Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

Mitchell, M.; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

doi:10.1016/j.rsase.2016.01.003

Cited by 8 publications

(14 citation statements)

References 11 publications

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“…This effect was partly overcome by using 5-year cohorts. Although being in agreement with accuracies of other studies (Azzari & Lobell, 2017;Huang et al, 2017;Mitchell, Wilson, Twedt, Mini, & James, 2016), the accuracy may be increased by adding radar data as input to the classifier (Joshi et al, 2016). Also, adding satellite data with higher spatial and temporal resolution improves accuracy, like Sentinel2 data, with a resolution of 10 × 10 m, or even commercially available higher accuracy satellites, like Spot, Triple Sat or Worldview 3.…”

Section: Discussionsupporting

confidence: 74%

Revealing 35 years of landcover dynamics in floodplains of trained lowland rivers using satellite data

Harezlak

Geerling

Rogers

et al. 2020

River Research & Apps

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Lacking substantial erosive and sedimentation forces, regulated rivers allow their floodplains to become overgrown with forest, increasing the flood risk of the hinterland. In the Netherlands, floodplains have therefore been subjected to interventions, like clear cutting, lowering and creation of side channels, and management, consisting of grazing and mowing. However, the comprehension of how those activities influence landcover dynamics is lacking. The aim of this study is therefore to investigate long‐term landcover dynamics of a regulated river system through the lens of remote sensing. What transitions between landcover classes can be observed? And how (if) do management and interventions impact succession and retrogression of landcover classes? The study area comprised the upstream part of the Dutch Rhine River, its three branches and five adjacent floodplains. Satellite data (LandSat 5 and 8), encompassing a 35‐year period (1984–2018), were used for studying landcover dynamics. Landcover classification was based on seven classes: water, built‐up area, bare substrate, grass, herbaceous vegetation, shrubs and forest. Retrogression was highest for the landcover classes obstructing water flow (shrubs, forest and herbaceous vegetation), succession was most frequent on bare substrate, and water and grass were the most stable landcover classes. The regulated nature of the system became apparent from the spatial and temporal cacophony of landcover dynamics which differ from those of natural meandering rivers. This study showed that satellite data are useful for analyzing the impact of human activities within floodplains of regulated rivers and may assist in floodplain management aimed at combining water safety and nature policies.

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Section: Discussionsupporting

confidence: 74%

Revealing 35 years of landcover dynamics in floodplains of trained lowland rivers using satellite data

Harezlak

Geerling

Rogers

et al. 2020

River Research & Apps

View full text Add to dashboard Cite

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“…We found that the combination of object-based image analysis and decision tree classification was an effective method of classifying forest across a large area and is accurate based on comparison to ground truth data and global products. Utilizing a decision tree provides a transparent, repeatable, and easily interpreted process to facilitate regular updates and assessments of forest area and their spatial distribution [33]. Forest subcategory classification requires multi-temporal data to incorporate greater spectral variability within each class.…”

Section: Discussionmentioning

confidence: 99%

“…In the most recent decades, there are various remote sensing-based methodologies that have been widely used to obtain the extent and subcategories of forest, including conventional supervised and unsupervised classification [31,32], the phenology-based method [19], and object-based classification [33]. Using these methods, classification accuracies of 70–90% for forest cover or types were reported.…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Ren

Zhang

Wang

et al. 2018

Sensors

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Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.

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“…Object-based image analysis (OBIA) has become increasingly popular for land cover classification over the last decade [32] and is proven to be economical and efficient at large scale through use of more effective, transparent, and repeatable analytical processes [33]. The updating approach has potential to update land cover dataset effectively [34,35], which integrate the post-classification and change detection approaches [36].…”

Section: Forest Mapping With An Updating and Object-based Image Analymentioning

confidence: 99%

Spatio–Temporal Changes of Forests in Northeast China: Insights from Landsat Images and Geospatial Analysis

Chunying

Chen

Wang

et al. 2019

Forests

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Dramatic changes of forests have strong influence on regional and global carbon cycles, biodiversity, and ecosystem services. Understanding dynamics of forests from local to global scale is crucial for policymaking and sustainable development. In this study, we developed an updating and object-based image analysis method to map forests in Northeast China using Landsat images from 1990 to 2015. The spatio-temporal patterns of forests were quantified based on resultant maps and geospatial analysis. Results showed that the percentage of forested area occupying the entire northeast China was more than 40%, about 94% of initial forest cover remained unchanged (49.37 × 10 4 km 2 ) over the course of 25 years. A small net forest loss (1051 km 2 ) was observed during 1990-2015. High forest gain (10,315 km 2 ) and forest loss (9923 km 2 ) both occurred from 2010 to 2015. At the provincial level, Heilongjiang demonstrated the highest rate of deforestation, with a net loss of 1802 km 2 (0.89%). Forest changes along elevation, slope, and distance from settlements and roads were also investigated. Over 90% of forest changes occurred in plains and low mountain areas within the elevation of 200-1000 m and slope under 15 • . The most dramatic forest changes can be found within the distance of 2000 m from settlements and roads. The reclamation of sloping land, construction of settlements and roads, and possible smallholder clearing contributed more to forest loss, while ecological projects and related government policies play an important role on afforestation and reforestation. These results can provide useful spatial information for further research on the driving forces and consequences of forest changes, which have critical implications for scientific conservation and management of forests.

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Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

Cited by 8 publications

References 11 publications

Revealing 35 years of landcover dynamics in floodplains of trained lowland rivers using satellite data

Revealing 35 years of landcover dynamics in floodplains of trained lowland rivers using satellite data

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Spatio–Temporal Changes of Forests in Northeast China: Insights from Landsat Images and Geospatial Analysis

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