageries and image processing techniques have been done. Anzali pond is one of the important ecosystems in Iran that under the impact of some factors such as drought has the gradual drying trend over the last years. This study measures the area of basin surface and predicts the process of changes in the climate of the pond neighborhood during the next years, using GMDH neural network. Satellite imagery and meteorological data is used for this analysis. The final results represent reduction in area from 82 in 1998 to 51 in 2010. The average depth of the pond decreased to less than 4m in 2010 from 9m in 1998. The main reason for this reduction is diversion of rivers, sediment entering and changes in land use around the pond. If this trend continues, the amount of pollutants and toxins will reach to warning and this is a serious threat for animals and pond dwellers.

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Estimation of Wetland Area Change Using Sentinel-2 Satellite Images

Annavarapu,

Popuru,

Afreen

et al. 2023

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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Integrating Remote Sensing and Wetland Ecology: a Case Study on South African Wetlands

Cited by 3 publications

References 21 publications

Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Area and Depth Investigation of Anzali Pond Using Satellite Imageries and Group Method of Data Handling Neural Network

Estimation of Wetland Area Change Using Sentinel-2 Satellite Images

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