Potential effects in multi-resolution post-classification change detection

Colditz, René R.; Acosta-Velázquez, Joanna; Gallegos, José Reyes Díaz; Lule, Alma Delia Vázquez; Rodríguez-Zúñiga, María Teresa; Maeda, Pedro; López, María Isabel Cruz; Ressl, Rainer

doi:10.1080/01431161.2012.688148

Cited by 35 publications

(19 citation statements)

References 29 publications

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“…We resampled the two coarse resolution maps of 60 m to 30 m using the nearest neighbor algorithm to avoid information loss in the three fine spatial resolution maps. This procedure has been found to perform similarly to the resampling method, which coarsens the fine resolution map [79].…”

Section: Quantification Of Land Cover Change and Habitat Fragmentationmentioning

confidence: 99%

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Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

et al. 2015

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Abstract:In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and the provisioning of ecosystem services. To find sustainable land use options adapted to the local situation, a profound knowledge of the long-term spatiotemporal patterns of land cover change and its drivers is necessary, but hitherto lacking. The complex topography and the high cloud frequency make the use of remote sensing in this area a challenge. To deal with these conditions, we pursued specific pre-processing steps before classifying five Landsat scenes from 1975 to 2001. Then, we quantified land cover changes and habitat fragmentation, and we investigated landscape changes in relation to key spatial elements (altitude, slope, and distance from roads). Good classification results were obtained with overall accuracies ranging from 94.5% to 98.5% and Kappa statistics between 0.75 and 0.98. Forest was OPEN ACCESSRemote Sens. 2015, 7 2510 strongly fragmented due to the rapid expansion of the arable frontier and the even more rapid invasion by bracken. Unexpectedly, more bracken-infested areas were converted to pastures than vice versa, a practice that could alleviate pressure on forests if promoted. Road proximity was the most important spatial element determining forest loss, while for bracken the altitudinal range conditioned the degree of invasion in deforested areas. The annual deforestation rate changed notably between periods: ~1.5% from 1975 to 1987, ~0.8% from 1987 to 2000, and finally a very high rate of ~7.5% between 2000 and 2001. We explained these inconstant rates through some specific interrelated local and national political and socioeconomic drivers, namely land use policies, credit and tenure incentives, demography, and in particular, a severe national economic and bank crisis.

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Section: Quantification Of Land Cover Change and Habitat Fragmentationmentioning

confidence: 99%

“…We quantified land cover change by means of the post-classification inter-comparison technique, nowadays one of the most used change detection approaches [79]. It implies a pixel-by-pixel comparison of separately classified images where the same cell size is required.…”

Section: Quantification Of Land Cover Change and Habitat Fragmentationmentioning

confidence: 99%

Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

et al. 2015

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“…Spatial co-registration errors were estimated with an iterative two-step approach: (1) coarsening Landsat data to the MODIS grid cell size and (2) correlation. This process was repeated within a defined window displacing Landsat data by a specified interval in x-and y-direction, and the offset with the highest correlation coefficient indicates the displacement between both images [45]. In this study, the correlations are based on the NDVI from downloaded Landsat images and the closest available MODIS composite.…”

Section: Spatial Co-registrationmentioning

confidence: 99%

An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms

Colditz

2015

Remote Sensing

148

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Land cover mapping for large regions often employs satellite images of medium to coarse spatial resolution, which complicates mapping of discrete classes. Class memberships, which estimate the proportion of each class for every pixel, have been suggested as an alternative. This paper compares different strategies of training data allocation for discrete and continuous land cover mapping using classification and regression tree algorithms. In addition to measures of discrete and continuous map accuracy the correct estimation of the area is another important criteria. A subset of the 30 m national land cover dataset of 2006 (NLCD2006) of the United States was used as reference set to classify NADIR BRDF-adjusted surface reflectance time series of MODIS at 900 m spatial resolution. Results show that sampling of heterogeneous pixels and sample allocation according to the expected area of each class is best for classification trees. Regression trees for continuous land cover mapping should be trained with random allocation, and predictions should be normalized with a linear scaling function to correctly estimate the total area. From the tested algorithms random forest classification yields lower errors than boosted trees of C5.0, and Cubist shows higher accuracies than random forest regression.

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“…Instead of simply reassigning smaller members by the surrounding majorities as the way of MMUs [5], we use the self-adaptive morphology to depict the inter-member dependency for a more reliable reassigning. Based on the member-level spatial analysis, each misclassified dark grass member will be readjusted according to the inter-member dependency rule.…”

Section: Readjusting Misclassified Members Based On the Member-level mentioning

confidence: 99%

“…Remotely sensed imagery is an important source data available to characterize changes systematically and consistently [4]. Despite both frequent criticism and the availability of many alternatives, change detection from remotely sensed imagery still remains one of the most applied techniques due to its simplicity and intuitive manner [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale

Zhou

Qin

2014

Remote Sensing

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Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel-and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed.

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Potential effects in multi-resolution post-classification change detection

Cited by 35 publications

References 29 publications

Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

Land Cover Change in the Andes of Southern Ecuador—Patterns and Drivers

An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms

Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale

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