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A COMPARATIVE ANALYSIS OF PLANETSCOPE AND SENTINEL SENTINEL-2 SPACE-BORNE SENSORS IN MAPPING STRIGA WEED USING GUIDED REGULARISED RANDOM FOREST CLASSIFICATION ENSEMBLE
- Source :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W13, Pp 701-708 (2019)
- Publication Year :
- 2019
- Publisher :
- Copernicus GmbH, 2019.
-
Abstract
- Weeds are one of the major restrictions to sustaining crop productivity. Weeds often outcompete crops for nutrients, soil moisture, solar radiation, space and provide platforms for breeding of pests and diseases. The ever-growing global food insecurity triggers the need for spatially explicit innovative geospatial technologies that can deliver timely detection of weeds within agro-ecological systems. This will help pinpoint maize fields to be prioritized for weed control. Satellite remote sensing offers incomparable opportunities for precision agriculture, ecological applications and vegetation characterisation, with vast socioeconomic benefits. This work compares and evaluates the strength of Sentinel-2 (S2) satellite with the constellation of Dove nanosatellites i.e. PlanetScope (PS) data in detecting and mapping Striga (Striga hermonthica) weed within intercropped maize fields in Rongo sub-county in western Kenya. We applied the S2 and PS derived spectral data and vegetation indices in mapping the Striga occurrence. Data analysis was implemented, using the Guided Regularised Random Forest (GRRF) classifier. Comparatively, Sentinel-2 demonstrated slightly lower Striga detection capacity than PlanetScope, with an overall accuracy of 88% and 92%, respectively. The results further showed that the VNIR (Blue, Green Red and NIR) and the Atmospheric resistance Vegetation Index (ARVI) were the most fundamental variables in detecting and mapping Striga presence in maize fields. Findings from this work demonstrate that Sentinel-2 data has the capability to provide spatial explicit near real-time field level Striga detection – a previously daunting task with broadband multispectral sensors.
- Subjects :
- lcsh:Applied optics. Photonics
Striga hermonthica
010504 meteorology & atmospheric sciences
biology
lcsh:T
Multispectral image
0211 other engineering and technologies
lcsh:TA1501-1820
02 engineering and technology
Vegetation
Weed control
biology.organism_classification
lcsh:Technology
01 natural sciences
VNIR
Striga
lcsh:TA1-2040
Environmental science
Precision agriculture
lcsh:Engineering (General). Civil engineering (General)
Weed
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- ISSN :
- 21949034
- Database :
- OpenAIRE
- Journal :
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Accession number :
- edsair.doi.dedup.....b8194624a9192900d8e5b3225a3756a7
- Full Text :
- https://doi.org/10.5194/isprs-archives-xlii-2-w13-701-2019