UAV and Satellite-Based Sensing to Map Ecological States at the Landscape Scale

Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.

Ecological evaluation of marine macroalgal communities on five islands of Korea in the Yellow Sea

Macroalgae have long been used as biological indicators of marine ecosystem health worldwide due to their ecological importance and sensitivity to environmental stress.A number of previous studies have utilized macroalgal communities in monitoring surveys of environmental conditions.This study examined the characteristics and patterns of marine macroalgal communities in the Yellow Sea off the western coast of Korea.Macroalgae were analyzed for the number of species,biomass,and coverage ratio by macroalgal type.During the study period,82 macroalgal species(10 green algae,17 brown algae,and 55 red algae)were identified at the five study sites,with the highest number of species found at Gwanrido and Uido(both containing 41 species)and the lowest at Daeijakdo(27 species).The average biomass(via dry weight)was 98.63 g/m^(2),consisting of green algae(8.39 g/m^(2)),brown algae(35.08 g/m^(2)),and red algae(55.16 g/m^(2)).The dominant macroalgae species in terms of biomass were Corallina pilulifera,Sargassum thunbergii,and Ulva australis in the intertidal zones,and Botryocladia wrightii and Gelidium elegans in the subtidal zones.Richness,evenness,and diversity indices based on the biomass of abundant species were 5.08,0.65,and 2.30,respectively,over the entire study area.Based on the evaluation of the environmental states by the community indices,overall,the Ecological Evaluation Index of macroalgae communities in the study area was marked as“Good-Moderate”,but was determined as“ModerateLow”at several sites during summer.The results can be a direct approach in the assessment of coastal habitats in which anthropogenic as well as climate change influences persist.

The Analysis of NGOs' Organizational Ecosystem in Participating in Climate Change Issues in the United States

How many NGOs were there in the climate change field in the United States？ Who were they？ And what was the relationship between them？ In the United States,climate change was only a branch of environmental issues at the beginning; therefore there were no specific quantitative statistics and network analysis of NGOs in climate change. Diversity was an important feature of NGOs in American climate change fields,and it showed a complex organizational ecology through varieties of networks and interaction among organizations. In order to make a difference in climate field,Chinese NGOs firstly needed to improve and enhance their own abilities.

State Key Laboratory of Xinjiang Institute of Ecology and Geography was successfully founded

Recently, State Key Laboratory of Desert and Oasis Ecology of Xinjiang Institute of Ecology and Geography （XIEG）, Chinese Academy of Sciences, was approved by Ministry of Science and Technology of the People＇s Republic of China to be founded.

Research on Block Storage and Analysis Model for Belt and Road Initiative Ecological Carrying Capacity Evaluation System

Assessment and analysis of ecological carrying capacity are significant issues in regional sustainable development.Large-scale ecological carrying capacity research consume a lot of time and labor costs.In this paper,considering redundant storage,on-demand computing,and multi-scale representation,a block storage and analysis model(BSAM)is proposed to make computation faster.Taking“The Belt and Road Initiative”as an example,the ecological carrying capacity evaluation system(ECCES)was developed.The results show that the problems of low calculation efficiency and high time cost were effectively solved with improved data storage,analysis,and expression approaches used by the ECCES.And the calculation speed is improved with the BSAM.Moreover,in the BSAM,the complexity of the original data is under a more significant impact on computing speed.In contrast,the complexity of an algorithm has a smaller influence on computing speed.Furthermore,the early warning push function and automated report function can generate the ecological carrying capacity states or changes automatically,which can improve the real-time,usability,and convenience of the ecological carrying capacity assessment.

Urbanization effects on the river systems in the Bucharest City region(Romania)

This article identifies and analyzes the effects of human pressures on the river systems,landscape,flow regime,and water quality in the Bucharest region,the largest urbanized area in Romania.The analyses focused on four streams crossing the Bucharest region,namely the Dâmboviţa,Colentina,Argeş,and Sabar rivers.Our approach relied especially on three types of information:(1)spatial data;(2)hydrological data sets;and(3)water quality data.We made a diachronic analysis of the available maps and ran classic statistical analysis of the data sets,as well as trend analysis.At the same time,we compared the flows in natural(reconstituted)and modified(current)regimes.The results showed that the stream system and its associated landscapes have considerably changed due to several engineering works(reservoirs,dams,channelization works,diversion canals,water intakes,etc.).Under these circumstances,the flow regime suffered changes that differed among the rivers.Thus,the multiannual discharges of the Argeşand Dâmboviţa rivers did not exhibit significant changes,but only mitigated the monthly discharge variability.In the case of Sabar and Colentina,a significant increase of the annual and monthly discharges was identified,due to the water transferred from the neighboring rivers.Water quality worsened,especially on the lower courses of the Dâmboviţa and Argeşrivers,degrading the states and health of the aquatic ecosystems in the study region.