Smartphones and Spatial Data Collection

 

    2.6 million years ago humans invented the stone tools which have changed the human evolution. From that, we haven’t changed our reliance on tools to make our life easy and better. We rely completely on the modern tools, in the information era too. Smartphones are one of them. 

    Nowadays the smartphones are just manifested beyond the purpose of communication. It can offer a lot to the geoscience studies. They could replace your paper maps, compass, GPS and questionnaire forms and could act as a personal assistant. So anyone can use it like a professional mapping device, navigator and data collector, etc. But how to do that? All you need to install the small apps on the device, That’s it. I found some apps are having great productivity and more helpful at the time of spatial and non-spatial data collection. Here I will explain how the apps act as economically viable tools for field data collection. 

    Researchers from the developing countries and nonprofit organizations might not have the budget for high-end GPS like Trimble geoExplorer and end up with using low budget gadgets. But the lower end gadgets are having limitations in data storage, visualization, data analysis and export (ex. Garmin etrex). Syncing and organizing all those data is a hectic problem too. Moreover using paper forms for questionnaire survey definitely need to convert them into digital by data entry, digitisation, assigning codes for data analysis, etc. 

Advantages of data collection apps

    The main advantage of the apps, allow us to collect, visualize, organize, analyze and manipulate the data at the time of data collection. Secondly, the user can define the survey design and data structure; we can define the necessary data inputs, which should be collected in the field and optional data which may or may not collected. Interestingly, some apps are supporting mobile and web-based platforms which are helping us to prepare the questionnaire forms with additional data like photo, audio, video and geolocation. This could be the added advantage for ecological researchers to do species identification surveys where the photos could help to identify the species. Most of these apps allow us to download the online maps and save it for offline use purpose or we can use our own map (raster format) as a base map with the help of MapTiler like software. 

    In the end, instead of writing or marking the data in a paper, a single click or a drop down menu button could reduce the survey time and which might increase the quality of the survey. At the end of the survey, we can save the data physically in the device or they can send back to the online servers simultaneously or when they have the internet connection. Most important thing is many of the apps are supporting us to export the data into different GIS friendly formats like GPX, txt, kml and shp. Additionally you could store the geotagged photos with the waypoints. So the next time, think about adopting any one of the following apps for your data collection!

Important apps worth a try!!

GeoODK (my favorite!!), CyberTracker, Open Data Kit ,Collector for ArcGIS, Epicollect, PDF Maps, Aquasurvey, Mobile Topographer ,Map it

Application of Spectral Indices in Landscape Ecology

       



    Spectral Indices are nothing but the ratio between two or more wavelengths. It converts the multiple spectral band values to a single value which is easy to correlating to biophysical variables (Tucker 1979). Their sensitivity to the biophysical properties is higher than the individual spectral bands and it can be used to study the earth system that includes physical, chemical and biological process. The advantage of the indices are easy to compute because of the simple algorithms and requires minimum computation power. It can be done over the large area with minimal ancillary data to classify and extract the information what the user required. The best example is NDVI which is used over the decades to do the qualitative as well as the quantitative study of biophysical variables. The application of spectral indices in landscape ecology is quite enormous. Vegetation indices were created and validated on their usefulness in vegetation characteristics studies. The parameters like phenology, spatial distribution, extent, vegetation decomposition, vegetation fraction, productivity and stress has been studied with the help of remote sensing indices. The temporal indices could be used to quantify the forest fire, drought, desertification, deforestation and flooding.
    The selection of indices depends on the parameters to be studied as well as the landscape where it presence. Example, the indices like Soil Adjusted Vegetation Index (SAVI) has a soil background adjustment factor which could reduce the soil influence and enhance the vegetation signals (Huete 1987). This is specially designed for rangeland and grassland mapping. Another example is ARVI, a modified NDVI best suitable for deserted area studies, because of its usefulness on aerosol presence. Generally the ARVI is four times less sensitive to the atmospheric effect than the NDVI (Kaufman 1992). Indices like TSAVI, MSAVI and tasseled cap index are the better indices for wetland vegetation mapping because the soil brightness and wetness plays an important role (Tiner et al 2015).
    The modern day development of sensors have increased the choices of wavelength selection to study the earth. The hyperspectral sensors having narrow wavelengths which could give more accurate results on this kind studies. Even though the spectral indices have more benefits than individual wavelengths, but it has their own limitations too. The ranges of values for a single parameter may differ at different locations and seasons. Also, the accuracy of the results depends on the data quality, image preprocessing, complexity of the landscape and the quality of field data.

References   

1. Tiner, Ralph W., Megan W. Lang, and Victor V. Klemas. "Remote Sensing of Wetlands: Applications and Advances." (2015).
2.  Tucker, Compton J. "Red and photographic infrared linear combinations for monitoring vegetation."Remote sensing of Environment 2 (1979): 127-150.
3. Huete, A. R., and R. D. Jackson. "Suitability of spectral indices for evaluating      vegetation characteristics on arid rangelands."Remote sensing of environment2 (1987):    213-IN8. 
4. Kaufman, Yoram J., and Didier Tanre. "Atmospherically resistant vegetation index (ARVI) for EOS-MODIS."Geoscience and Remote Sensing, IEEE Transactions on 2 (1992): 261-270.