Spatial-temporal pattern of change in production-living-ecological space of Nanchong City from 2000 to 2020 and underlying factors.

This study analyzed the spatial-temporal change pattern and underlying factors in production-living-ecological space (PLES) of Nanchong City, China, over the past 20 years using historical land use data (2000, 2010, 2020). A land use transfer matrix was calculated from the historical land use maps, and spatial analysis was conducted to analyze changes in the land use dynamics degree, standard deviation ellipse, and center of gravity. The results showed that there was a rapid spatial evolution of the PLES in Nanchong from 2000 to 2010, followed by a stabilization in the second decade. The transfer of ecological-production space occurred mainly in the Jialing and Yilong River basins, while the reduction of production space and the increase of living space were most prominent in the intersection of three districts (Shunqing, Jialing, and Gaoping districts). The return of production-ecological space was observed in the south and northeast of Yingshan, and there was little notable transfer of other types. The distribution of production space in Nanchong evolved in a north-south to east-west trend, with the center of gravity moving from Yilong to Peng'an County. The living space and production space expanded in a north-south direction, and the center of gravity position was in Nanbu, indicating a more balanced growth or decrease in the last 20 years. The changes in the spatial-temporal pattern of PLES in Nanchong were attributed to the intertwined factors of national policies, economic development, population growth, and the natural environment. This study introduced a novel approach towards rational planning of land resources in Nanchong, which may facilitate more sustainable urban planning and development.

Contributions of climate change and human activities to grassland degradation and improvement from 2001 to 2020 in Zhaosu County, China.

Grassland degradation poses a serious threat to biodiversity, ecosystem services, and human well-being. In this study, we investigated grassland degradation in Zhaosu County, China, between 2001 and 2020, and analyzed the impacts of climate change and human activities using the Miami model. The actual net primary productivity (ANPP) obtained with CASA (Carnegie-Ames-Stanford Approach) modeling, showed a decreasing trend, reflecting the significant degradation that the grasslands in Zhaosu County have experienced in the past 20 years. Grassland degradation was found to be highest in 2018, while the degraded area continuously decreased in the last 3 years (2018-2020). Climatic factors for found to be the dominant factor affecting grassland degradation, particularly the decrease in precipitation. On the other hand, human activities were found to be the main factor affecting improvement of grasslands, especially in recent years. This finding profoundly elucidates the underlying causes of grassland degradation and improvement and helps implement ecological conservation and restoration measures. From a practical perspective, the research results provide an important reference for the formulation of policies and management strategies for sustainable land use.

Exploring Indigenous and Local Knowledge and Practices (ILKPs) in Traditional Jhum Cultivation for Localizing Sustainable Development Goals (SDGs): A Case Study from Zunheboto District of Nagaland, India.

Globally, shifting cultivation is known to be an important driver of tropical deforestation. However, in this paper, we argue that it can be sustainably managed if the environmental boundary conditions, laid by the traditional customs and practices, are fully respected. We narrate an empirical study from the Zunheboto district of Nagaland, India, where we deployed a mixed research method to explore the Indigenous and Local Knowledge and Practices (ILKPs) associated with shifting cultivation (aka Jhum), particularly concerning farm-level practices, forest and biodiversity conservation, and disaster risk reduction measures. The research method included analysis of primary data obtained through Focus Group discussions (FGDs), key informant interviews (n = 21), and a questionnaire survey (n = 153) with Jhum farmers from two different age groups, i.e., below 50 years (middle-aged farmers) and above 50 years (older farmers). From the qualitative inquiry, we identified 15 ILKPs, which were then validated from survey responses. We used the Mann-Whitney U test to examine differences in agreement between two groups of framers. Based on this analysis, we conclude that upholding of the ILKPs holds strong potential for the local implementation of several Sustainable Development Goals (SDGs), particularly, SDG-1(No poverty), SDG-2 (Zero hunger), and SDG-15 (Life on land). However, eight of the identified ILKPs showed a statistically significant difference between older and middle-aged farmers, underlining a declining trend. Finally, we suggest suitable policy measures to mainstream ILKPs to balance the trade-offs in food production and biodiversity conservation, and to ensure the future sustainability of Jhum cultivation in the region and beyond.

Rethinking forest monitoring for more meaningful global forest landscape change assessments.

Forest ecosystems play an indispensable role in addressing various pressing sustainability and social-ecological challenges such as climate change, biodiversity loss, and ecosystem services deterioration, hence the monitoring of the world's forests is crucial. As part of the global forest assessment workflow, a forest is generally classified and mapped based on land use and/or using a tree canopy cover threshold. In this paper, we examine the limitations of this approach and argue that the use of a land use-based forest definition and tree canopy cover thresholds can overlook forest degradation and enhancement, disguise the actual status of forest landscapes, and misinform management planning. These limitations can delay the development and implementation of forest restoration and conservation measures. To help overcome these issues, we propose some enhancements to the global forest assessment workflow, including the sharing of spatial data and inclusion of tree canopy cover estimates in assessment reports. Such enhancements are needed to achieve more meaningful forest monitoring and reporting in the context of global environmental initiatives, such as those related to climate change mitigation and adaptation, forest restoration, biodiversity conservation, and ecosystem services monitoring.

