Collect Earth: An online tool for systematic reference data collection in land cover and use applications

Saah, David; Johnson, Gary W.; Ashmall, Billy; Tondapu, Githika; Tenneson, Karis; Patterson, Matthew; Poortinga, Ate; Markert, Kel; Quyen, N. H.; Aung, K. S.; Schlichting, Lena; Matin, Mir A.; Uddin, Kabir; Aryal, Raja Ram; Dilger, John; Ellenburg, W. L.; Flores-Anderson, Africa; Wiell, D.; Lindquist, Erik; Goldstein, Joshua; Clinton, Nick; Chishtie, Farrukh

doi:10.1016/j.envsoft.2019.05.004

Cited by 112 publications

(59 citation statements)

References 18 publications

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“…This research was motivated by the need for information on forest degradation using definitions that correspond precisely with what a human interpreter would choose, as well as interest in leveraging the abundance of georeferenced national forest inventory data [34,38,60] and PI-based forest monitoring information [28] that has become readily available. Potential applications include multi-scale comparisons of changes in metric values or differences between analysis units (e.g., county groups, Figure 5, and related discussion), or use as a covariate in assessments of forest condition and health, as has been done with other fragmentation metrics [61,62].…”

Section: Applicationsmentioning

confidence: 99%

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: Applicationsmentioning

confidence: 99%

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

Lister

Weber

2019

Forests

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“…Each selected sample location was then assessed by a trained, experienced image analyst to identify the LULC class for 1997 and 2010 time periods using assorted reference data that included Landsat false color RGBs and higher spatial resolution imagery from Bing and Google via the QGIS OpenLayers plugin. We considered using the Collect Earth tool (Bey et al, 2016;Saah et al, 2019a) for accuracy assessment, though selected QGIS for this task mainly due to its image enhancement, vector grid overlay, vector editing, and the "AcATaMa" accuracy assessment plugin capabilities. Within QGIS, some of the random sample areas were also reviewed on various dates of high-resolution true color imagery resident to the Google Earth Pro software.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

Spruce

Bolten²,

Mohammed

et al. 2020

Front. Environ. Sci.

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“…Error rates are generated for both the primitive and land cover products using independent, probabilistic reference data sets. For the full details of the system architecture and co-designed products, please refer to Poortinga et al (2019), Saah et al (2019), Saah et al (in press), and Potapov et al (2019); other manuscripts are forthcoming. The Mekong data is available at https://rlcms-servir.adpc.net/en/.…”

Section: Examples Of the Operationalized System And Architecturementioning

confidence: 99%

“…Accurate and timely land cover maps play a critical role in a variety of sectors in the developing world including food security, land use planning, hydrology modeling, and natural resource management planning. Countries like Cambodia and Vietnam suffer from substantial rice crop yield losses and understanding the spatial distribution of such variable yields are critical for agricultural planning for food security (Pandey et al, 2007;Saah et al, 2019). National development plans use land cover as a basis for understanding changes in a country's natural capital, that in turn forms the basis for budget priorities and allocations (Tucker et al, 1985;Bounoua et al, 2002;Foley et al, 2005;Jung et al, 2006;Running, 2008).…”

Section: Introductionmentioning

confidence: 99%

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

Saah

Tenneson²,

Matin

et al. 2019

Front. Environ. Sci.

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Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity, and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system that meets land management needs. Local capacity may be replaced by external consultants or methods which lack long-term sustainability. In this study, we characterize and respond to the key land cover mapping gaps and challenges encountered in the Lower Mekong (LMR) and Hindu Kush-Himalaya (HKH) region through a needs assessment exercise and a collaborative system design. Needs were assessed using multiple approaches, including focus groups, user engagement workshops, and online surveys. Efforts to understand existing limitations and stakeholder needs resulted in a co-developed and modular land cover monitoring system which utilizes state-of-the-art cloud computing and machine learning which leverages freely available Earth observations. This approach meets the needs of diverse actors and is a model for transnational cooperation.

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Collect Earth: An online tool for systematic reference data collection in land cover and use applications

Cited by 112 publications

References 18 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

Mapping Land Use Land Cover Change in the Lower Mekong Basin From 1997 to 2010

Land Cover Mapping in Data Scarce Environments: Challenges and Opportunities

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