Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers. KEYWORDS automated feature extraction, landscape analysis, machine learning, object-based image analysis, pattern recognition, remote sensing

Despite decades of archaeological research, roughly 75% of Madagascar's land area remains archaeologically unexplored and the oldest sites on the island are difficult to locate, as they contain the ephemeral remains of mobile hunter/forager campsites. The known archaeological record is therefore biased toward later sites, especially sites dating to the second millennium AD, following the expansion of Indian Ocean trading networks. Systematic archaeological investigations are required to address these biases in the known archaeological record and clarify the island's early human history, but funding limitations, logistical and time constraints in surveying large areas and a relatively small number of active field archaeologists present substantial barriers to expansive areal survey coverage. Using theoretical models derived from human behavioral ecology (i.e., ideal free distribution, optimal foraging theory) in conjunction with freely available remote sensing data, we illustrate how archaeological survey of Madagascar's landscapes can be rapidly expanded, more effectively target early archaeological deposits, and address questions about the island's settlement. This study illustrates the potential for theoretically-driven satellite-based remote sensing analysis to improve our understanding of the archaeological record of the world's fourth largest island.

The study of precontact anthropogenic mounded features-earthen mounds, shell heaps, and shell rings-in the American Southeast is stymied by the spotty distribution of systematic surveys across the region. Many extant, yet unidentified, archaeological mound features continue to evade detection due to the heavily forested canopies that occupy large areas of the region, making pedestrian surveys difficult and preventing aerial observation. Object-based image analysis (OBIA) is a tool for analyzing light and radar (lidar) data and offers an inexpensive opportunity to address this challenge. Using publicly available lidar data from Beaufort County, South Carolina, and an OBIA approach that incorporates morphometric classification and statistical template matching, we systematically identify over 160 previously undetected mound features. This result improves our overall knowledge of settlement patterns by providing systematic knowledge about past landscapes.

Narratives of landscape degradation are often linked to unsustainable fire use by local communities. Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity viewed as threatened by unsustainable anthropogenic fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we contribute a comparative approach to determining relationships between regional fire regimes and global patterns and trends, applied to Madagascar using MODIS remote sensing data (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019). Rather than a global exception, we show that Madagascar's fire regimes are similar to 88% of tropical burned area with shared climate and vegetation characteristics, and can be considered a microcosm of most tropical fire regimes. From 2003-2019, landscapescale fire declined across tropical grassy biomes (17%-44% excluding Madagascar), and on Madagascar at a relatively fast rate (36%-46%). Thus, high tree loss anomalies on the island (1.25-4.77× the tropical average) were not explained by any general expansion of landscape-scale fire in grassy biomes. Rather, tree loss anomalies centred in forests, and could not be explained by landscape-scale fire escaping from savannas into forests. Unexpectedly, the highest tree loss anomalies on Madagascar (4.77×) occurred in environments without landscape-scale fire, where the role of small-scale fires (<21 h [0.21 km 2 ]) is unknown. While landscape-scale fire declined across tropical grassy biomes, trends in tropical forests reflected important differences among regions, indicating a need to better understand regional variation in the anthropogenic drivers of forest loss and fire risk. Our new understanding of Madagascar's fire regimes offers two lessons with global implications: first, landscape-scale fire is declining across tropical grassy biomes and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with tropical forest loss, indicating a need for socio-ecological context in framing new narratives of fire and ecosystem degradation. K E Y W O R D S anthropogenic fire, fire regimes, forest degradation, forest loss, global change, land use and land cover change, landscape degradation, vegetation change This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Key PointsQuestionWas the Centers for Medicare & Medicaid Services Oncology Care Model (OCM), an alternative payment model for cancer patients undergoing chemotherapy, associated with differences in Medicare spending, utilization, quality, and patient experience over the model’s first 3 years?FindingsIn this exploratory difference-in-differences study of Medicare fee-for-service beneficiaries with cancer undergoing chemotherapy (483 310 beneficiaries with 987 332 episodes treated at 201 OCM participating practices and 557 354 beneficiaries with 1 122 597 episodes treated at 534 comparison practices), OCM was associated with a statistically significant relative decrease in total episode payments of $297 that was not sufficient to cover the costs of care coordination or performance-based payments. There were no statistically significant differences in most measures of utilization, quality, or patient experiences.MeaningIn its first 3 years, the OCM was significantly associated with modestly lower Medicare episode payments that did not offset model payments to participating practices, and there were no significant differences in most utilization, quality, or patient experience outcomes.