This project, "Characterizing Vegetation Using NEON Remote Sensing Data" was completed in 2020 by students who participated in Earth Lab's HDR Earth Data Science Corps (EDSC) summer internship, which focuses on teaching technical data science skills to diverse student populations.</p
Generating a satisfactory classification image from remote sensing data is not a straightforward tas...
<p>This is the code to implement a canopy height model for a submission to the data science competit...
In the face of a dramatically changing climate, the need to model, monitor, and respond to our envir...
This NEON teaching data subset contains data for the San Joachium field site located in southern Cal...
<p>This includes teaching data subsets that contains spatio-temporal data for the National Ecologica...
Accurately mapping tree species composition and diversity is a critical step towards spatially expli...
This report concludes my six-months internship within the Consultative Group on International Agricu...
Teaching data subsets for the NEON Lower Teakettle (TEAK), San Joaquin Experimental Range (SJER), an...
Over the past decades, the discipline of remote sensing has become an essential tool in earth scienc...
Forest management requires the use of comprehensive remote sensing data which enable monitoring of b...
The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring i...
The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts...
<p>This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJE...
Non-Peer ReviewedRemote Sensing, with its unique characteristics of multi-spatial, multi-temporal, a...
Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but me...
Generating a satisfactory classification image from remote sensing data is not a straightforward tas...
<p>This is the code to implement a canopy height model for a submission to the data science competit...
In the face of a dramatically changing climate, the need to model, monitor, and respond to our envir...
This NEON teaching data subset contains data for the San Joachium field site located in southern Cal...
<p>This includes teaching data subsets that contains spatio-temporal data for the National Ecologica...
Accurately mapping tree species composition and diversity is a critical step towards spatially expli...
This report concludes my six-months internship within the Consultative Group on International Agricu...
Teaching data subsets for the NEON Lower Teakettle (TEAK), San Joaquin Experimental Range (SJER), an...
Over the past decades, the discipline of remote sensing has become an essential tool in earth scienc...
Forest management requires the use of comprehensive remote sensing data which enable monitoring of b...
The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring i...
The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts...
<p>This is a beta teaching dataset that contains spatio-temporal data for the Harvard Forest and SJE...
Non-Peer ReviewedRemote Sensing, with its unique characteristics of multi-spatial, multi-temporal, a...
Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but me...
Generating a satisfactory classification image from remote sensing data is not a straightforward tas...
<p>This is the code to implement a canopy height model for a submission to the data science competit...
In the face of a dramatically changing climate, the need to model, monitor, and respond to our envir...
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