NALC/Mexico land-cover mapping results: Implications for assessing landscape condition

Lunetta, Ross S.; Álvarez, Román; Edmonds, Curtis M.; Lyon, J. G.; Elvidge, C. D.; Bonifaz, R.; García, Carlos M.

doi:10.1080/01431160110071888

Cited by 29 publications

(14 citation statements)

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“…However, using only two images can lead to suboptimal results, as similar land cover types can vary significantly during various stages of the natural growth seasonal cycle [2]. To address this problem it was proposed that the sample rate of medium resolution remote sensing data acquisitions should be high enough to ascertain change events from natural phenological cycles [3], [4]. The Moderate-resolution Imaging Spectroradiometer (MODIS) data product MCD43A4 (used in this study) uses daily Terra and Aqua satellite overpasses to produce a 500 meter resolution conglomerated image every 8 days, and as such offers a high temporal frequency, which makes it possible to effectively detect changes by using time-series analysis.…”

Section: Introductionmentioning

confidence: 99%

Using Page's Cumulative Sum Test on MODIS Time Series to Detect Land-Cover Changes

Grobler

Ackermann

Zyl

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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Abstract-Human settlement expansion is one of the most pervasive forms of land cover change in South Africa. The use of Page's Cumulative Sum Test is proposed as a method to detect new settlement developments in areas that were previously covered by natural vegetation using 500 m MODIS time series satellite data. The method is a sequential per pixel change alarm algorithm that can take into account positive detection delay, probability of detection and false alarm probability to construct a threshold. Simulated change data was generated to determine a threshold during a preliminary off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the Gauteng and Limpopo provinces of South Africa. The experimental results indicated that CUSUM performs better than band differencing in the before mentioned study areas.

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Section: Introductionmentioning

confidence: 99%

Using Page's Cumulative Sum Test on MODIS Time Series to Detect Land-Cover Changes

Grobler

Ackermann

Zyl

et al. 2013

IEEE Geosci. Remote Sensing Lett.

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“…For moderate resolution imagery, such as MODIS, the use of pixel-based methods is problematic [32], due to the large spatial variability and ultimately the mapping of mixed pixels that is mixed classes [30,57]. Therefore, we accounted for the area-proportion of each class in the respective MODIS cell and also represented these proportions in the error matrix.…”

Section: Accuracy Assessment Resultsmentioning

confidence: 99%

Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables

et al. 2010

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Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km 2 large study area in western Burkina Faso and southern Mali (West Africa).A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class "Wetland") is 9,350 km 2 , corresponding to 4.3% of the total study area size. The classes OPEN ACCESSRemote Sens. 2010, 2 1752"No wetland"/"Wetland" could be separated with very high certainty; the overall agreement (KHAT) was 84.2% (0.67) and 97.9% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.

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“…However, such a comparison of only two images is not always reliable, as similar land cover types can appear significantly different at various stages of the natural growth seasonal cycle [5]. To mitigate this problem it was shown in [6] and [7] that the temporal frequency of medium resolution remote sensing data acquisitions should be high enough to distinguish change events from natural phenological cycles. The Moderate-resolution Imaging Spectroradiometer (MODIS) data product used in this study utilizes daily Terra and Aqua satellite overpasses to produce a 500 m resolution composite image every 8 days [8], and as such offers a high enough temporal frequency of the remote sensing data for change detection through time-series analysis [9].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Using Autocorrelation Analysis on MODIS Time-Series Data: Detection of New Human Settlements in the Gauteng Province of South Africa

Kleynhans

Salmon

Olivier

et al. 2012

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Human settlement expansion is one of the most pervasive forms of land cover change in the Gauteng province of South Africa. A method for detecting new settlement developments in areas that are typically covered by natural vegetation using 500 m MODIS time-series satellite data is proposed. The method is a per pixel change alarm that uses the temporal autocorrelation to infer a change index which yields a change or no-change decision after thresholding. Simulated change data was generated and used to determine a threshold during an off-line optimization phase. After optimization the method was evaluated on examples of known land cover change in the study area and experimental results indicate a 92% change detection accuracy with a 15% false alarm rate. The method shows good performance when compared to a traditional NDVI differencing method that achieved a 75% change detection accuracy with a 24% false alarm rate for the same study area.

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NALC/Mexico land-cover mapping results: Implications for assessing landscape condition

Cited by 29 publications

References 0 publications

Using Page's Cumulative Sum Test on MODIS Time Series to Detect Land-Cover Changes

Using Page's Cumulative Sum Test on MODIS Time Series to Detect Land-Cover Changes

Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables

Land Cover Change Detection Using Autocorrelation Analysis on MODIS Time-Series Data: Detection of New Human Settlements in the Gauteng Province of South Africa

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