The use of digitally interpreted satellite imagery, with special reference to topographical shadow effects, as an aid to vegetation mapping in the Hottentots Holland Mountain catchment area of the Western Cape Province

This report includes a summary account of the operation of the LANDSAT 2 satellite and describes some of the procedures for extracting information, relating to vegetation, from digital imagery. The effect of topographic shadow on the imagery is explained and a short discussion of mountain fynbos, the natural vegetation of the mountains of the Western Cape Province of South Africa, is included. The report explains the methods used to estimate the amount of shadow on the imagery of the study area and to investigate the potential of various spectral band ratios for giving useful management information. It was found that approximately seventeen percent of the image of the Hottentots Holland Mountain Catchment Area is affected by topographic shadow. No meaningful information could be extracted from the shadowed areas, by digital image processing. Band ratioing did however, result in strong correlations between spectral values and vegetation height, percentage cover and biomass, as well as leaf surface area, veld condition and aspect, for sun illuminated areas

strengthening agricultural decisions in countries at risk of food insecurity the geoglam crop monitor for early warning

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National-scale cropland mapping based on spectral-temporal features and outdated land cover information

<div><p>The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring.</p></div

Agricultural regions of South Africa and provincial breakdown.

<p>Commercial grain-growing areas are predominantly located in the Western Cape province and in the maize quadrangle (North West and Free State provinces). Subsistence farming mostly occurs in the North West and the Eastern Cape provinces.</p

Critical distance diagram.

<p>The top line in the diagram is the axis along which the average rank of each spectral-temporal feature is plotted, from the lowest ranks (most important) on the left to the highest ranks (least important) on the right. Groups of features that are not statistically different from one another are connected. The critical difference (CD) is shown above the graph.</p

Selected zooms over four contrasted sites at the 1:200,000 scale.

<p>Left-hand side images for the four subsets provide a synoptic view of South African Cropland as well as its accuracy since areas in red represents omission errors, and areas in blue commission errors. Right-hand side images are false color composites of the maximum NDVI Landsat feature (maxNDVI.swir1, maxNDVI.nir, maxNDVI.red).</p

Spatially constrained accuracy assessment for the three accuracy measures.

<p>Cold spots of overall accuracy and F-score for the non-cropland class occur where the crop proportion is low. Hot spots of F-score for the cropland class are in intensive grain-growing regions and irrigated areas. Note the different color scales.</p

Accuracy measures for different post-filtering scenarios.

<p>The overall accuracy (OA) is given with the standard deviation (SD) of its estimation. Weighted majority filter performs better than the conventional majority filter and no filtering. Discarding edge pixels increased the accuracy, highlighting the difficulty to classify boundary (mixed) pixels and a less than perfect co-registration between the field boundary data set and the Landsat data.</p

Flowchart of the proposed classification scheme to derive spatially consistent national-scale cropland maps based on outdated land cover information and spectral-temporal features.

<p>It highlights the four main steps of the procedure: 1) extraction of the spectral-temporal features, 2) stratum-specific classification, 3) fusion based on class memberships, and 4) speckle removal.</p

Updated cropland map of South Africa for the 2013-2015 period.

<p>Fig 4a) illustrates the national-scale cropland map and Fig 4b) shows the corresponding pixel-level confidence map. The red points are the locations of the four zooms of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0181911#pone.0181911.g005" target="_blank">Fig 5</a>.</p