Forestland Converted to Cropland in

Hubbard County, MN

Henry RodmanCory KimballSpring 2013

FR 3262

Project Description

In this study we have conducted a survey on the transition of forest

land to agricultural land in Hubbard County, Minnesota. Hubbard County

covers 639,360 acres in North Central Minnesota and is made up of 28

townships and 1008 sections. Over the last several years, there has been a

trend of increased agricultural practices on the landscape – mostly in the

form of forests converted to potato fields. Hubbard County has been known

for its potato crops and this seems to be the main source of land conversion

according to the county Land Department,

possibly as a result of the expiration of many

Conservation Reserve Program (CRP)

easements. In Minnesota alone, there was a

declined enrollment in CRP of 43,000 acres

between 2011 and 2012 (Haroldson 2012).

Using image classification software and

Landsat imagery we aim to quantify the

amount of land that has been converted from

forestland to cropland in recent years.

Remote sensing, in combination with field observations provides a

great toolset for quantifying the amount of deforestation in the last several

years. Deforestation is often associated with slash and burn agriculture

practices in the Amazon rainforest, but evidence suggests that it is a growing

problem in northwest Minnesota. With access to orthorectified satellite

imagery and image processing software, the task of measuring changes in

land-use becomes quite streamlined. We aim to

classify land-use in four images of Hubbard

County and create historical comparisons

between these images and previously developed

land-cover maps. By creating maps that show

change from year to year and comparing land-

use between several years, we can make inferences about the severity and

rate of deforestation in Hubbard County.

Materials, Tools, and Concepts:

We used 7-band Landsat imagery collected in the summer months

between 2000 and 2009 and accessed through the GLOVIS browser at the

USGS website. In addition, we had on-the-ground photos to provide some

context for the discussion on deforestation. These photos were taken in April

of 2013 by Cory Kimball in Park Rapids, MN. We

also had personal conversations with individuals at the Hubbard Co. Land

Department that have provided some perspective on land-use changes.

Digital image classification is a very powerful tool in land-use

classification efforts so we made use of our training and expertise in these

processes to produce estimates of land-use. Supervised classifications of the

imagery were carried out using ERDAS Imagine 2011 (ERDAS) and shapefile

manipulation was performed in ArcGIS 10.1. The combination of these two

Figure 1: Potato Field near Park Rapids, MN

software packages gave us the tools we needed to make estimates of land-

use change over a decade-long period.

Procedures

1. Imagery containing the study area was acquired from the USGS,

accessed from the GLOVIS browser.

a. Imagery was captured in either July or August, many of the

images had either significant amounts of clouds, or (if the image

was taken from Landsat 7) there were streaks that made it

unusable

b. The best combination of images was included July 2000 and July

2009

2. Landsat GEOTIFFs were stacked in ERDAS and saved as ‘.img’ files

3. Imagery for each year was reprojected into the NAD1983 Zone 15N

datum

4. Each image was clipped in ERDAS to the study area, Hubbard County

5. Training samples for a supervised classification were created for the

2000 and 2009 imagery in an AOI and entered using the

signature editor.

a. Classes included: Forest, clearcut forest, wetlands,

developed (commercial/residential buildings, roads),

agriculture, clouds, shadows.

b. 5-20 training samples for each class (more for forest and

agriculture than the others).

Figure 2: Clipped image

6. Supervised classification was performed and effectively similar classes

were combined.

a. Forest: forest + clearcut forest, non-forest: developed +

agriculture, clouds/shadows: clouds + shadows

7. Histograms of each classified image were

compared to assess percent change in land use

a. Proportion of total pixels classified

was calculated for each land-use

classification

8. Accuracy assessment of the classification was carried out in ERDAS

using the Landsat imagery as reference data with a stratified random

sampling of points to be compared.

Results:

The results of the supervised classifications did not reflect any significant

change in land-use between 2000 and 2009. There were small fluctuations in

percent land cover of forest, agriculture and wetlands but the results of the

accuracy assessments suggest that there was no significant observable

difference between the classified images from the two years.

It is possible that the slight increase in cloud cover in 2009 contributed to the

muted differences in other land-use classes. Based on field observations and

trends in CRP enrollment in Minnesota, it seems unlikely that there was no

significant deforestation between 2000 and 2009. The results of this

comparison are likely skewed by the inaccuracy of the supervised

classifications.

Discussion:

Although we were unable to produce any ground-breaking results, this

endeavor was a good introduction to the process of managing imagery and

gathering useful information from amazingly low-cost sources. The final

result was largely a product of trial-and-error and the

eventual choice of Landsat data proved to make the

project a manageable task. The 1-m resolution NAIP

data was conveniently available as one Hubbard

County-wide image, but the file was so gargantuan (700

MB zipped) that image classification processes would

drag on for hours, and produce no results. After

struggling with those files, stacking the Landsat

GEOTIFFs was a welcome break from the cumbersome

processes associated with the NAIP imagery, even at the cost of decreased

resolution.

When we settled on Landsat imagery, the challenge then became

sifting through the vast amounts of data to find images that were not

corrupted by cloud cover or Landsat 7 lines. We

emerged from this step with two relatively cloud=free images that were

separated by an appropriate amount of time (2000, 2009). We hoped that

that window would be wide enough to make any change in land use apparent

at 30 meter spatial resolution.

The next challenge was creating accurate, valuable training samples

for the supervised classification. This was difficult because there was a

striking similarity between recently harvested forest and some agricultural

fields – the color of emergent vegetation on recently harvested forest are

very similar to many crop fields. Additionally, wetlands appeared had a

Figure 3: Excessive cloud-cover, courtesy of the USGS

similar color signature to some crop fields. The ambiguity between these

classes was probably the reason our results did not reflect the trend of

increasing deforestation, so it seems that imagery from late spring, early

summer, or early fall would have been better choices for classification

purposes. Another direction we could have taken in distinguishing forest

from agricultural fields was the use of LiDAR data – it is available statewide,

but historical comparison between LiDAR data sets may not have been

possible because the statewide dataset is a very recent development.

The accuracy assessment reflected the difficulty of creating adequately

distinct training samples for each class – the 2000 classification yielded an

overall accuracy of 72%, while the 2009 classification yielded 78.67%. Many

of the classification errors occurred around the edges of clouds, between

wetlands and agricultural fields, and in recently harvested areas. The

questionable accuracy of the classifications is what led us to downplay the

significance of our results regarding land-use change in Hubbard County –

however, it would not require a large expansion of time or resources to

accomplish a more meaningful study. By following our procedure and using

imagery from a different time of year, a subsequent study could reveal the

true amount of deforestation in Hubbard County.

Appendix

Imagery:

Classifications

Figure 4: Landsat Imagery from July 2000, courtesy of the USGS

Figure 5: Landsat Imagery from July 2009, courtesy of the USGS

Figure 6: 2000 Classification Figure 7: 2009 Classification

Accuracy Assessment

References

1. DNR Data Deli. Shapefile Database Accessed 3/2013.

2. FR 3262 Lab Manuals. Accessed between 1/2013 and 5/2013. Dept. of

Forest Resources, University of Minnesota.

3. Haraldson, Kurt J., 2012 Minnesota August Roadside Survey.

September 2012. Minnesota Department of Natural Resources.

4. Lomeier, Mark. Personal Communication April 5, 2013

5. Trappe, Kevin. Personal Communication April 5, 2013

6. USGS Eros Data Center: GLOVIS Browser, Accessed April 15, 2013