Change detection of wetland ecosystems using Landsat imagery and change vector analysis

Baker, Corey; Lawrence, Rick L.; Montagne, Cliff; Patten, Duncan T.

doi:10.1672/0277-5212(2007)27[610:cdoweu]2.0.co;2

Cited by 118 publications

(55 citation statements)

References 38 publications

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“…Corcoran, Knight and Gallant [95] discriminated upland, water, and wetland areas using RF with a similar assortment of predictors (e.g., Landsat 5 TM NIR and SWIR, elevation and curvature, hydric soils data, as well as PALSAR (L-band) cross-polarization (HV) data). Other studies have confirmed the importance of NDVI [14], NDMWI [97], Tasseled-cap components [98,99], and Landsat thermal band 6 as wetland predictors [95,98]. Notably, "Net Radiation", derived from Landsat band-6 (thermal), was among the top five optical variables for this study, and class response was consistent across RF models.…”

Section: Random Forest Classifier Performance and Variable Importancesupporting

confidence: 82%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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Abstract:The Dabus Wetland complex in the highlands of Ethiopia is within the headwaters of the Nile Basin and is home to significant ecological communities and rare or endangered species. Its many interrelated wetland types undergo seasonal and longer-term changes due to weather and climate variations as well as anthropogenic land use such as grazing and burning. Mapping and monitoring of these wetlands has not been previously undertaken due primarily to their relative isolation and lack of resources. This study investigated the potential of remote sensing based classification for mapping the primary vegetation groups in the Dabus Wetlands using a combination of dry and wet season data, including optical (Landsat spectral bands and derived vegetation and wetness indices), radar (ALOS PALSAR L-band backscatter), and elevation (SRTM derived DEM and other terrain metrics) as inputs to the non-parametric Random Forest (RF) classifier. Eight wetland types and three terrestrial/upland classes were mapped using field samples of observed plant community composition and structure groupings as reference information. Various tests to compare results using different RF input parameters and data types were conducted. A combination of multispectral optical, radar and topographic variables provided the best overall classification accuracy, 94.4% and 92.9% for the dry and wet season, respectively. Spectral and topographic data (radar data excluded) performed nearly as well, while accuracies using only radar and topographic data were 82-89%. Relatively homogeneous classes such as Papyrus Swamps, Forested Wetland, and Wet Meadow yielded the highest accuracies while spatially complex classes such as Emergent Marsh were more difficult to accurately classify. The methods and results presented in this paper can serve as a basis for development of long-term mapping and monitoring of these and other non-forested wetlands in Ethiopia and other similar environmental settings.

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Section: Random Forest Classifier Performance and Variable Importancesupporting

confidence: 82%

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

et al. 2017

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“…Other studies have confirmed that thermal data is important for land cover classification, particularly in separating vegetated and impervious areas and different moisture levels throughout the landscape [58,93]. The Tasseled Cap transformation also has been used by others to improve wetland mapping [81,94]. We found that using radar backscatter was more useful than using the polarimetric decompositions; in particular, our findings further confirm those of others documenting the importance of co-and cross-polarization radar backscatter (HH and HV, respectively) in classifying land cover [95][96][97].…”

Section: Cowardin Wetland Classification (Level 2)mentioning

confidence: 94%

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

2013

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Abstract:Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource OPEN ACCESS Remote Sens. 2013, 5 3213 managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

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“…CVA output is a discrete map showing the change status and land-cover class of each pixel. We chose CVA as a baseline, or reference, change-analysis algorithm, as it is a well-established and commonly-used algorithm [30].…”

Section: Land-change Analysismentioning

confidence: 99%

What is the Direction of Land Change? A New Approach to Land-Change Analysis

et al. 2017

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Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a novel perspective on land-change analysis by focusing on directionality of change. To this end, we employed Maximum Cross-Correlation (MCC) approach to estimate the directional change in land cover in a dynamic river floodplain environment using Landsat 5 Thematic Mapper (TM) images. This approach has previously been used for detecting and measuring fluid and ice motions but not to study directional changes in land cover. We applied the MCC approach on land-cover class membership layers derived from fuzzy remote-sensing image classification. We tested the sensitivity of the resulting displacement vectors to three user-defined parameters-template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors-that directly affect the generation of change, or displacement, vectors; this has not previously been thoroughly investigated in any application domain. The results demonstrate that it is possible to quantitatively measure the rate of directional change in land cover in this floodplain environment using this particular approach. Sensitivity analyses indicate that template size and MCC threshold parameter are more influential on the displacement vectors than search window size. The results vary by land-cover class, suggesting that spatial configuration of land-cover classes should be taken into consideration in the implementation of the method.

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Change detection of wetland ecosystems using Landsat imagery and change vector analysis

Cited by 118 publications

References 38 publications

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota

What is the Direction of Land Change? A New Approach to Land-Change Analysis

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