From Pixels to Mobile Pastoral Households: Scale Mismatch, Exposure Measurement, and Inference in Mongolian Rangeland Research
Abstract
Spatially explicit Earth-observation products and machine-learning models increasingly support detailed monitoring of vegetation productivity and land-surface dynamics across Mongolia. Yet the spatial unit observed by a satellite, the area actually grazed by a mobile herd, the administrative unit used in official statistics, and the household unit used in socioeconomic surveys are rarely identical. This methodological study formalizes that scale mismatch as a source of exposure-measurement and inferential error in linked rangeland–livelihood research. It develops a pixel-to-household framework that separates five objects: remotely sensed environmental signals, ecological interpretation, seasonal grazing locations, mobility-weighted household exposure, and livestock or household outcomes. The framework identifies four recurrent mismatches—pixel-to-pasture, residence-to-grazing-area, temporal aggregation, and administrative-boundary mismatch—and specifies alternative procedures for constructing exposure weights using GPS tracks, participatory mapping, seasonal-location surveys, livestock unit weighting, and probabilistic exposure surfaces. Using Mongolian rangeland research as the application domain, the study shows why strong predictive agreement with a remotely sensed productivity proxy does not by itself establish household-level exposure or causality. The resulting design standards emphasize explicit spatial and temporal support, structured validation, exposure uncertainty, and sensitivity to alternative linkage rules. The framework is intended as a technical design tool for remote-sensing, ecological, livestock, and socioeconomic studies in mobile pastoral systems
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BibTeX
@misc{ganbat2026from,
title = {{From Pixels to Mobile Pastoral Households: Scale Mismatch, Exposure Measurement, and Inference in Mongolian Rangeland Research}},
author = {Lkhamdulam Ganbat and Uyanga Ganbat},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5rz2n},
url = {https://eartharxiv.org/repository/view/15283/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From Pixels to Mobile Pastoral Households: Scale Mismatch, Exposure Measurement, and Inference in Mongolian Rangeland Research
AU - Lkhamdulam Ganbat
AU - Uyanga Ganbat
PY - 2026
DA - 2026/09/30
PB - EarthArXiv
DO - 10.31223/x5rz2n
UR - https://eartharxiv.org/repository/view/15283/
AB - Spatially explicit Earth-observation products and machine-learning models increasingly support detailed monitoring of vegetation productivity and land-surface dynamics across Mongolia. Yet the spatial unit observed by a satellite, the area actually grazed by a mobile herd, the administrative unit used in official statistics, and the household unit used in socioeconomic surveys are rarely identical. This methodological study formalizes that scale mismatch as a source of exposure-measurement and inferential error in linked rangeland–livelihood research. It develops a pixel-to-household framework that separates five objects: remotely sensed environmental signals, ecological interpretation, seasonal grazing locations, mobility-weighted household exposure, and livestock or household outcomes. The framework identifies four recurrent mismatches—pixel-to-pasture, residence-to-grazing-area, temporal aggregation, and administrative-boundary mismatch—and specifies alternative procedures for constructing exposure weights using GPS tracks, participatory mapping, seasonal-location surveys, livestock unit weighting, and probabilistic exposure surfaces. Using Mongolian rangeland research as the application domain, the study shows why strong predictive agreement with a remotely sensed productivity proxy does not by itself establish household-level exposure or causality. The resulting design standards emphasize explicit spatial and temporal support, structured validation, exposure uncertainty, and sensitivity to alternative linkage rules. The framework is intended as a technical design tool for remote-sensing, ecological, livestock, and socioeconomic studies in mobile pastoral systems
ER -