Crop area monitoring within an advanced agricultural information system

Ehrlich, Daniele; Estes, J. E.; Scepan, Joseph; McGwire, Kenneth C.

doi:10.1080/10106049409354468

Cited by 15 publications

(7 citation statements)

References 6 publications

(5 reference statements)

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“…To classify the rice cohorts within the 'rice' category obtained above, we followed the sequential masking classification approach of Ehrlich et al (1994). In order to maximize the discriminability among rice growth stages, we generated a 'non-rice' mask from the land use map.…”

Section: Image Analysismentioning

confidence: 99%

“…Multi-temporal datasets give best classification accuracy by overcoming the ambiguity that exists in defining the spectral signature in single-date data (Hill and Megier 1988). Multiseasonal imagery (within a given year) has proved highly effective to map different crops (Ehrlich et al 1994, Brisco and Brown 1995, Brewster et al 1999, Oetter et al 2001, including rice (Tennakoon and Murty 1992, Okamoto et al 1998, Turner and Congalton 1998, Xiao et al 2002. In the case of rice, agronomic management practices such as localized flood control, ploughing and harvesting of rice fields may produce distinctive signatures during certain periods of the cropping season, that may help in the discrimination of rice from other crops.…”

Section: Introductionmentioning

confidence: 99%

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Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

Bagayoko

Sogoba

et al. 2004

International Journal of Remote Sensing

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The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season and at harvesting times could be used to map different land uses and rice planted at different times (cohorts), and to infer which rice growth stages were present earlier in the season. We performed a maximum likelihood supervised classification and evaluated the robustness of the classifications with the transformed divergence separability index, the kappa coefficient and confusion matrices. Rice was distinguished from other land uses with 98% accuracy and rice cohorts were discriminated with 84% accuracy (three classes) or 94% (two classes). Our study showed that optical remote sensing can reliably identify potential malaria mosquito breeding habitats from space. In the future, these 'crop landscape maps' could be used to investigate the relationship between cultivation practices and malaria transmission.

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Section: Image Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

Bagayoko

Sogoba

et al. 2004

International Journal of Remote Sensing

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“…Now a day's satellites acquire improved spatial and spectral resolution images which result in the possibility of accurate forest identification (Aziz Ahmed et al, 2014). Crop recognition based on consequent multi-date imagery within an emergent season has benefits over single date imagery (S. Panigrahy et al, 1997;D. Ehrlich et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Supervised Remote Sensing Methods for Forest Identification in Pakistan

Ahmad¹

2019

IJRASET

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In order to overcome the deforestation rate and to track the growth rate of forests in Pakistan, automated monitoring methods need to be developed and adopted. An effective way of monitoring and identifying existing forest conditions is through remote sensing. We can monitor and observe the land cover through satellite. It is a challenging task because during the spring season competing for green fields also appears along with forests which make it difficult to differentiate from these green fields like shrubs and bushes. In this paper, supervised classifiers are presented to classify the underlying land cover into different categories including forest. Specifically, a patch image of the Northern Pakistan region is obtained through SPOT-5 (2.5 meters) satellite imagery. Mahalanobis Distance Classifier and Maximum Likelihood classifier is executed on this and end results are compared in this paper. Maximum Likelihood achieved better classification results than Mahalanobis Distance classifier. Overall Accuracy of Maximum likelihood is 97.65% as compared to Mahalanobis distance which has 85.97% overall accuracy. Similarly, Maximum likelihood achieved forest's producer accuracy of 97% with reference to Mahalanobis distance in which we achieved forests Producer accuracy of 83%.

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“…Advancements in digital image processing and geographic information systems (GIS) have increased the potential for deriving more accurate crop information from satellite imagery (Ehrlich et al, 1994;Rodrıguez et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data

Alabi

Haertel

Chiejile

2016

Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management

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Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies.

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Crop area monitoring within an advanced agricultural information system

Cited by 15 publications

References 6 publications

Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

Performance Analysis of Supervised Remote Sensing Methods for Forest Identification in Pakistan

Investigating the Use of High Resolution Multi-spectral Satellite Imagery for Crop Mapping in Nigeria - Crop and Landuse Classification using WorldView-3 High Resolution Multispectral Imagery and LANDSAT8 Data

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