This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 Today, validation or accuracy assessment is an integral component of most mapping projects incorporating remotely sensed data. Other spatial information may not be so stringently evaluated, but at least requires meta-data that documents how the information was generated. This emphasis on data quality was not always the case. In the 1970s only a few brave scientists and researchers dared ask the question, ‘How good is this map derived from Landsat MSS imagery?’ In the 1980s, the use of the error matrix became a common tool for representing the accuracy of individual map categories. By the 1990s, most maps derived from remotely sensed imagery were required to meet some minimum accuracy standard. A similar progression can be outlined for other spatial information. However, this progression is about 5 years behind the validation of remotely sensed data. This paper presents a series of steps moving towards better assessment and validation of spatial information and asks the reader to evaluate where they are in this series and to move forward.

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

Given the advances in remotely sensed imagery and associated technologies, several global land cover maps have been produced in recent times including IGBP DISCover, UMD Land Cover, Global Land Cover 2000 and GlobCover 2009. However, the utility of these maps for specific applications has often been hampered due to considerable amounts of uncertainties and inconsistencies. A thorough review of these global land cover projects including evaluating the sources of error and uncertainty is prudent and enlightening. Therefore, this paper describes our work in which we compared, summarized and conducted an uncertainty analysis of the four global land cover mapping projects using an error budget approach. The results showed that the classification scheme and the validation methodology had the highest error contribution and implementation priority. A comparison of the classification schemes showed that there are many inconsistencies between the definitions of the map classes. This is especially true for the mixed type classes for which thresholds vary for the attributes/discriminators used in the classification process. Examination of these four global mapping projects provided quite a few important lessons for the future global mapping projects including the need for clear and uniform definitions of the classification scheme and an efficient, practical, and valid design of the accuracy assessment.

Unmanned Aerial Systems (UAS) offer users the ability to capture large amounts of imagery at unprecedented spatial resolutions due to their flexible designs, low costs, automated workflows, and minimal technical knowledge barriers. Their rapid extension into new disciplines promotes the necessity to question and understand the implications of data capture and processing parameter decisions on the respective output completeness. This research provides a culmination of quantitative insight using an eBee Plus, fixed-wing UAS for collecting robust data on complex forest environments. These analyses differentiate from measures of accuracy, which were derived from positional comparison to other data sources, to instead guide applications of comprehensive coverage. Our results demonstrated the impacts of flying height on Structure from Motion (SfM) processing completeness, discrepancies in outputs based on software package choice, and the effects caused by processing parameter settings. For flying heights of 50 m, 100 m, and 120 m above the forest canopy, key quality indicators within the software demonstrated the superior performance of the 100-m flying height. These indicators included, among others, image alignment success, the average number of tie points per image, and planimetric model ground sampling distance. We also compared the output results of two leading SfM software packages: Agisoft PhotoScan and Pix4D Mapper Pro. Agisoft PhotoScan maintained an 11.8% greater image alignment success and a 9.91% finer planimetric model resolution. Lastly, we compared the “high” and “medium” resolution processing workflows in Agisoft PhotoScan. The high-resolution processing setting achieved a 371% increase in point cloud density, with a 3.1% coarser planimetric model resolution, over a considerably longer processing time. As UAS continue to expand their sphere of influence and develop technologically, best-use practices based on aerial photogrammetry principles must remain apparent to achieve optimal results.