Explore the vast range of titles available.

We assessed the potential of Machine Learning (ML) for mapping crop growth in three flood irrigated fields. Results generated from ML algorithms were compared to the output generated by the ISODATA algorithm. Affinity Propagation (AP) identifies the number of clusters by considering all data points as potential exemplars and iteratively refine the set, while Gaussian Mixture Model (GMM) algorithm treats the data as a mixture of several Gaussian distributions, allowing for flexible cluster shapes. In contrast, ISODATA, a statistical clustering method, requires an analyst to specify the number of output clusters followed by iterative splitting and merging of clusters based on variance and distance criteria. We acquired Landsat derived NDVI images for three flood-irrigated fields over a span of four years. These images were collected at the start of the growing season to ensure consistency. Initially we clustered the pixels in these images for each field using AP and determine the number of clusters. Next, we applied GMM to identify and define the clusters. Finally, we plotted the mean value of all the pixels in each cluster for every year and assigned the clusters into six thematic classes: the first three classes for consistent growth (good, average, or poor) across all four years, and the other three for mixed growth patterns (e.g., good in three years and average in one). Output maps generated from these methods were compared using IoU scores. ML methods had greater efficiency in terms of replicating the steps for other fields, whereas ISODATA requires analyst intervention and interpretation.

Biologically active areas play an extremely important role in the structure of a city and increasing their coverage, especially in large urban centres, is an activity with a number of advantages. This article compares, in terms of green spaces, two European cities of similar size – Warsaw (517.2 km2) and Oslo (454 km2). Both cities are capitals of their respective countries but implement different spatial policies in the scope of the Green Deal. In Warsaw, many industrial and post-industrial areas still exist and simultaneously urban green areas are decreasing year by year. In Oslo, a strategy based on deindustrialisation of the city and possible maximum use of urban greenery and public spaces is implemented. The research described in this article involved analysing the coverage of the analysed cities and their districts with biologically active area and then checking the correlation with other indicators that can be affected by this coverage. These included data on the incidence of the most common diseases among residents, the attractiveness of living for the elderly and families with children, as well as air and soil pollution and the occurrence of negative effects of climate change. The correlation of urban space use in terms of the presence of industrial land in relation to currently existing green spaces in the districts concerned was subsequently determined.

Rangelands play a crucial role in Ethiopia’s rural economy, ecological biodiversity, and food security. However, their condition at the local level remains inadequately documented, limiting timely responses to degradation. This study aimed to assess rangeland conditions using Normalized Difference Vegetation Index (NDVI) analysis and field data collection methods, employing Landsat 8 Operational Land Imager (OLI) for 2024 NDVI data. In addition to geospatial data, we conducted 123 household surveys, six key informant interviews, and field surveys to evaluate rangeland conditions and identify the drivers of degradation. Our results documented 19 herbaceous species in the study area, with grasses making up the majority (68.5%), followed by two succulent species (10.5%), two forbs (10.5%), and two sedge species (10.5%). We also recorded 24 woody plant species, categorized into 11 trees (45.8%) and 13 shrubs (54.2%). The rangeland condition assessment yielded an average score of 29.4 ± 6.17 based on seven ecological indicators. NDVI values ranged from −0.02 to 0.41, indicating varying levels of vegetation health, with evidence of both healthy and degraded areas. The findings showed that 1.6% of the land was healthy rangeland, 11.6% was low degraded, 67.8% was moderately degraded, and 19% was highly degraded. The study identified the main drivers of rangeland degradation as the weakening of traditional management systems (93%), population pressure (89%), recurrent droughts (85%), bush encroachment (65%), and agricultural expansion into grazing lands. Key Informant Interviews confirmed statistically significant differences among these drivers (Friedman test: χ 2 (4) = 21.63, p < 0.001), with the weakening of traditional rangeland management ranked highest. In conclusion, this study underscores the urgent need for localized monitoring and management strategies to combat rangeland degradation in the study area. We recommended that policymakers invest in developing and implementing sustainable rangeland management practices that consider traditional knowledge and address the identified drivers of degradation.

The Upper Amazon River forms an anabranching planform, which has been found to have significant changes in migration rate and river morphology. Previous studies have elaborated long-term evolution of the anabranching systems; however, research on the influence of the water level on temporal changes in anabranching is absent. According to the theory of river hydraulics, fluvial scour usually occurs when the shear force possessed by the high flow exceeds the resistance of the streambank. In contrast, deposition occurs when the tractive force of the low flow is insufficient to overcome the forces of gravity and friction. This study investigated the Muyuy anabranching planform change of the Upper Peruvian Amazon River due to the seasonal flow fluctuations and a severe flood in 2012. The Muyuy anabranching area is located 20 km upstream of Iquitos City, Peru. Landsat images from the wet and dry seasons in 2008, 2009, 2012, and 2013 were collected. The images were classified into three land cover classes (water, bare soil, vegetation and others) based on NDVI analysis. Quantitative analysis of the erosion/deposition shows that deposition is more noticeable than erosion in the Muyuy anabranching area. Considerable deposition can be found on the island of the anabranching system, and the streambank erosion occurred in the outer (concave) side of the main channel. This phenomenon of river erosion and deposition consistently occurred in 2008 and 2009 because of the periodical variation among the wet and dry seasons. However, prominent erosion was observed in 2012 and it was recognized to be caused by the severe flood. Furthermore, the extensive island was formed in 2013, which means substantial depositions accumulated in the recession of the 2012 flood.

