@article {JRN55409,
	title = {Accuracy of regional-to-global soil maps for on-farm decision making: Are soil maps {\textquotedblleft}good enough{\textquotedblright}?},
	journal = {Egusphere},
	year = {2022},
	month = {5/19/2022},
	abstract = {A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6,514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1{\textendash}2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class showed that all soil maps had similar performance in estimating the correct property class, with overall accuracies ranging from 8{\textendash}39 \% for soil texture classes and 26{\textendash}33 \% for soil rock fragment classes. Furthermore, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps despite their similar overall accuracies. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modelling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 \% of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.},
	keywords = {accuracy, decision making, on-farm, regional-to-global, soil maps},
	doi = {10.5194/egusphere-2022-246},
	url = {files/bibliography/22-021.pdf},
	author = {Jonathan Maynard and Edward Yeboah and Stephen Owusu and M. Buenemann and J. Neff and Jeffrey E. Herrick}
}
@article {JRN53930,
	title = {Using very-large-scale aerial imagery for rangeland monitoring and assessment: some statistical considerations},
	journal = {Rangeland Ecology and Management},
	volume = {65},
	year = {2012},
	month = {07/01/2012},
	pages = {330-339},
	chapter = {330},
	abstract = {<p>The availability of very-high-resolution (VHR) imagery and techniques for processing those data into indicators of ecosystem function has opened the door for VHR imagery to be used in rangeland monitoring and assessment. However, VHR imagery can be expensive and, like any survey measurement, studies that rely on VHR-image-based indicators will be subject survey errors that lead to biased results or inflated standard errors. To yield defensible monitoring and assessment data for managers, indicators derived from VHR imagery must be implemented with a statistically valid sampling design. We discuss the use of VHR imagery from the perspectives of achieving unbiased estimates with minimal survey errors in the context of rangeland monitoring and assessment. Our discussion includes study design considerations such as the importance of probability-based sampling for generating credible estimates; measurement errors in VHR-derived indicators; and model-assisted estimation to integrate data from multiple sources, improve precision and adjust for sources of errors. We demonstrate the effects of these design considerations using example datasets where VHR imagery and concurrent field data were collected. Finally, we propose practical ways to conduct statistically-sound rangeland monitoring and assessment using VHR imagery.</p>
},
	keywords = {accuracy, monitoring, precision, sample design, statistics, very-large-scale aerial image},
	doi = {10.2111/REM-D-11-00102.1},
	url = {/files/bibliography/12-041.pdf},
	author = {Jason W. Karl and Danielle Duni and Nusser, Sarah and Opsomer, Jean and Unnasch, Robert S.}
}
@proceedings {50045,
	title = {Feature selection methods for object-based classification of sub-decimeter resolution digital aerial imagery},
	volume = {XXXVIII},
	year = {2010},
	pages = {4/C7},
	address = {Ghent, Belgium},
	abstract = {Due to the availability of numerous spectral, spatial, and contextual features, the determination of optimal features and class separabilities can be a time consuming process in object-based image analysis (OBIA). While several feature selection methods have been developed to assist OBIA, a robust comparison of the utility and efficiency of approaches could facilitate broader application. In this study, we tested feature selection methods and assessments of class separability for object-based classifications of arid land vegetation in the southwestern U.S. using sub-decimeter digital aerial imagery with a 6 cm ground resolved distance. Using Definiens Developer software, we evaluated 1) classification tree analysis (CTA), 2) feature space optimization (FSO), and 3) SEparability and THreshold (SEaTH). We assessed strengths, weaknesses and best uses for each approach using the criteria of ease of use, ability to rank and/or reduce input features, ability to obtain feature thresholds and class separation distances, and underlying statistical assumptions. We conclude that CTA is best suited for reducing a large number of input features, either for nearest neighbor classification or for deriving rules for rule-based classification. FSO is most appropriate for determining a suitable group of features relatively quickly, because it operates within the Definiens software. However, the features are not ranked and feature thresholds cannot be determined. SEaTH interfaces well with outputs from Definiens and is most appropriate for ranking input features based on class separation distances for two-class comparisons, but has the disadvantage of assuming normality, unlike the non-parametric CTA. All methods offered an objective approach for determining suitable features for classifications of hyperspatial aerial imagery. Limitations, assumptions, and appropriate uses for this and other datasets are discussed.},
	keywords = {accuracy, aerial photography, classification, feature selection, high resolution, object-based image analysis (OBIA), vegetation},
	url = {/files/bibliography/10-048.pdf},
	author = {Andrea S. Laliberte and Dawn M. Browning and A. Rango}
}
