Spatiotemporal Drivers of Fine-Scale Forest Plantation Establishment in VillageBased Economies of Andhra Pradesh

The Indian state of Andhra Pradesh, our study area, has decreased in overall forest cover in recent years, with concomitant (though not commensurate) increases in forest plantation area, largely through conversion of degraded and existing agricultural land. Unfortunately, however, accurately mapping forest plantations in India using remotely- sensed data has been difficult because: (1) many plantations are small relative to moderate resolution earth resource satellite data, (2) newly established plantations are very difficult to identify, and (3) the surrounding cropland area is very variegated in both time and space. A concomitant socio-economic issue is the relatively unknown incentives and risks associated with establishing plantations within the broader land use context at the decision maker level in this region. Leveraging forest industry partnerships and strong extant approaches pioneered by our team, our overall goals are to (1) improve the accuracy and precision by which forest degradation and plantation establishment can be remotely-sensed using data from ResourceSat-1, Landsat, SPOT, and/or RapidEye; and (2) determine the most predictive local, regional, village, and household based drivers of plantation forest establishment (the social science aspect of the proposed study). Our proposed research is particularly responsive to the solicitation, as it (1) couples remote sensing observations (using both NASA and synergistic non-NASA assets) from which land-cover can be derived with research on the human dimensions of land-use change, (2) is explicitly focused on the South Asia geographic region of interest, (3) improves the detection, monitoring, and predictions of land cover and land use change in the region, (4) attributes land cover and land use changes to their primary causes, and (5) provides a formal means and rigorous method for evaluating the socially best land use mix over time as the region develops. The primary expected outcomes are as follows: (1) improvements to algorithms from which both discrete and continuous land use and land cover change variables can be remotely-estimated in this tropical, fine-scale, temporally dynamic, and spectrally variegated landscape, and (2) an empirical realization of forest plantation establishment in village-based economies where smallholders establish forest plantations on previously degraded lands for both timber and non-timber use, using a unique data set developed over time and space through the period covered by the proposal. Deliverables will include, but not be limited to, the following: (1) a map of plantations, natural forests, degraded forests, and primary non-forest land uses in East and West Godavari, (2) a system of equations resulting from the econometric analysis, with a rent function describing the net returns from each land use option for a household, and a response model of the decision to establish forest plantations on existing and new land as a competing land use with other uses such as agriculture and grazing. Our tentative schedule is as follows: Year 1: (1) develop econometrics survey instrument, (2) use high-resolution remotely-sensed data in concert with in situ reference data to (a) develop tree canopy cover dependent variable distributions (b) map plantations and other key land uses in East and West Godavari circa 2016, (3) use resulting map and ancillary data to develop sampling strata for survey, (4) distribute and collect survey instruments. Year 2: (1) refine tree canopy cover (both static and dynamic) estimation approaches using Landsat data, (2) analyze survey data; develop econometric models. Year 3: (1) map canopy cover and change in the region, (2) assess how property rights risks and future market opportunities for competing uses affect forest plantation establishment, (3) quantify the errors associated with non-integrated land use change modeling. Overall, our study has strong potential to help enrich LCLUC science in South Asia.