Mapping erosion in Zimbabwe: a methodology for rapid survey using aerial photographs

Whitlow, Richard

doi:10.1016/0143-6228(86)90016-0

Cited by 14 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although Landsat 8 demonstrated moderately poor quality (i.e. poor radiometric, spatial, spectral characteristics), particularly for detecting spatial occurrence of soil erosion, it holds a good record, especially large-scale mapping, as it is acknowledged by researcher on similar studies (Millington and Townshend, 1984;Whitlow, 1986;Vrieling, 2006;Zhou et al, 2008;Taruvinga, 2009;Seutloali et al, 2017;Dube et al, 2017). Based on the digital classification of Landsat thematic mapper and JERS-1 data, the study by Metternicht and Zinck (1998) detected and mapped different soil erosion feature in Bolivia and concluded that the synergy of Landsat TM provides a unique combination that allows more accurate identification of eroded areas.…”

Section: Discussionmentioning

confidence: 99%

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

Sepuru

Dube

2018

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

In this study, we determine the most suitable multispectral sensor that can accurately detect and map eroded areas from other land cover types in Sekhukhune rural district, Limpopo Province, South Africa. Specifically, the study tested the ability of multi-date (wet and dry season) Landsat 8 OLI and Sentinel-2 MSI images in detecting and mapping eroded areas. The implementation was done, using a robust non-parametric classification ensemble: Discriminant Analysis (DA). Three sets of analysis were applied (Analysis 1: Spectral bands as independent dataset; Analysis 2: Spectral vegetation indices as independent and Analysis 3: Combined spectral bands and spectral vegetation indices). Overall classification accuracies ranging between 80% to 81.90% for MSI and 75.71%-80.95% for OLI were derived for the wet and dry season, respectively. The integration of spectral bands and spectral vegetation indices showed that Sentinel-2 (OA = 83, 81%), slightly performed better than Landsat 8, with 82, 86%. The use of bands and vegetation indices as independent dataset resulted in slightly weaker results for both sensors. Sentinel-2 MSI bands located in the NIR (0.785-0.900 μm), red edge (0.698-0.785 μm) and SWIR (1.565-2.280 μm) regions were selected as the most optimal for discriminating degraded soils from other land cover types. However, for Landsat 8OLI, only the SWIR (1.560-2.300 μm), NIR (0.845-0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, NDVI and SAVI and SAVI and Global Environmental Monitoring Index (GEMI) were ranked selected as the most suitable for detecting and mapping soil erosion. Additionally, SRTM DEM derived information illustrates that for both sensors eroded areas occur on sites that are 600 m and 900 m of altitude with similar trends observed in both dry and wet season maps. Findings of this work emphasize the importance of free and readily available new generation sensors in continuous landscape-scale soil erosion monitoring. Besides, such information can help to identify hotspots and potentially vulnerable areas, as well as aid in developing possible control and mitigation measures.

show abstract

Section: Discussionmentioning

confidence: 99%

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

Sepuru

Dube

2018

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

show abstract

“…From supervised Landsat image classification, we were able to map 14 cover types in the MGR, which necessitated the use of techniques capable of yielding a comparable number of information classes from CORONA photographs. Initially guided by a preliminary exploration of what is available in the literature (Watson 1995, Whitlow 1986, Rossouw 1997, Garland and Broderick 1992, vector onscreen digitization in Erdas Imagine was tried first. Apart from being time consuming, this procedure yielded outputs that could not be meaningfully compared with their counterparts from Landsat imagery mainly because of difficulties in digitizing fragmented cover types below the minimum mappable threshold.…”

Section: Reference Area and Rationale For Improvisation Of Proposed Tmentioning

confidence: 99%

The use of step‐wise density slicing in classifying high‐resolution panchromatic photographs

Hamandawana

Eckardt

Ringrose

2006

International Journal of Remote Sensing

View full text Add to dashboard Cite

The present paper provides an improvised, simple and easy-to-follow methodology in which step-wise density slicing was used to classify high-resolution panchromatic photographs to levels of spatial detail and accuracy that offer end products comparable with what is obtainable from conventional signature-based classification of multispectral images. By tapping on a range of manipulative remote sensing and GIS techniques, we provide an illustrated example that takes the reader from on-screen image display to final compilation and accuracy assessment of a classified map output. Comprising digital classification by image segmentation and visually-guided grey scale coding, the methodology we offer was practically tested in a change detection investigation around the Okavango Delta in northern Botswana. We conveniently selected one site from this broad study and sampled results over four time-slices for illustrative purposes. With this improvisation giving results with an accuracy level of 69%, we suggest that this methodology may be used for detailed classification of high-resolution panchromatic images.

show abstract

Potential versus actual erosion in Zimbabwe

Whitlow

1988

Applied Geography

View full text Add to dashboard Cite

Mapping erosion in Zimbabwe: a methodology for rapid survey using aerial photographs

Cited by 14 publications

References 3 publications

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

The use of step‐wise density slicing in classifying high‐resolution panchromatic photographs

Potential versus actual erosion in Zimbabwe

Contact Info

Product

Resources

About