Remote Sensing

Earth Observation Data to support environmental justice; Linking non-permitted Poultry Operations to environmental vulnerability indices

Industrial agriculture disproportionately affects minority, low-income, and Tribal communities, propagating environmental injustice. Concentrated Animal Feeding Operations (CAFOs) apply massive amounts of untreated waste to nearby farmlands. Even though the environmental health impacts of CAFOs are well documented, most studies rely almost exclusively on known CAFO locations from public records, which are incomplete.

Building Trust in Reidsville GA Through Residential Green Infrastructure

Reidsville GA Community Floods, a small community group, seeks to prevent residential flooding in Reidsville, GA. This community science project will help residents understand their flood risks and the potential for green infrastructure as a step toward this greater goal. In parallel with the group’s existing partnerships that are investigating county-level flooding, the outcomes of this project are to 1) create a map that outlines flood risk in the community 2) better understand the potential for green infrastructure, including replicable green infrastructure on private property.

Forest water use is increasingly decoupled from water availability even during severe drought

Key to understanding forest water balances is the role of tree species regulating evapotranspiration (ET), but the synergistic impact of forest species composition, topography, and water availability on ET and how this shapes drought sensitivity …

In-season wheat sown area mapping for Afghanistan using high resolution optical and RADAR images in cloud platform

Afghanistan has only 11% of arable land while wheat is the major crop with 80% of total cereal planted area. The production of wheat is therefore highly critical to the food security of the country with population of 35 million among which 30% are …

Evaluating static and dynamic landscape connectivity modelling using a 25-year remote sensing time series

Despite calls for landscape connectivity research to account for spatiotemporal dynamics, studies have overwhelmingly evaluated the importance of habitats for connectivity at single or limited moments in time. Remote sensing time series represent a promising resource for studying connectivity within dynamic ecosystems. However, there is a critical need to assess how static and dynamic landscape connectivity modelling approaches compare for prioritising habitats for conservation within dynamic environments.

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop …

Remotely sensed forest cover loss shows high spatial and temporal variation across Sumatera and Kalimantan, Indonesia 2000–2008

The Indonesian islands of Sumatera and Kalimantan (the Indonesian part of the island of Borneo) are a center of significant and rapid forest cover loss in the humid tropics with implications for carbon dynamics, biodiversity conservation, and local …

Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery

A desirable feature of a global sampling design for estimating forest cover change based on satellite imagery is the ability to adapt the design to obtain precise regional estimates, where a region may be a country, state, province, or conservation …

Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided …

A comparison of sampling designs for estimating deforestation from Landsatimagery: A case study of the Brazilian Legal Amazon

Three sampling designs —simple random, stratified random, and systematic sampling —are compared onthe basis of precision of estimated loss of intact humid tropical forest area in the Brazilian Legal Amazon from 2000 to 2005. The results of this case study demonstrate the utility of a stratified design based on MODIS-derived deforestationdata to improve precision of the estimated loss of intact forest area as estimated from sampling Landsat imagery.