Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz, Moncef; Eisold, Stefanie; Guermazi, Emna

doi:10.1007/s41207-017-0036-7

Cited by 33 publications

(12 citation statements)

References 27 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the overall accuracy and Kappa hat coefficient of the current study were consistent with other researchers who have classified LULC data based on Landsat imagery using the SVM algorithm [68][69][70][71]; however, to apply SVM for LULC classification, users must select sample points between the boundaries of LULC classes precisely, where mixed pixels are common, in order to ensure accurate classification [72]. Therefore, selecting the SVM algorithm's training points for LULC classification under the EnMap BOX software requires time and skill.…”

Section: Lulc Classification Using Svm Algorithmsupporting

confidence: 90%

Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand

Kulsoontornrat

Ongsomwang

2021

Applied Sciences

View full text Add to dashboard Cite

Human activity and land-use changes have affected the water quality of Kwan Phayao, Upper Ing watershed, due to the associated high sediment load and eutrophication. This study aims to identify suitable LULC allocation scenarios for minimizing sediment and nutrient export into the lake. For this purpose, the LULC status and change were first assessed, based on classified LULC data in 2009 and 2019 from Landsat images, using the SVM algorithm. Later, the land requirements of three scenarios between 2020 and 2029 were estimated, based on their characteristics, and applied to predict LULC change using the CLUE-S model. Then, actual LULC data in 2019 and predicted LULC data under three scenarios between 2020 and 2029 were used to estimate sediment and nutrient export using the SDR and NDR models. Finally, the ecosystem service change index identified a suitable LULC allocation for minimizing sediment or/and nutrient export. According to the results, LULC status and change indicated perennial trees and orchards, para rubber, and rangeland increased, while forest land and paddy fields decreased. The land requirements of the three scenarios provided reasonable results, as expected, particularly Scenario II, which adopts linear programming to calculate the land requirements for maximizing ecosystem service values. For sediment and nutrient export estimation under the predicted LULC for the three scenarios, Scenario II led to the lowest yield of sediment and nutrient exports, and provided the lowest average ESCI value among the three scenarios. Thus, the LULC allocation under Scenario II was chosen as suitable for minimizing sediment or/and nutrient export into Kwan Phayao. These results can serve as crucial information to minimize sediment and nutrient loads for land-use planners, land managers, and decision makers.

show abstract

Section: Lulc Classification Using Svm Algorithmsupporting

confidence: 90%

Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand

Kulsoontornrat

Ongsomwang

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It separates data points into various classes using a hyper-spectral plane. In this process, the vectors ensure that the width of the margin will be maximized [76]. SVM can support multiple continuous and categorical variables as well as linear and non-linear samples in different class membership.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

View full text Add to dashboard Cite

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

show abstract

“…The kappa coefficient value is refereed to explain the classification results as it represents the agreement between classified data and actual ground data. Readers interested in detailed descriptions of the land use classification methods are referred to [28][29].…”

Section: Land Use Classificationmentioning

confidence: 99%

Quantitative Contribution of Climate Change and Land Use Change to Runoff in Tarbela Catchment, Pakistan

Shaukat¹,

Khan²,

Shahid³

et al. 2020

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Estimating the quantitate contribution of climate and land use change is necessary for planning water resources. Tarbela catchment in Pakistan was selected for this study. The Mann Kendall and Pettit test has been used for trend analysis of hydro climatic variables. Original climate elasticity method and improved empirical model of precipitation have been used. The results of trend analysis showed that precipitation and runoff trends decreased and potential evaporation trends increased. The annual rainfall and runoff presented a change point around the years 1999 and 1994 respectively. According to change point analysis, the runoff series was divided into two parts. The period before change point has been regarded as the pre-change period and the period after change point has been regarded as the post change period. According to the original climate elasticity method, the relative contribution of climate change and land use change has been computed as 39.3% and 60.7% respectively. Similarly the improved empirical model of precipitation showed relative contributions of climate change as 41.7% and the same for land use change as 58.3%. To validate the land use change contribution we prepared land use maps. It can be concluded that land use and climate change are responsible for runoff change in Tarbela catchment, and both methods performed well and results are in agreement.

show abstract

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Cited by 33 publications

References 27 publications

Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand

Suitable Land-Use and Land-Cover Allocation Scenarios to Minimize Sediment and Nutrient Loads into Kwan Phayao, Upper Ing Watershed, Thailand

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Quantitative Contribution of Climate Change and Land Use Change to Runoff in Tarbela Catchment, Pakistan

Contact Info

Product

Resources

About