The results of the Forest Resources Assessment 2000 carried out by FAO are synthetically presented and discussed. The world forest coverage is estimated equal to 38.6 million km 2 . The comparison of the estimates from the period 1990-2000 with those from the period 1980-1990 points out a certain general deceleration of the rate of net deforestation, that currently involves around 9 million hectares every year. However, the annua1 loss of tropical forests is still very large, while temperate and borea1 forests are in expansion. Overall, FRA2000 produced a relevant effort to compensate the existing technical, institutional and financial constraints and shortcomings for monitoring the world forest resources. The need to increase the quality and the frequency of forest surveys, both at national and international levels, s t a stands as a major issue to cope with.

http://ojs.aisf.it/index.php/ifm/article/download/774/738

Valutazione delle risorse forestali a livello globale

A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research

Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov ), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001 to 2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error (SE) was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001 and 2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this period. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001–2006 at twice the rate of 2011–2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.

Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result.

Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series

Abstract Identifying the socioeconomic drivers of COVID‐19 deaths is essential for designing effective policies and health interventions. However, how the significance and impact of these factors varies across different spatial regimes has been scantly explored. In this ecological cross‐sectional study, we apply the spatial lag by regimes regression model to examine how the socioeconomic and health determinants of COVID‐19 death rate vary across (a) metropolitan vs. non‐metropolitan, (b) shelter‐in‐place vs. no‐shelter‐in‐place order, and (c) Democratic vs. Republican US counties. A total of 20 variables were studied across 3108 counties in the contiguous US for the first year of the pandemic (6 February 2020 to 5 February 2021). The results show that the COVID‐19 death rate not only depends on a complex interplay of the population demographic, socioeconomic and health‐related characteristics, but also on the spatial regime that the residents live, work and play. Household median income, household size, percentage of African Americans, percentage aged 40–59 and heart disease mortality are significant to metropolitan but not to non‐metropolitan counties. We identified lack of insurance access as a significant driver across all regimes except for Democratic. We also showed that the political orientation of the governor might have impacted COVID‐19 death rates due to the public response (i.e., shelter‐in‐place vs. no‐shelter‐in‐place order). The proposed analysis allows for understanding the socioeconomic context in which public health policies can be applied, and importantly, it presents how COVID‐19 death related factors vary across different spatial regimes.

Exploring the socioeconomic drivers of COVID‐19 mortality across various spatial regimes

https://link.springer.com/content/pdf/10.1007%2Fs10707-011-0150-7.pdf

Erratum to: A fuzzy index for detecting spatiotemporal outliers

In the United States, the rise in hypertension prevalence has been connected to neighborhood characteristics. While various studies have found a link between neighborhood and health, they do not evaluate the relative dependence of each component in the growth of hypertension and, more significantly, how this value differs geographically (i.e., across different neighborhoods). This study ranks the contribution of ten socioeconomic neighborhood factors to hypertension prevalence in Chicago, Illinois, using multiple global and local machine learning models at the census tract level. First, we use Geographical Random Forest, a recently proposed non-linear machine learning regression method, to assess each predictive factor’s spatial variation and contribution to hypertension prevalence. Then we compare GRF performance to Geographically Weighted Regression (local model), Random Forest (global model), and OLS (global model). The results indicate that GRF outperforms all models and that the importance of variables varies by census tract. Household composition is the most important factor in the Chicago tracts, while on the other hand, Housing type and Transportation is the least important factor. While the household composition is the most important determinant around north Lake Michigan, the socioeconomic condition of the neighborhood in Chicago’s mid-north has the most importance on hypertension prevalence. Understanding how the importance of socioeconomic factors associated with hypertension prevalence varies spatially aids in the design and implementation of health policies based on the most critical factors identified at the local level (i.e., tract), rather than relying on broad city-level guidelines (i.e., for entire Chicago and other large cities).

Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA

Examining the importance of built and natural environment factors in predicting self-rated health in older adults: An extreme gradient boosting (XGBoost) approach

Previous studies have mainly examined the independent effects of green or blue space on health from a perspective of spatial homogeneity, which neglects their interactive or spatially varying effects. Here, we examined the spatially varying and interactive effects of green and blue space on health using open access data in Northern Ireland (NI). Aggregate health data was collected from 2017 Northern Ireland Multiple Deprivation Measure at the Super Output Area (SOA) level. Green and blue spaces were extracted from Land Cover Map data. The proportion of grassland and the proportion of woodland for each SOA were calculated as proxies for green space, while the proportion of water bodies was calculated for measuring blue space. Spatially varying effects of green and blue space were modelled using multiscale Geographic Weighted Regression (MGWR). Interaction terms between green and blue spaces were added into the MGWR models to test the interactive association of green and blue space on different health outcomes (e.g., preventable death ratio and cancer registrations). Results indicate that associations were distributed zonally, with green and blue spaces in eastern areas of NI more strongly associated with health outcomes than in western areas. Within these large regional zones, further spatially varying effects of different green and blue spaces were observed. Grassland was generally positively associated with some health outcomes (e.g., less preventable death ratio, cancer registrations ratio, multiple prescriptions ratio, and long-term health problem or disability ratio), while the results of woodland and water body were mixed. Water bodies were found to strengthen the effect of woodland and grassland. The above results indicate that green and blue space have independently and interactive spatially varying associations with different health outcomes in NI. It is also important to combine both green and blue space elements to enhance health impacts in future interventions.

https://iopscience.iop.org/article/10.1088/2515-7620/acc3e1/pdf

Examining the spatially varying and interactive effects of green and blue space on health outcomes in Northern Ireland using multiscale geographically weighted regression modeling

George Grekousis

Papers

Exploring the socioeconomic drivers of COVID‐19 mortality across various spatial regimes

Erratum to: A fuzzy index for detecting spatiotemporal outliers

Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA

Examining the importance of built and natural environment factors in predicting self-rated health in older adults: An extreme gradient boosting (XGBoost) approach

Examining the spatially varying and interactive effects of green and blue space on health outcomes in Northern Ireland using multiscale geographically weighted regression modeling