Natural Resource Mapping Using Landsat and Lidar towards Identifying Digital Elevation, Digital Surface and Canopy Height Models

What is it about?

The natural resources signifying available land, water and forest have been serving the mankind by providing valuable services like food items, fuel and fodder, important medicines, regulating the air and water currents, protecting the soil and its components etc. FAO [1]. Moreover still the rural livelihood in every country is dependent on these natural resources and its condition for their sustainability and livelihood World Bank [2]. The major factors like population growth agriculture expansion and increased human well-being have been at the cost of forest depletion. The exponential increase in human population from seven billion to nine billion in the coming years has increased pressure on natural resources which are depleting at a faster rate than expected Millennium Ecosystem Assessment [3]. Further, the analysis of Ivanova et al. [4] has confirmed that some of the ecosystem services are at stake. We may soon face water scarcity, flooding of cities (because of degradation of wetlands) and increase in temperatures (because of overgrazing and overexploitation of forest trees).

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Photo by Markus Spiske on Unsplash

Photo by Markus Spiske on Unsplash

Why is it important?

The evaluation of the natural resource plays a decisive role in management and sustainable developmental planning. The study aims to generate natural resource based maps of rural areas of the USA using remote sensing datasets such as Landsat and LiDAR to analyze generated natural resources for developing digital elevation (DEM) and canopy height models (CHMs). Natural resource maps were generated using Landsat image of the year 2010. The area statistics reveal that the tree class percent (23.21 %) was the second highest after fallow land class (60.44 %) whereas shrubs and grass/agriculture was 6.59 % and 6.47 %, respectively. Later, LiDAR datasets for the same time were used to generate DEM/surface models (DSM) and CHMs.