Land use change monitoring in Maryland using a probabilistic sample and rapid photointerpretation

Lister, Tonya W.; Lister, Andrew J.; Alexander, Eunice

doi:10.1016/j.apgeog.2014.03.002

Cited by 11 publications

(14 citation statements)

References 25 publications

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“…The plot design of the MD sample was chosen as part of a larger land-use change study [19]. For that study, a cluster design was chosen, with four points separated by 500 m arranged in a square pattern (Figure 2a).…”

Section: Plot Designmentioning

confidence: 99%

“…For PG, a plot design selection procedure was implemented, similar in principle to the concept of cyclic sampling described in Burrows et al [40]. Based on PI heuristics established by Lister et al [19], it was determined that a cluster plot with an array of 10 subplots (points) would conservatively allow us to finish all plots within the time and budget allotted for the study. Since the fragmentation metric used in this study, described below, relies upon grouping points into separation distance classes and summarizing land-use class adjacency frequencies, our goal was to select a 10-point configuration that would provide a roughly equal count of pairs of points per separation distance class.…”

Section: Plot Designmentioning

confidence: 99%

“…Next, we counted the number of pairs of points in each separation distance class and stored the result. We then randomly chose the final plot design from For MD and PG, the sampling design we used was chosen as part of a larger study of land-use in Maryland [19]. We used 5046 plots (described below) for the MD study and 3465 plots for the PG study.…”

Section: Plot Designmentioning

confidence: 99%

“…Efficient, low-cost protocols can be designed using less specialized knowledge and training than that which is typically needed for production mapping, something appealing to many REDD participants. For MD and PG, the sampling design we used was chosen as part of a larger study of land-use in Maryland [19]. We used 5046 plots (described below) for the MD study and 3465 plots for the PG study.…”

mentioning

confidence: 99%

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Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery

Lister

Weber

2019

Forests

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Forest fragmentation and degradation are a problem in many areas of the world and are a cause for concern to land managers. Similarly, countries interested in curtailing climate change have a keen interest in monitoring forest degradation. Traditional methods for measuring forested landscape pattern dynamics with maps made from classified satellite imagery fall short with respect to the compatibility of their forest definitions with information needs. In addition, they are not easily amenable to interpretation using tools like confidence intervals derived from survey sampling theory. In this paper, we described a novel landscape monitoring approach that helps fill these gaps. In it, a grid of photo plots is efficiently created and overlaid on high-resolution imagery, points are labeled with respect to their land-use by a human interpreter, and mean values and their variance are calculated for a suite of point-based fragmentation metrics related to forest degradation. We presented three case studies employing this approach from the US states of Maryland and Pennsylvania, highlighted different survey sampling paradigms, and discussed the strengths and weaknesses of the method relative to traditional, satellite imagery-based approaches. Results indicate that the scale of forest fragmentation in Maryland is between 250 and 1000 m, and this agrees with compatible estimates derived from raster analytical methods. There is a positive relationship between an index of housing construction and change in forest aggregation as measured by our metrics, and strong agreement between metric values collected by human interpretation of imagery and those obtained from a land cover map from the same period. We showed how the metrics respond to simulated degradation, and offered suggestions for practitioners interested in leveraging rapid photointerpretation for forest degradation monitoring.

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Section: Plot Designmentioning

confidence: 99%

Section: Plot Designmentioning

confidence: 99%

Section: Plot Designmentioning

confidence: 99%

mentioning

confidence: 99%

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Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery

Lister

Weber

2019

Forests

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“…The combination of photointerpretation and field visits is a very popular up-dating technique because of the high level of accuracy it provides [2]. However, it is very costly in terms of both the economic and time investments it requires.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

et al. 2017

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Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60-85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.

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Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest

Gelli

Costa

Nicolau

et al. 2022

Environ Monit Assess

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Land use change monitoring in Maryland using a probabilistic sample and rapid photointerpretation

Cited by 11 publications

References 25 publications

Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery

Semi-Automated Sample-Based Forest Degradation Monitoring with Photointerpretation of High-Resolution Imagery

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest

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