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler’s criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of −0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia’s total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management.

Traditional agricultural activities and rural livelihoods in Morocco’s High Atlas Mountains are rapidly changing. This is triggered by increasing rural-urban interactions and new livelihood opportunities in cities. A typical example is the oasis of Tizi N’Oucheg in the country’s High Atlas Mountains, which over centuries was largely self-sufficient in food grain and livestock production. Improved infrastructure and better connections to distant urban centers have caused declining livelihood reliance on agricultural activities and enhanced dependence on remittances and the tourism sector in the region. Based on the case of Tizi N’Oucheg, this study aims at assessing the socio-economic and ecological implications of rural-urban transformation for ancient oasis systems in Morocco. Surveys on agricultural practices, census, and meteorological data were combined with GIS (Geographical Information System) -based analyses to examine land use and cropping pattern changes of 625 fields from 1967 to 2022. For the GIS analyses, historical aerial images, multispectral satellite images, and drone-based surveys were used to generate manually classified agricultural fields and NDVI (Normalized Difference Vegetation Index) time series. Our results show a major decline in cultivated land from 13 ha to 6.8 ha over the past 50 years accompanied by an expansion of modern infrastructure since the 2000s. Land management has shifted from labor-intensive multiple cropping and natural fertilization to monocropping of barley for local livestock feeding and increased application of mineral fertilization. The challenging geography of the oasis increased the hardship of practicing traditional agriculture, and therefore largely determines the response of the community to rural-urban transformation. Our data also highlight the increased financial dependence of rural populations on urban centers and the demise of traditional, sustainable agriculture in Morocco’s High Atlas Mountains if policies on agricultural development are not adapted to rural circumstances.

Changes in the spatiotemporal pattern of vegetation alter the structure and function of landscapes, consequently affecting biodiversity and ecological processes. Distinguishing human-induced vegetation changes from those driven by environmental variations is critically important for ecological understanding and management of landscapes. The main objectives of this study were to detect human-induced vegetation changes and evaluate the impacts of land use policies in the Xilingol grassland region of Inner Mongolia, using the NDVI-based residual trend (RESTREND) method. Our results show that human activity (livestock grazing) was the primary driver for the observed vegetation changes during the period of 1981–2006. Specifically, vegetation became increasingly degraded from the early 1980s when the land use policy—the Household Production Responsibility System—led to soaring stocking rates for about two decades. Since 2000, new institutional arrangements for grassland restoration and conservation helped curb and even reverse the increasing trend in stocking rates, resulting in large-scale vegetation improvements in the region. These results suggest that most of the degraded grasslands in the Xilingol region can recover through ecologically sound land use policies or institutional arrangements that keep stocking rates under control. Our study has also demonstrated that the RESTREND method is a useful tool to help identify human-induced vegetation changes in arid and semiarid landscapes where plant cover and production are highly coupled with precipitation. To effectively use the method, however, one needs to carefully deal with the problems of heterogeneity and scale in space and time, both of which may lead to erroneous results and misleading interpretations.

Fil: Landi, Marcos Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; Argentina

Fractional green vegetation cover (FVC) is a useful parameter for many environmental and climate-related applications. A common approach for estimating FVC involves the linear unmixing of two spectral endmembers in a remote sensing image; bare soil and green vegetation. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources (e.g., soil databases or land cover maps), or extracted directly from the imagery. Most FVC estimation approaches do not consider that the spectral properties of endmembers may vary across space. However, due to local differences in climate, soil type, vegetation species, etc., the spectral characteristics of soil and green vegetation may exhibit positive spatial autocorrelation. When this is the case, it may be useful to take these local variations into account for estimating FVC. In this study, spatial interpolation (Inverse Distance Weighting and Ordinary Kriging) was used to predict variations in the spectral characteristics of bare soil and green vegetation across space. When the spatially-interpolated values were used in place of scene-invariant endmember values to estimate FVC in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, the accuracy of FVC estimates increased, providing evidence that it may be useful to consider the effects of spatial autocorrelation for spectral mixture analysis.

Disadvantaged groups worldwide, such as low-income and racially/ethnically minoritized people, experience worse health outcomes than more privileged groups, including wealthier and white people. Such health disparities are a major public health issue in several countries around the world. In this systematic review, we examine whether green space shows stronger associations with physical health for disadvantaged groups than for privileged groups. We hypothesize that disadvantaged groups have stronger protective effects from green space because of their greater dependency on proximate green space, as they tend to lack access to other health-promoting resources. We use the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and search five databases (CINAHL, Cochrane, PubMed, Scopus, and Web of Science) to look for articles that examine whether socioeconomic status (SES) or race/ethnicity modify the green space-health associations. Based on this search, we identify 90 articles meeting our inclusion criteria. We find lower-SES people show more beneficial effects than affluent people, particularly when concerning public green spaces/parks rather than green land covers/greenness. Studies in Europe show stronger protective effects for lower-SES people versus higher-SES people than do studies in North America. We find no notable differences in the protective effects of green space between racial/ethnic groups. Collectively, these results suggest green space might be a tool to advance health equity and provide ways forward for urban planners, parks managers, and public health professionals to address health disparities.