Showing papers by "Christopher O. Justice published in 2021"
19 May 2021
27 citations
TL;DR: In this paper, the effect of political transition and subsequent timber bans on forest loss in Myanmar, in the context of identified drivers, was analyzed using Cook's distance (CD) to measure the effect.
Abstract: This study addresses the effect of political transition and subsequent timber bans on forest loss in Myanmar, in the context of identified drivers. Cook’s Distance (CD) was applied to remotely sensed time-series forest loss dataset to measure the effect of the events. Forest loss derived fragmentation metrics were linked to drivers at a landscape scale. Results show that at the national level, the political transition in 2011 had maximum effect (CD 0.935) on forest loss while the timber bans decreased forest loss by 612.04 km2 and 213.15 km2 in 2015 and 2017 (CD 0.146 and 0.035), respectively. The effect of the events varied for different States/Regions. The dominant drivers of change shifted from plantations in 2011 to infrastructure development in 2015. This study demonstrates the effects of policy on forest loss at various scales and can inform decision-makers for forest conservation, planning and development of mitigation measures.
18 citations
TL;DR: In this paper, the authors presented the Agriculture Remotely-sensed Yield Algorithm (ARYA) an EO-based method, advancing the state of EO data application and usage to forecast wheat yield.
7 citations
TL;DR: An innovative program focused on collaboration and capacity building is looking to improve outcomes for smallholder farmers, reduce hunger, and alleviate food insecurity in sub-Saharan Africa.
Abstract: An innovative program focused on collaboration and capacity building is looking to improve outcomes for smallholder farmers, reduce hunger, and alleviate food insecurity in sub-Saharan Africa.
3 citations
11 Jul 2021
TL;DR: In this paper, the authors presented a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), adapted to Franch et al. (2019) model.
Abstract: In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.