Informatics Publishing Limited

B. K. Handique ¹, P. T. Das ¹, J. Goswami ¹, C. Goswami ¹, P. S. Singh ¹, C. J. Prabhakar ², C. M. Bajpeyi ², P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Shillong 793 103, IN
2 Central Silk Board, Ministry of Textiles, B.T.M. Layout, Madivala, Bengaluru 560 068, IN

Abstract

Potential areas for expansion of sericulture in 108 selected districts covering 24 states in the country were mapped using remote sensing, GIS and GPS tools. Special emphasis was given to northeastern (NE) region, where 41 districts out of a total of 108 districts were selected. Potential area identification for sericulture development was based on land evaluation, water resources and climatic requirements for growing silkworm food plants as well as rearing silkworms. Among NE states, Mizoram has maximum highly suitable area (4.7% of total geographical area) followed by Meghalaya (2.8%), that can be brought under mulberry sericulture. Among non-traditional sericulture states, Himachal Pradesh has the highest suitable area (0.9% highly suitable and 6.2% moderately suitable areas) in the selected districts. Among the five traditional sericulture states, Tamil Nadu has the highest area under highly suitable category, which is about 4% of the total geographical area in the selected districts. To provide information on sericulture and spatial information on potential areas for the selected 108 districts, a geoportal titled 'Sericulture Information Linkages and Knowledge System' (SILKS) was conceptualized and developed using open source GIS, and put in the public domain (http://silks.csb.gov.in). Within three years, the portal could make a significant impact in the country particularly in NE states and a number of sericulture expansion activities have been taken up based on the study.

Keywords

Geoportal, Geospatial Tools, Open Source GIS, Sericulture, Web Technology.

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B. K. Handique ¹, Jonali Goswami ¹, Abdul Qadir ¹, Chirag Gupta ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN

Abstract

In April 2016, farmers from Morigaon and Nagaon districts of Assam, India encountered severe pest infestation in their boro paddy (summer paddy) areas, which was unusual. Morigaon district with an area of 1550 sq. km having a population of about 9.6 lakhs (as of 2011), was the worst affected with four out of the five revenue circles, viz. Mayong, Bhuragaon, Laharighat, Morigaon and Mikirbheta being affected. Investigations by the District Agricultural Department and Regional Agricultural Research Station, Nagaon confirmed it to be infestation by brown planthopper (BPH), Nilaparvata lugens (Stal). Planthoppers are a problem in rainfed and irrigated wetland environments.

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Kasturi Chakraborty ¹, S. Sudhakar ¹, K. K. Sarma ¹, P. L. N. Raju ¹, Ashesh Kr. Das ²

Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, IN
2 Department of Ecology and Environmental Science, Assam University, Silchar 788 011, IN

Abstract

With the current trend of land use/land cover (LULC) change taking place globally, several parts of northeast India are also showing signs of change in LULC pattern leading to forest loss. This study focusses on the expansion of monoculture rubber plantation (Hevea brasiliensis) in selected sub-watersheds in north-east India, and distributed in parts of north Tripura, Mizoram and a major portion in the Karimganj district of Assam. Remote sensing and GIS technique has been used to map and analyse the extent of rubber plantation using temporal IRS LISS III satellite data from 1997 to 2013. It has been observed that rubber plantation increased from 4.47 sq. km to 28.42 sq. km in various parts of the study area. The expansion was more rapid during recent times, i.e. during 2010 to 2013. The plantation took place in dense forest, open forest and degraded forest areas. The spread of the plantation was also observed in one reserved forest located within the study area. There are several instances of negative impacts of rubber plantation expansion in Southeast Asia. Similar expansion of rubber plantation has been observed in northeast India as well. Further spread of rubber plantations in the region needs to be regulated to avoid conversion of dense and reserved forest areas by fostering use of mixed cropping methods instead of rubber monocultures, and by adopting more sustainable land use and management practices.

Keywords

Northeast India, Remote Sensing and GIS, Rubber Plantation.

