Laboratory_7
Lab_7
Vegetation Trend Analysis in Minnesota and Wisconsin areas
Introduction:
Many things on the land have been changing over time in the Earth. Those changes would significantly affect the human in a good way or bad way. People in the world have responsibility to address these phenomena and should monitor how can this be influenced by human activity.
In remote sensing method, we can do vegetation trend analysis to see how the vegetation tends to be growing in a two types, such as greening and browning. Browning trend indicates that vegetation in that particular area has degradation of green grass land and vice versa for greening. Therefore, in this lab, we are going to use outputs from previous lab and apply to TerrSet to create the Ordinary Least (OLS) trend analysis and its significance in Minnesota and Wisconsin areas. Therefore, we can assume that whether this areas have particular vegetation trends or not. We can also confirm that whether this phenomena is effected by human activity or not.
Study Area:
Figure 1. Study area satellite image that contains Minnesota and Wisconsin areas.
This data was obtained by MODIS that collected from 2000 - 2017. The data type is 16 days composites and it was positioned at Upper Midwest, Great Lake Region. Data was downloaded from NASA website and image was taken during growing season.
Figure 2. Earth Trend Modeler program through 2000 - 2017
After we have discovered the trend, we can do some statistical magnitude calculation of OLS Trend Regression and also able to calculate significance and applying the equation (b1 lt 0.05)*b2 to get output of significant pixel.
Figure 3. OLS regression result in study area
After done with this process, we open the image in Arc map which show the significance in pixel. We then classified into 4 classes that depict trend of greening and browning.
We also need to download shapefile of Minnesota and Wisconsin to mask out the output so we can only see the output in terms of two states.
Figure 4. Significance of Browning and Greening shown by Arc-map
Figure 5. Significance of Browning and Greening masked by MN & WI
In this lab, we also need to see browning and greening trend in different land cover type. In this case, we need ENVI and select the ROI (Region of Interest) and set the band threshold to select the pixel in particular land cover types. We used LULC.
Based on the table above (Figure 7.), 'Closed Evergreen Forest' land type has largest significant area and its significance of G&B was relatively same magnitude. 'Closed Deciduous forest' has been assigned second largest area for significant trend and number of its significant browning and greening pixel was relatively in high standard. Herbaceous land type also has decent amount of greening significance.
According to the results that were used by remotely sensed methods such as ENVI and TerrSet, we are able to monitor the vegetation trend in MN and WI. We can also make hypothesis of whether this area has high vegetation reduction or not.
This data was obtained by MODIS that collected from 2000 - 2017. The data type is 16 days composites and it was positioned at Upper Midwest, Great Lake Region. Data was downloaded from NASA website and image was taken during growing season.
Methodology:
Pre-processing steps are needed to be used in TerrSet thus, we might need to compile the the data in ENVI referred to previous lab, and then we can tack them in to time series data in TerrSet.
In the TerrSet, we convert the stacked NDVI raster file to ldrisi file to be used in the Earth Trend Modeler program.
In the TerrSet, we convert the stacked NDVI raster file to ldrisi file to be used in the Earth Trend Modeler program.
Figure 2. Earth Trend Modeler program through 2000 - 2017
After we have discovered the trend, we can do some statistical magnitude calculation of OLS Trend Regression and also able to calculate significance and applying the equation (b1 lt 0.05)*b2 to get output of significant pixel.
Figure 3. OLS regression result in study area
After done with this process, we open the image in Arc map which show the significance in pixel. We then classified into 4 classes that depict trend of greening and browning.
We also need to download shapefile of Minnesota and Wisconsin to mask out the output so we can only see the output in terms of two states.
In this lab, we also need to see browning and greening trend in different land cover type. In this case, we need ENVI and select the ROI (Region of Interest) and set the band threshold to select the pixel in particular land cover types. We used LULC.
Results:
Figure 6. Map of significant Greening and Browning in MN & WI
According to the results I got, the area with statistically significant browning trend was 16,322 km sq sq. and for the greening trend, it was 20,489 km sq.
The highest magnitude of browning was -426.72 and 366.46 for the greening.
Figure 7. LULC land types with significant G&B trend.
Discussion & Conclusion:
Overall, based on the result, we can visually recognize north and south sections of Minnesota have been greening significantly except one particular area that has intense browning trend and we assume there might be construction or forest fire in that specific area. Wisconsin also has little amount of significant greening at north ans south. The overall phenomena of Wisconsin would be browning trend.Based on the table above (Figure 7.), 'Closed Evergreen Forest' land type has largest significant area and its significance of G&B was relatively same magnitude. 'Closed Deciduous forest' has been assigned second largest area for significant trend and number of its significant browning and greening pixel was relatively in high standard. Herbaceous land type also has decent amount of greening significance.
According to the results that were used by remotely sensed methods such as ENVI and TerrSet, we are able to monitor the vegetation trend in MN and WI. We can also make hypothesis of whether this area has high vegetation reduction or not.
References:
Didan, K. (2015). MOD13C1 MODIS/Terra Vegetation Indices 16-Day Global 0.05Deg CMG V006 [Data set]. NASA EOSDIS LP DAAC. doi: 10.5067/MODIS/MOD13C1.006
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