A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

Zheng, Boyuan; Myint, Soe W.; Thenkabail, Prasad S.; Aggarwal, Rimjhim

doi:10.1016/j.jag.2014.07.002

Cited by 267 publications

(177 citation statements)

References 33 publications

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“…The technical errors were mainly caused by the characteristics of the wetlands, such as changes in different season, they have complex spectra, they are heterogeneous, and the same land cover types have multiple spectrums. To improve the classification accuracy in the future, more research can be done on the following aspects: (1) To discover more effective features, not just in spectrum, new technologic methods maybe good alternative choices (e.g., synthetic aperture radar, lidar, and geospatial modeling); (2) to enhance the intensity of machine learning; taking into account the all possible situations via the new learning structures (e.g., deep convolutional artificial neural network (ANN) and deep learning) [104][105][106][107][108][109][110][111][112]. The deep convolutional neural network algorithm, in particular, has better learning and generalization performance for multiple variables and large datasets.…”

Section: Technical Errorsmentioning

confidence: 99%

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Zhu

Zhang

Huang

2018

Water

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Yellow River Delta wetlands are essential for the migration of endangered birds and breeding. The wetlands, however, have been severely damaged during recent decades, partly due to the lack of wetland ecosystem protection by authorities. To have a better historical understanding of the spatio-temporal dynamics of the wetlands, this study aims to map and characterize patterns of the loss and degradation of wetlands in the Yellow River Delta using a time series of remotely sensed images (at nine points in time) based on object-based image analysis and knowledge transfer learning technology. Spatio-temporal analysis was conducted to document the long-term changes taking place in different wetlands over the four decades. The results showed that the Yellow River Delta wetlands have experienced significant changes between 1973 and 2013. The total area of wetlands has been reduced by 683.12 km 2 during the overall period and the trend of loss continues. However, the rates and trends of change for the different types of wetlands were not the same. The natural wetlands showed a statistically significant decrease in area during the overall period (36.04 km 2 ·year −1 ). Meanwhile, the artificial wetlands had the opposite trend and showed a statistically significant increase in area during the past four decades (18.96 km 2 ·year −1 ). According to the change characteristics revealed by the time series wetland classification maps, the evolution process of the Yellow River Delta wetlands could be divided into three stages: (1) From 1973From -1984, basically stable, but with little increase; (2) from 1984-1995, rapid loss; and (3) from 1995-2013, slow loss. The area of the wetlands reached a low point around 1995, and then with a little improvement, the regional wetlands entered a slow loss stage. It is believed that interference by human activities (e.g., urban construction, cropland creation, and oil exploitation) was the main reason for wetland degradation in the Yellow River Delta over the past four decades. Climate change also has long-term impacts on regional wetlands. In addition, due to the special geographical environment, the hydrological and sediment conditions and the location of the Yellow River mouth also have a significant influence on the evolution process of the wetlands.

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Section: Technical Errorsmentioning

confidence: 99%

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Zhu

Zhang

Huang

2018

Water

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“…SVM was developed in the late 1970s, but its popularity in Remote Sensing only began to increase about a decade ago (Zheng et al, 2015) Previous studies showed that SVM has the ability to generalize to unseen data with a small training dataset (Zheng et al, 2015) compared SVM to two other non-parametric classifiers. SVM has been familiar as an effective classifier in land-cover mapping (Clinton et al, 2015) We used the ENVI (the environment for visualizing images software), SVM classifier in this study and the parameter settings were as follows: the radial basis function was used as the kernel type, the gamma in the kernel function was set as 0.143, and the penalty parameter was set as 100.…”

Section: Classificationmentioning

confidence: 99%

“…We tested the ability of Support Vector Machines (SVMs) to discriminate classes using a limited number of training samples and it was applied to time-series (Zheng et al, 2015). Overall Accuracy % Satellites were used to evaluate and modify urbanization and transgressions during this period.…”

Section: Classification and Evaluationmentioning

confidence: 99%

Monitoring Urban Land Cover/Land Use Change in Algiers City Using Landsat Images (1987–2016)

Bouchachi

Zhong

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Monitoring the Urban Land Cover / Land Use change detection is important as one of the main driving forces of environmental change because Urbanization is the biggest changes in form of Land, resulting in a decrease in cultivated areas. Using remote sensing ability to solve land resources problems. The purpose of this research is to map the urban areas at different times to monitor and predict possible urban changes, were studied the annual growth urban land during the last 29 years in Algiers City. Improving the productiveness of long-term training in land mapping, were have developed an approach by the following steps: 1) pre-processing for improvement of image characteristics; 2) extract training sample candidates based on the developed methods; and 3) Derive maps and analyzed of Algiers City on an annual basis from 1987 to 2016 using a Supervised Classifier Support Vector Machine (SVMs). Our result shows that the strategy of urban land followed in the region of Algiers City, developed areas mostly were extended to East, West, and South of Central Regions. The urban growth rate is linked with National Office of Statistics data. Future studies are required to understand the impact of urban rapid lands on social, economy and environmental sustainability, it will also close the gap in data of urbanism available, especially on the lack of reliable data, environmental and urban planning for each municipality in Algiers, develop experimental models to predict future land changes with statistically significant confidence.

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“…Regarding the developed vineyard detection methodology, in contrast to similar research efforts towards vineyard and/or other crop identification tasks [8,9,15,[22][23][24]27,30], the proposed approach employs very high resolution multispectral data, along with a specific set of features, rules, segmentation scales and a set of parameters that deliver relatively higher detection rates. Instead of employing only spectral features (e.g., [8,22,23,27]), we employ, as in [9,24], textural features, as well.…”

Section: Contributionmentioning

confidence: 99%

“…In particular, apart from the within-field crop analysis and the estimation of the spatial variability in wine-grape composition and yield [17][18][19][20][21][22][23], there is plenty of research towards the classification, identification and delineation of crops in remote sensing data [24][25][26][27]. However, despite recent research efforts towards the detection and delineation on medium [28][29][30] and high resolution [31,32] spatial scales, the development and validation of efficient classification frameworks for operational vineyard detection in high resolution data and over large agricultural regions still remain a challenge.…”

Section: Introductionmentioning

confidence: 99%

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

2016

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Abstract:In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

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A support vector machine to identify irrigated crop types using time-series Landsat NDVI data

Cited by 267 publications

References 33 publications

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Four Decades of Estuarine Wetland Changes in the Yellow River Delta Based on Landsat Observations Between 1973 and 2013

Monitoring Urban Land Cover/Land Use Change in Algiers City Using Landsat Images (1987–2016)

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

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