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment.

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab ) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab , while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables' spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Bushmeat, wet markets, and the risks of pandemics: Exploring the nexus through systematic review of scientific disclosures.

The novel coronavirus (SARS-CoV-2) is the third coronavirus this century to threaten human health, killing more than two million people globally. Like previous coronaviruses, SARS-CoV-2 is suspected to have wildlife origins and was possibly transmitted to humans via wet markets selling bushmeat (aka harvested wild meat). Thus, an interdisciplinary framework is vital to address the nexus between bushmeat, wet markets, and disease. We reviewed the contemporary scientific literature to: (1) assess disease surveillance efforts within the bushmeat trade and wet markets globally by compiling zoonotic health risks based on primarily serological examinations; and (2) gauge perceptions of health risks associated with bushmeat and wet markets. Of the 58 species of bushmeat investigated across 15 countries in the 52 articles that we analyzed,one or more pathogens (totaling 60 genera of pathogens) were reported in 48 species, while no zoonotic pathogens were reported in 10 species based on serology. Burden of disease data was nearly absent from the articles resulting from our Scopus search, and therefore was not included in our analyses. We also found that perceived health risks associated with bushmeat was low, though we could not perform statistical analyses due to the lack of quantitative perception-based studies. After screening the literature, our results showed that the global distribution of reported bushmeat studies were biased towards Africa, revealing data deficiencies across Asia and South America despite the prevalence of the bushmeat trade across the Global South. Studies targeting implications of the bushmeat trade on human health can help address these data deficiencies across Asia and South America. We further illustrate the need to address the nexus between bushmeat, wet markets, and disease to help prevent future outbreaks of zoonotic diseases under the previously proposed "One Health Framework", which integrates human, animal, and environmental health. By tackling these three pillars, we discuss the current policy gaps and recommend suitable measures to prevent future disease outbreaks.

Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review.

Green fractional vegetation cover (fc ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIS, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.

Assessing sustainable development prospects through remote sensing: A review.

The Earth's ecosystems face severe environmental stress from unsustainable socioeconomic development linked to population growth, urbanization, and industrialization. Governments worldwide are interested in sustainability measures to address these issues. Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Scientists and decision-makers have endorsed extensive use of remote sensing to bridge gaps among disciplines and achieve sustainable development. This paper presents an extensive review of remote sensing technology used to support sustainable development efforts, with a focus on natural resource management and assessment of natural hazards. We further explore how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing sustainable development challenges. Remote sensing technology has improved significantly in terms of sensor resolution, data acquisition time, and accessibility over the past several years. This technology has also been widely applied to address key issues and challenges in sustainability. Furthermore, an evaluation of the suitability and limitations of various satellite-derived indices proposed in the literature for assessing sustainable development goals showed that these older indices still perform reasonably well. Nevertheless, with advancements in sensor radiometry and resolution, they were less exploited and new indices are less explored.

Hydrological Simulation for Predicting the Future Water Quality of Adyar River, Chennai, India.

Just a few decades ago, Adyar River in India's city of Chennai was an important source of water for various uses. Due to local and global changes (e.g., population growth and climate change), its ecosystem and overall water quality, including its aesthetic value, has deteriorated, and the water has become unsuitable for commercial uses. Adverse impacts of excessive population and changing climate are expected to continue in the future. Thus, this study focused on predicting the future water quality of the Adyar river under "business as usual" (BAU) and "suitable with measures" scenarios. The water evaluation and planning (WEAP) simulation tool was used for this study. Water quality simulation along a 19 km stretch of the Adyar River, from downstream of the Chembarambakkam to Adyar (Bay of Bengal) was carried out. In this analysis, clear indication of further deterioration of Adyar water quality by 2030 under the BAU scenario was evidenced. This would be rendering the river unsuitable for many aquatic species. Due to both climate change (i.e., increased temperature and precipitation) and population growth, the WEAP model results indicated that by 2030, biochemical oxygen demand (BOD) and Escherichiacoli concentrations will increase by 26.7% and 8.3%, respectively. On the other hand, under the scenario with measures being taken, which assumes that "all wastewater generated locally will be collected and treated in WWTP with a capacity of 886 million liter per day (MLD)," the river water quality is expected to significantly improve by 2030. Specifically, the model results showed largely reduced concentrations of BOD and E.coli, respectively, to the tune of 74.2% and 98.4% compared to the BAU scenario. However, even under the scenario with measures being taken, water quality remains a concern, especially in the downstream area, when compared with class B (fishable surface water quality desirable by the national government). These results indicate that the current management policies and near future water resources management plan (i.e., the scenario including mitigating measures) are not adequate to check pollution levels to within the desirable limits. Thus, there is a need for transdisciplinary research into how the water quality can be further improved (e.g., through ecosystem restoration or river rehabilitation).

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.