Acreage and production estimation of mango orchards using Indian Remote Sensing (IRS) satellite data

Yadav, Inderjit Singh; Rao, N. K. Srinivasa; Reddy, Benjaram M.; Rawal, Ruby; Srinivasan, V.; Sujatha, N.T.; Bhattacharya, Chaithali; Rao, P. Parthasarathy; Ramesh, K. S.; Elango, Sriram

doi:10.1016/s0304-4238(01)00321-1

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…The main advantages of supervised classifiers are: (1) The classifier can be trained in a way which has a perfect decision boundary to distinguish different classes accurately, and (2) It can specifically determine how many classes you want to have. The disadvantages are: (1) Decision boundary may be over fitted; (2) Accurate a priori information is required, otherwise in case of inaccurate may produce erroneous classified image. Therefore, to obtain an accurate classified image a knowledge based decision tree may require, which be able to produce the accurate classified image.…”

Section: Image Classificationmentioning

confidence: 99%

“…Satellite images have various applications for mango crop such as crop classification and monitoring, change detection, crop acreage estimation, identification of crop stress, and yield prediction, etc. In brief literature review, it has been observed that high resolution IRS LISS series of images have been mostly used for mango orchard mapping [2][3][4]. Reference [3] used IRS 1C LISS-II data for estimation of mango orchard acreage and production in Krishna district of Andhra Pradesh.…”

Section: Introductionmentioning

confidence: 99%

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Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

Verma¹,

Ahmed²,

Rajan³

2020

IJITEE

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Mango is a very important fruit which is liked by majority of the population due to its nutritional value and excellent taste. India is the largest producer of mango in the world. Accurate information is required for policy decision making in terms of providing subsidy, area expansion, and crop insurance planning. Hence, this type of information may be retrieve through satellite images by using the image classification techniques, which are playing a crucial role in crop cover classification, yield prediction and crop monitoring etc. Classification of optical satellite images is still a challenging task due to effect of changing atmospheric conditions such as cloud, snow, haze, dust, fog, and rain etc. In this paper, knowledge based decision tree classification (DTC) has been proposed to classify the mango orchards of Lucknow district using multi-temporal Landsat 8 operational land imager (OLI) images from year 2015 to 2017 and further mango orchard area were also estimated. In order to develop the DTC, separability analysis for various land cover classes was carried out on different vegetation indices namely, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and soil adjusted vegetation index (SAVI). In order to analyze the performance of DTC, most commonly used satellite image classifiers such as unsupervised classifier (i.e. ISODATA) and supervised classifier (i.e. Maximum Likelihood) have been used and it is observed that the proposed DTC outperformed these traditional classifiers. Also, accuracy assessment has been carried out to measure the performance of proposed DTC and it is observed that all of the three images from 2015 to 2017 are classified with high overall accuracy, which is ranging from 70.66% to 86.69%. Kappa Coefficient (KC) for all the three images ranged from 0.65 to 0.83, which indicates that classified images are highly acceptable for area estimation.

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Section: Image Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

Verma¹,

Ahmed²,

Rajan³

2020

IJITEE

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“…Various types of remote sensing data are available to estimate crop acreage at regional scales using digital classification methods, such as optical data with high spatial resolution (CastillejoGonzález et al, 2009;Oza et al, 2008), medium spatial resolution (Badhwar, 1984;Dutta et al, 1994;Yadav et al, 2002;Chang et al, 2007), and radar data (Bouman and Uenk, 1992;Chakraborty and Panigrahy, 2000;McNairn et al, 2009). However, Pixels in remote sensing data do not always correspond to a single crop type or field.…”

Section: Introductionmentioning

confidence: 99%

Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes

2012

International Journal of Applied Earth Observation and Geoinfor

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“…Remote sensing has been used for mapping the extent of grain crops and to a limited extent, horticulture. However, there has been limited application of high spatial resolution image data because of the heterogeneity of the fields to be mapped in traditional per-pixel image classification (Navalgund et al, 1991;Shrivastava and Gebelein, 2007;Tennakoon et al, 1992;Yadav et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery

Johansen¹,

Phinn²,

Witte³

et al. 2009

photogramm eng remote sensing

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The objectives of this research were to develop and evaluate an approach for object-oriented mapping of banana plantations from SPOT-5 imagery, and to compare these results to banana plantations manually delineated from high spatial resolution airborne imagery. Cultivated areas were first identified through large spatial scale mapping using spectral and elevation data. Within the cultivated areas, separation of banana plantations and other land-cover classes increased when including image co-occurrence texture measures and context relationships in addition to spectral information. The results showed that a pixel size of Յ2.5 m was required to accurately identify the row structure within banana plantations, which enabled object-based separation from other crops based on texture information. The user's and producer's accuracies for mapping banana plantations increased from 73 percent and 77 percent, respectively, to 94 percent and 93 percent after post-classification visual editing. The results indicate that the data and processing techniques used offer a reliable approach for mapping banana plants and other plantation crops.

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Acreage and production estimation of mango orchards using Indian Remote Sensing (IRS) satellite data

Cited by 27 publications

References 10 publications

Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

Mapping and Area Estimation of Mango Orchards of Lucknow Region by Applying Knowledge Based Decision Tree to Landsat 8 OLI Satellite Images

Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes

Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery

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