In the context of land-use and land-cover (LULC) classification, there is a lack of leverage of the recent increase of the ease of access to satellite imagery data, cloud computing platforms, and classification techniques. We present both the development of an operational method for LULC classification that considers these progresses and the implementation of this operational method for a mountainous area of Nepal. The operational method allows the comparison of three LULC maps, each derived with a different classification technique [classification and regression tree (CART), max entropy (MaxEnt), and random forest (RF)] applied to Sentinel-2 data on the Google Earth Engine platform. The results derived with the RF technique have the highest overall accuracy coefficient (92%). The probabilities that the RF technique produces a more accurate LULC map than the MaxEnt (95%) and CART (61%) techniques are based on Kappa statistics. Results of general linear models suggest that some LULC types have higher producer’s and user’s accuracies at a statistically significant level. The operational method can help the producers of LULC maps conduct future work on areas in developing countries, as such contributing to addressing various issues that involve land use.
Multispectral remote sensing (MRS) sensors have proved their potential in acquiring and retrieving information of Land Use Land (LULC) Cover features in the past few decades. These MRS sensor generally acquire data within limited broad spectral bands i.e. ranging from 3 to 10 number of bands. The limited number of bands and broad spectral bandwidth in MRS sensors becomes a limitation in detailed LULC studies as it is not capable of distinguishing spectrally similar LULC features. On the counterpart, fascinating detailed information available in hyperspectral (HRS) data is spectrally over determined and able to distinguish spectrally similar material of the earth surface. But presently the availability of HRS sensors is limited. This is because of the requirement of sensitive detectors and large storage capability, which makes the acquisition and processing cumbersome and exorbitant. So, there arises a need to utilize the available MRS data for detailed LULC studies. Spectral reconstruction approach is one of the technique used for simulating hyperspectral data from available multispectral data. In the present study, spectral reconstruction approach is utilized for the simulation of hyperspectral data using EO-1 ALI multispectral data. The technique is implemented using python programming language which is open source in nature and possess support for advanced imaging processing libraries and utilities. Over all 70 bands have been simulated and validated using visual interpretation, statistical and classification approach.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.