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P. S. Singh ¹, Mayuri Sharma ², Victor Saikhom ¹, Dibyajyoti Chutia ¹, Chirag Gupta ¹, Avinash Chouhan ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Government of India, Umiam 793 103, IN
2 Department of Computer Science, Assam Don Bosco University, Guwahati 781 017, IN

Abstract

There has been increasing popularity in large scale mapping for deriving 3D surface and elevation models of earth and building structures. The techniques of computer vision comprising feature detections and matching and photogrammetry play an important role in deriving near accurate 3D reconstruction of scenes from 2D images. Since the images captured by the unmanned aerial vehicle (UAVs) are of high resolution, there is need for more sophisticated processing and analysis of the imagery to generate 3D models and other useful imagery products. The open source softwares are excellent tools for research and can be modified or changed to suit our model, as specific or combinations of algorithms behave differently based on the nature of UAV image scene to be processed. Though many algorithms are available for performing feature extractions from images, few studies have been carried out to identify suitable detector algorithms to be used based on the nature of image or scene that the UAV captures. An attempt has been made to understand and analyse the suitability of feature detection and descriptor algorithms for different scene types. This article also describes the popular technique called structure from motion process pipeline for sequential processing of UAV images with high overlapping, which involves the estimation of 3D point clouds from the keypoint correspondences. The relative accuracy of the 3D point cloud derived from our approach is comparable with similar output from other state-of-the-art UAV processing systems and is found to match with high precision.

Keywords

3D Reconstruction, Open Source, Point Clouds, Remote Sensing, Structure from Motion, Unmanned Aerial Vehicle.

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Gopal Sharma ¹, S. Mohanty ², P. K. Champati Ray ³, M. Somorjit Singh ¹, K. K. Sarma ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Application Centre, Umiam 793 103, IN
2 Indian Institute of Technology (Indian School of Mines), Dhanbad 826 004, IN
3 Indian Institute of Remote Sensing, Dehradun 248 001, IN

Abstract

A large magnitude (M_w 7.8) earthquake occurred on 25 April 2015 (06:11 UTC) at 28.1473°N and 84.7079°E, 34 km east-southeast of Lamjung, Nepal. The devastating event was accompanied by two large aftershocks of M_w 6.6 (on 25 April 2015, 06:45 UTC) and M_w 6.7 (on 26 April 2015 at 09:10 UTC). According to the USGS earthquake catalogue, 65 aftershocks were recorded within a period of three days from the main event; the strongest aftershock had occurred on 12 May 2015 at 07:05 UTC.

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Pratibha T. Das ¹, B. K. Handique ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Govt of India, Umiam 793 103, IN

Abstract

Suitable areas for boro rice expansion in Meghalaya were identified using geospatial technology based on land evaluation using information on soil, slope, elevation, rainfall and temperature. The study showed that 635 ha area is highly suitable followed by 581.74 sq. km and 219.07 sq. km area is marginally and moderately suitable respectively. The suitable areas are distributed in 20 blocks of 8 districts. More than 50% of suitable areas are distributed in West Garo hills. The highest suitable areas are found in Selsella and Dadenggre block. The findings of this study are being used by the user department for expanding boro rice cultivation in the state.

Keywords

Boro Rice, Geospatial Tools, Meghalaya, Site Suitability.

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Kasturi Chakraborty ¹, Victor Saikom ¹, Suranjana B. Borah ¹, Mamita Kalita ¹, Chirag Gupta ¹, Laishram Ricky Meitei ², K. K. Sarma ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, IN
2 Botanical Survey of India, ERC, Shillong 793 003, IN

Abstract

Frequent ground surveys and satellite-based information on tree height, canopy gaps and forest dynamics are limited by time, cost and spatial scales. In this study, an attempt has been made to derive forest biometric parameter on tree height by canopy height model and crown area projections using unmanned aerial vehicles (UAV)–RGB image. Sorensen’s coefficient has been used as an index to compare between ground inventory and UAV-based species identification. The statistical paired t-test showed UAV RGB can be used for maximum tree height and tree crown extraction to aid in ground surveys.

