Comparison of citrus orchard inventory using LISS-III and LISS-IV data

Singh, N.; Chaudhari, K. N.; Manjunath, K. R.

doi:10.1117/12.2228059

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Singh et al [5] states that in his study, shows classification accuracy increased from 55% while using LISS III to 77% when using LISS IV. Also, according to classified outputs and NDVI values obtained, April and May months were identified as optimum bio-window for crop identification.…”

Section: Introductionmentioning

confidence: 82%

Mango Area Mapping Using Very High-Resolution Satellite Data in Major Blocks of Krishnagiri District, Tamil Nadu, India

Harish

Kaliaperumal

Pazhanivelan

et al. 2023

IJECC

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A research study was carried out for mapping mango plantation from LISS IV data in the major blocks of Krishnagiri district, Tamil Nadu where there is substantial production of Mango. A very high-resolution satellite data namely LISS IV was acquired and processed with GIS tools. Ground truth data gathered during the survey were utilized to identify significant dB values for mango plantations, which were then used to classify the mango pixels in the study region using supervised classification technique. The Mango area in major blocks of Krishnagiri district was found to be 9077.9 ha during the year 2023. Accuracy assessment and cross validation was done using confusion matrix with the ground truth points collected. The classification resulted with an overall accuracy of 91.2 per cent with a kappa score of 0.62.

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Section: Introductionmentioning

confidence: 82%

Mango Area Mapping Using Very High-Resolution Satellite Data in Major Blocks of Krishnagiri District, Tamil Nadu, India

Harish

Kaliaperumal

Pazhanivelan

et al. 2023

IJECC

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“…For better classification, in general, the RS data of two dates were used with a caution that at least one from summer season when other field crops have least population. For citrus (Singh et al, 2016), used LISS-III and LISS-IV data of two dates in order to identify the orchard area and also found better accuracy with LISS -IV data. For banana, the union of classified data of two dates were used to estimate the gross banana acreage (Nishant et al 2016).…”

Section: Supervised Classificationmentioning

confidence: 99%

Crop Inventory of Orchard Crops in India Using Remotely Sensed Data

Chaudhari

Nishant

Upadhyay

et al. 2019

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

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<p><strong>Abstract.</strong> The use of satellite remote sensing (RS) technologies for purpose of crop discrimination, mapping, area estimation, condition and yield assessment has been proved to be effective and efficient in terms of time and cost, having better consistency implemented with scientific approaches. However, application of satellite RS technology for horticultural crops in India has certain challenges due to scattered and small field sizes, comparatively short duration such as vegetable crops and mixed cropping. Hence the study was taken for developing research methodology for area assessment of three major fruit crops such as Banana, Mango and Citrus over 20 districts in four states viz. Gujarat, Madhya Pradesh, Uttar Pradesh and Bihar. Appropriate bio-window for analysing different crop types was selected and mapping of crops were done using pixel based hybrid classification i.e. un-supervised ISODATA clustering plus supervised MXL classification as well as object based classification of high resolution remote sensing data (Resourcesat LISS III and/or LISS IV, Cartosat – 1 PAN) followed by their accuracy assessment and their comparison with departmental reported statistics. Overall, the classification accuracy was more than 80% for all the crops. Deviation from statistics were in the range of 3 to 38%. Higher deviations from statistics were mostly due to use of lower resolution satellite data or mixing of crops having similar spectral signatures e.g. mango and sapota in Navsari and Valsad districts of Gujarat. It was very difficult to discriminate the young orchards of 2&ndash;3 years from other field crops due to mixed / inter cropping practices. The maps were checked and certified by respective State Horticulture Departments and were archived in VEADS, SAC and BHUVAN, NRSC geoportals of ISRO. RISAT – 1 (microwave) data were explored for the estimation of banana orchards in order to detect banana plantation at early stage and under cloudy sky conditions. There is huge potential of application in this sector using advanced observations from hyperspectral, thermal infrared sensors and advanced radars or LIDAR’s on-board upcoming satellites.</p>

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Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

Ray

Dadhwal

Navalgund

2022

J Indian Soc Remote Sens

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The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982), IRS Utilization Program led (1983( -1995( ), post-IRS-1C launch (1996( -2011 and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings.

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Comparison of citrus orchard inventory using LISS-III and LISS-IV data

Cited by 3 publications

References 5 publications

Mango Area Mapping Using Very High-Resolution Satellite Data in Major Blocks of Krishnagiri District, Tamil Nadu, India

Mango Area Mapping Using Very High-Resolution Satellite Data in Major Blocks of Krishnagiri District, Tamil Nadu, India

Crop Inventory of Orchard Crops in India Using Remotely Sensed Data

Progress and Challenges in Earth Observation Data Applications for Agriculture at Field Scale in India and Small Farm Holdings Regions

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