## 4 Detecting Anthropogenic Disturbance Regimes  
Central to remote detection of disturbance is the concept that energy absorption and reflectance varies with the structural characteristics of land cover types. Remote detection of disturbance for vegetative cover such as forest relies on energy exchange properties in several key regions of the electromagnetic spectrum: (1) the absorption of visible energy; (2) the scattering of red and near-infrared energy at the \"red edge\"; (3) the absorption of short-wave infrared energy; and (4) the reflection of thermal energy [88, 89]. Property (1) is associated with leaf pigments, properties (2) and (3) are associated with cell structure, and property (4) is associated with the fractions of soil and vegetation in a pixel [89]. [PERSON] and [PERSON] [89] were among the first to recognize a disturbance trajectory by relating energy exchange to land cover types. The initial work on relating properties of energy exchange to land cover types pioneered a number of breakthroughs in disturbance detection specific to forested ecosystems. [PERSON] and colleagues [90] developed a disturbance index (DI) based on a linear combination of [PERSON] and [PERSON] [91] earlier tasseled cap transformation of Landsat-derived spectral reflectance. With the free public release of Landsat imagery in 2008 [92], and significant advancements in computing capabilities, every satellite observation in the Landsat archive is now being utilized for change detection and classification of land cover [93, 94]. Automated change detection has now been made possible by novel algorithms such as the object-based disturbance inventory framework [95, 96, 97], the vegetation change tracker (VCT) [98], and Landsat-based detection of trends in disturbance and recovery (LandTrendr) [99], which have been used to detect forest change at continental scales for the first time using Earth observations [100]. Such advancements in the discipline and computer processing have led to the first high-resolution map of recent forest cover changes for the entire planet [101] and near-real time monitoring of forest condition [102]. To date, there have been a number of studies focused on detecting, mapping, and monitoring forest change related to energy development. [PERSON] and colleagues [103] showed that coal mines could be extracted and mapped as features derived from endmember spectral mixture analysis using Landsat data with consistent and accurate results across three global study areas. [PERSON] and colleagues [104] characterized changes in the extent of surface mining and reclamation in Appalachich U.S. and observed that forest cover and active mines declined between 1976 and 2006 while reclaimed land increased over the same period. Such detection and mapping approaches can complement the disturbance inventory framework proposed by [PERSON] and [PERSON] [95, 96, 97], and are well-suited to multiple-use landscapes undergoing rapid energy development. Change detection at finer scales will be essential for contextualizing and attributing disturbance impacts to forest land uses. While there has been tremendous advancement in remote detection of disturbance and forest change using known spectral properties of vegetation, there has been less progress toward developing new methods for detecting novel types of change seen in the Anthropocene. [PERSON] and [PERSON] [14] methodically mapped the relationship between vegetation cover derived from MODIS and population density on a global scale to develop the anthromes concept. In the future, it will become necessary for research programs to detect and monitor change at finer spatial scales since the impacts of anthropogenic disturbance to forests are most commonly more localized and heterogeneous than a Landsat or MODIS pixel represents. Aerial photography is the oldest remote sensing technology that has resulted in the longest continuous record of Earth observation [105].