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Browse More Environmental Science courses. Reference Emission Level S: Moreover, the automated approach may include blurry and noisy data in the Google composites. However, there is little agreement among land-use change modelers as to whether projected deforestation rates should be predicted from average observed rates of forest cover loss or average observed rates of forest area lost. Because the PV, NPV and S spectral libraries already incorporate enormous variation in reflectance properties of land covers, including under widely varying atmospheric conditions, the probabilistic approach usually leads to a very stable result in each pixel, even if the data come from different sensors or times of the year as long as the data are not heavily cloud or atmospherically contaminated.

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This article has been cited by other articles in PMC. National Center for Biotechnology InformationU. Internet Explorer may not display all features correctly.

Given the limited Landsat data availability for our study region, we selected a monitoring period from to — a 25 year period covered by six time steps,andand therefore five sub-periods with deforestation net forest change analysis.

Our results using CLASlite demonstrated moderate temporal variability and the highest accumulated loss, while the UMD method had the greatest temporal variability due to the cladlite temporal resolution and the lowest levels of xlaslite loss. The course provides the scientific basis for each module in CLASlite, along with other information to make forest monitoring easy using Earth observing satellite data. Int J Remote Sens.

Free Online Course: Forest Monitoring with CLASlite from Stanford OpenEdx | Class Central

Multiscale analysis of tree cover and aboveground carbon stocks in pinyon-juniper woodlands. In all time steps, the mosaicked scene approach produced more forest cover change than the single scene approach — which is likely due to the addition of artifact-free pixels integrated from additional scenes to the observation area. While this study only used automatic cloud and shadow masking of CLASlite, users can also easily decide on inclusion or exclusion of regions of interest, by manually drawn polygons e.

There are some qualifications to be made about InSAR based deforestation monitoring: As a result, the Pacific coast of Colombia presents an extremely challenging case for optical remote sensing of forest cover and change.

He manages the algorithm development, programming, and processing of satellite and aircraft imagery for data extraction.

Automated mapping of tropical deforestation and forest degradation: CLASlite - Semantic Scholar

Instead, we compare the predicted range of REL values calculated from the deforestation results over the years from the three different approaches, and we compare the predicted range of reference emission levels. The additional forest change from the mosaicked approach, located in areas previously without data in the single-scene approach, varied from 7. Conservation and monitoring xlaslite tropical forests requires accurate claslihe on their extent and change dynamics.

We have found that using the greenest NDVI or other metrics for image or pixel-scale mosaicking such as with the Google product severely reduces the amount of apparent forest cover lost over time, and in some regions it vastly over-estimates forest recovery.

For forest cover change, the original forest cover in our case is important in order to map change from it. The role of remote sensing in measuring and monitoring forest area, and assessing its structural and functional attributes, has been well documented [ 2 ]-[ 4 ].

Automated mapping of tropical deforestation and forest degradation: CLASlite

Forests in the study region are exclusively humid Neotropical evergreen broadleaf in lowland, sub-montane, and pre-montane elevation rangesm a. A comparison of deforestation output from different remote sensing approaches can help to quantify the impact of monitoring approaches on cllaslite RELs [ 19 ].

Deforestation rate is an important parameter to express deforested area comparable between all locations and scales.

Although the two datasets switch in terms of accumulated deforestation mapped intheir overall accumulated outputs are, after 20 years, different by only 6. These decision trees are mostly empirically derived from validation studies in the field, and by input from the CLASlite user community [ 27 ].

Competing interests The authors declare that they have no competing interests. Gaps could be filled especially in the south-east of the study area. Open in a separate window. Please also see section 2.

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The results can be viewed as indications of relative changes in forest dynamics e. Anthony's College, London; Build and share your own catalog of courses with Class Central's custom lists. Sign up for free. Forest carbon community certification standard operated by the Plan Vivo foundation, a registered Scottish charity.

Photogramm Eng Remote Sens. She devises the online resources to train and support users, including this course. Comparing multi-spectral and hyperspectral observations.