Historical trends of degradation,loss,and recovery in the tropical forest reserves of Ghana

The Upper Guinean Forest region of West Africa,a globally significant biodiversity hotspot,is among the driest and most human-impacted tropical ecosystems.We used Landsat to study forest degradation,loss,and recovery in the forest reserves of Ghana from 2003 to 2019.Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm.Canopy cover was predicted with a predicted-observed r2 of 0.76,mean absolute error of 12.8%,and mean error of 1.3%.Forest degradation,loss,and recovery were identified as transitions between closed(>60%cover),open(15–60%cover)and low tree cover(<15%cover)classes.Change was relatively slow from 2003 to 2015,but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests.In 2016,widespread fires associated with El Niño drought caused forest loss and degradation across more than 12%of the moist semi-deciduous and upland evergreen forest types.The workflow was implemented in Google Earth Engine,allowing stakeholders to visualize the results and download summaries.Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience.

21世纪全球新建水库编目:丰度、热点区及总蓄水量

Dams and reservoirs play an essential role in regulating and managing water resources.Since the middle of the 20th century,the growing demand for water and hydropower has led to an unprecedented boom in reservoir construction worldwide[1,2].Meanwhile,reservoir construction has also resulted in a variety of ecological and socioeconomic impacts[3–5].Reservoirs transform natural flow regimes into conditions favored by human demand.The associated flow regulations,especially in reservoirs constructed in recent decades(e.g.,after 2000)with greater seasonal variability[6,7],represent a strong human-induced alteration of the hydrologic cycle.As reservoir construction continues to boom in many parts of the world,an up-to-date and openaccess inventory of reservoirs worldwide remains critically desired.

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Crop phenology is critical for agricultural management,crop yield estimation,and agroecosystem assessment.Traditionally,crop growth stages are observed from the ground,which is time-consuming and lacks spatial variability.Remote sensing Vegetation Index(VI)time series has been used to map land surface phenology(LSP)and relate to crop growth stages mostly after the growing season.In recent years,high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season.This paper summarizes two classes of near-real-time mapping methods,i.e.,curve-based and trend-based approaches.The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages.The curve-based approaches are capable of a shortterm prediction.The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds.The trend-based approaches only use current observations.Both curve-based and trend-based approaches are promising in mapping crop growth stages timely.Nevertheless,mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages.The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season.Recent satellite datasets such as the harmonized Landsat and Sentinel-2(HLS)are promising for mapping crop phenology within the season over large areas.Operational applications in the near future are feasible.

Space‑Time Clustering with the Space‑Time Permutation Model in SaTScan™ Applied to Building Permit Data Following the 2011 Joplin, Missouri Tornado

Community recovery from a major natural hazard-related disaster can be a long process, and rebuilding likely does not occur uniformly across space and time. Spatial and temporal clustering may be evident in certain data types that can be used to frame the progress of recovery following a disaster. Publically available building permit data from the city of Joplin, Missouri, were gathered for four permit types, including residential, commercial, roof repair, and demolition. The data were used to(1) compare the observed versus expected frequency(chi-square) of permit issuance before and after the EF5 2011 tornado;(2), determine if significant space-time clusters of permits existed using the SaTScan^(TM) cluster analysis program(version 9.7);and(3) fit any emergent cluster data to the widely-cited Kates 10-year recovery model. All permit types showed significant increases in issuance for at least 5 years following the event,and one(residential) showed significance for nine of the 10years. The cluster analysis revealed a total of 16 significant clusters across the 2011 damage area. The results of fitting the significant cluster data to the Kates model revealed that those data closely followed the model, with some variation in the residential permit data path.