Keywords

Canopy Height Model, Canopy Area Projection, Forest Biometry, Unmanned Aerial Vehicles.

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Gaurav Singhal ¹, Babankumar Bansod ², Lini Mathew ³, Jonali Goswami ⁴, B. U. Choudhury ⁵, P. L. N. Raju ⁴

Affiliations
1 CSIR-Central Scientific Instruments Organization, Chandigarh 160 030, IN
2 Academy of Scientific and Innovative Research, Ghaziabad 201 002, IN
3 National Institute of Technical Teachers, Training and Research, Chandigarh 160 019, IN
4 North East Space Application Centre, Barapani 793 103, IN
5 ICAR Research Complex for NEH Region, Umiam 793 103, IN

Abstract

The present study provides a systematic comparison of parametric and non-parametric retrieval methods using high-resolution data provided by the unmanned aerial vehicle (UAV). We used turmeric crop reflectance data to evaluate the vegetation index (VI)-based parametric methods and compared them with linear and nonlinear non-parametric methods to build a rigorous LCC estimation model. The study demonstrates that the best-performing VI was the normalized green red difference index (GNRDI), with R² = 0.68, RMSE = 0.13 and high processing speed of 0.08 s. With regard to non-parametric methods, almost all methods outperformed their parametric counterparts. Particularly, methods such as random forest (RF) and kernel ridge regression (KRR) showed the best performance characterized by R² > 0.72 and RMSE ≤ 0.12 mg/g of fresh leaf weight. These nonparametric methods possessed the benefit of total spectral information utilization and enabled robust, non-linear relationship between the predictor and target variables, but computational complexity is a major drawback.

Keywords

Chlorophyll, Machine Learning, Unmanned Aerial Vehicle, Vegetation Index.

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Ashu Negi ¹, Tapan Adhikari ¹, Chandan Goswami ¹, B. K. Handique ¹, P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Umiam 793 103, IN

Abstract

India is the largest producer, consumer and exporter of turmeric (Curcuma longa L.). The Lakadong variety of turmeric is endemic to Jaintia Hills of Meghalaya, India. It is considered as the best quality turmeric containing 7.5% curcumin, which is about three times higher than the other varieties (2–3%). This study identifies potential sites for turmeric cultivation in Jaintia Hills using geospatial techniques, viz. analytical hierarchical process (AHP) and weighted overlay analysis (WOA). WOA identified a total of 162,263.70 ha suitable for the expansion of Lakadong variety of turmeric in Jaintia Hills, of which 18% was highly suitable, 31% moderately suitable and 32% was marginally suitable. In the case of AHP, 21% area was found to be highly suitable, 25% moderately suitable and 45% marginally suitable.

Keywords

Analytical Hierarchical Process, Site Suitability Analysis, Turmeric, Weighted Overlay Analysis.

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J. Goswami ¹, D. K. Gogoi ², N. Rasid ¹, B. K. Handique ¹, G. Subrahmanyam ², P. P. Bora ², R. Das ², P. L. N. Raju ¹

Affiliations
1 North Eastern Space Applications Centre, Department of Space, Umiam 793 103, IN
2 Central Muga Eri Research and Training Institute, Central Silk Board, Lahdoigarh, Jorhat 785 700, IN

Abstract

Flacherie is a major bacterial disease causing >40% loss during Muga summer crops. For finding the ischolar_main causes of the diseases, relationships were established between rearing and production data corresponding to land use/land cover, land surface temperature and meteorological parameters. Adverse affects were found in farms associated with anthropogenic activities, in contrast to forest cover which shows a negative trend. Muga disease early warning system, a mobile-based application and dashboard has been developed to predict rate of flacherie infestation at least 5–10 days in advance, for proper precautionary measures by the farmers to avoid disease outbreak and crop loss

Keywords

Crop Loss, Early Warning System, Flacherie Disease, Mobile-Based Service, Muga Silkworm, Remote Sensing. Muga Silkworm.

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