Land cover classification and economic assessment of citrus groves using remote sensing

Shrivastava, Rahul J.; Gebelein, Jennifer

doi:10.1016/j.isprsjprs.2006.10.003

Cited by 34 publications

(27 citation statements)

References 36 publications

(32 reference statements)

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“…Recently, pilot studies have successfully demonstrated the potential use of high resolution Cartosat-1 and LISS-IV data for discrimination and mapping of major plantation crops like rubber, tea and coffee. The conventional * Corresponding author per pixel classifiers have limited application for classification of horticultural crops to achieve reasonable accuracy due to large spatial heterogeneity (Hebbar andRao, 2002, Yadav et al, 2002;Srivastava and Gebelein, 2007). Hence, Visual interpretation techniques have been adopted for mapping of plantation crops (RRSC-South, 2012).…”

Section: Introductionmentioning

confidence: 99%

Object oriented classification of high resolution data for inventory of horticultural crops

Hebbar

Ravishankar

Trivedi

et al. 2014

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

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ABSTRACT:High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75-80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at local levels and future development is focused on up-scaling the methodology for generation of fruit and plantation crop maps at regional and national level which is important for creation of database for overall horticultural crop development.

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Section: Introductionmentioning

confidence: 99%

Object oriented classification of high resolution data for inventory of horticultural crops

Hebbar

Ravishankar

Trivedi

et al. 2014

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

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“…The imageries are the best possible resource to demarcate the spatial variation of crops, including horticultural crops. Several studies have been conducted over the years to delineate or classify forests or shrubs, e.g., horticultural plants, based on satellite and aerial images for precision agriculture decision making [8,[42][43][44][45][46][47][48][49][50].…”

Section: Remote Sensing In Fruit and Nut Crop Sscmmentioning

confidence: 99%

“…But small orchards (< 2.5 ha) surrounded by forests or tall grasses cannot be differentiated easily [8]. Therefore, high resolution imageries such as SPOT panchromatic (10- Shrivastava and Gebelein [48] performed a study in Florida to delineate citrus groves for economic assessment, using Landsat Enhanced Thematic Mapper Plus (30 m) imagery. Their results showed a significant correlation between citrus production/income with remotely sensed imagery-derived citrus area coverage.…”

Section: Image Platform To Use For Fruit and Nut Plant Sscmmentioning

confidence: 99%

Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review

Panda¹,

Hoogenboom

Paz

2010

Remote Sensing

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Site-specific crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. Even though the SSCM is very widely used in row crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various crop characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing OPEN ACCESSRemote Sensing 2010, 2 1974 blueberry in Georgia, U.S. The study also provides a list of spectral reflectance curves developed for some fruit and nut crops, blueberry, crowberry, redblush citrus, orange, prickly pear, and peach. The study also explains these curves in detail to help researchers choose the image platform, sensor, and spectrum wavelength for various fruit and nut crops SSCM.

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“…The ETM+ image were opened in ENVI Imagine, using a band combination of 5, 4, and 3 for supervised classification. Image classification parameters were set to non-parametric rule-parallelepiped, overlap rule-parametric, unclassified rule-parametric and parametric rule-maximum likelihood (Shrivastava and Gebelein 2007). Vegetation classification was done with reference to field investigation data.…”

Section: Images Interpretation and Land Cover Classificationmentioning

confidence: 99%

Desert disturbance assessments of regional oil exploitation by Aster and ETM+ images in Taklimakan Desert China

Ouyang

Hao

et al. 2007

Environ Monit Assess

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To feed its rapidly growing energy demand, oil exploitation in China has never been more intensive. The most obvious characteristics of oil exploitation are progressive and regional, which can be monitored by remote sensing, such as land use and cover change, either perpetual or temporary, during oil field development such as construction of oil well, roads, transportation systems and other facilities. In this paper, the oil field located on the north edge of Taklimakan Desert, in the Tarim River watershed in northwest of China. The disturbance effects of regional oil exploitation were the main content of regional environmental managements and monitoring. Based on Enhanced Thematic Mapper Plus (ETM+) and Aster images, analyzed regional land use and landscape change from 2001 to 2003. By the comparison, it can be concluded that the ecological quality was deteriorating in these 3 years. The woodland was degrading to grass and desert. The area of woodland dropped from 9.06 km(2) in 2001 to 3.24 km(2) in 2003 with a 64.23% decrease. At the same time, the area of shrubbery lessened 18.23%. On the other hand, the whole area of desert and Saline soils inflated from 15.08 km(2) in 2001 to 25.36 km(2) in 2003. The patch number of bare land did climb dramatically, but single patch area increased. The research demonstrated that desert and Saline soils patches were activated by the human behavior and climate change. The information from the ETM+ and Aster images was proved be an effective and efficient way to be applied in regional environmental managements.

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Land cover classification and economic assessment of citrus groves using remote sensing

Cited by 34 publications

References 36 publications

Object oriented classification of high resolution data for inventory of horticultural crops

Object oriented classification of high resolution data for inventory of horticultural crops

Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review

Desert disturbance assessments of regional oil exploitation by Aster and ETM+ images in Taklimakan Desert China

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