Abstract
With increasing population, there is growing concern for food security in urban areas. Though, urban gardening has gained popularity, several studies have found higher concentrations of ...contaminants in urban soils, especially heavy metals, often at toxic levels, which pose a potential risk for human health. Moreover, heavy metal polluted sites have been strongly associated with areas populated by low-income families, newcomers and racial minorities. In this study, heavy metals in the soils of community gardens in the city of Guelph, ON were examined as a case study and their relationship with vulnerable populations. We analyzed soil samples at two depths for a range of heavy metals and characterized their spatial patterns to see if they were related to disadvantaged communities. We estimated the pollution levels using two index-based approaches and assessed their potential risk for human health, although concentrations of most heavy metals were below the limits established by Canadian regulations, metals like Cd, Pb, Se and Zn exhibited a mild degree of pollution, whereas As exhibited a severe degree. Their association with vulnerable populations were weak, but hotspots were mainly located in low-income areas. This case study provides scientific evidence that could help to expand our understanding around the interconnection between pollution and poverty/racial inequality. Also the importance of generating strategies for the protection of human health and sustainable soil management practices in urban areas where food for human consumption is grown.
Sampling design (SD) plays a crucial role in providing reliable input for digital soil mapping (DSM) and increasing its efficiency. Sampling design, with a predetermined sample size and consideration ...of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation. First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure; and 3) testing the feasibility and contribution of SDs in three-dimensional (3D) DSM with variability for multiple layers.
Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving ...factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.
Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and ...cost limitations of current soil phosphate tests have restricted their widespread use and have made them accessible only in laboratories. This study proposes a novel paper-based reagentless electrochemical soil phosphate sensor to extract and detect soil phosphate using an inexpensive and simple approach. In this test, concentrated Mehlich-3 and molybdate ions were impregnated in filter paper, which served as the phosphate extraction and reaction zone, and was followed by electrochemical detection using cyclic voltammetry signals. Soil samples from 22 sampling sites were used to validate this method against inductively coupled plasma optical emission spectroscopy (ICP) soil phosphate tests. Regression and correlation analyses showed a significant relationship between phosphate determinations by ICP and the proposed method, delivering a correlation coefficient, r, of 0.98 and a correlation slope of 1.02. The proposed approach provided a fast, portable, low-cost, accessible, reliable, and single-step test to extract and detect phosphate simultaneously with minimum waste (0.5 mL per sample), which made phosphate characterization possible in the field.
Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of ...salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.
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•The response of ecosystem WUE to soil moisture drought was quantitatively examined.•WUE increased significantly in most vegetated areas of the world during 1982–2018.•Drought had ...approximately 4-month lagged effect on WUE.•Drought-resilient ecosystems were concentrated in the Northern Hemisphere.
Water use efficiency (WUE) is an ecological indicator reflecting the link between carbon and water cycles in terrestrial ecosystems, which is often affected by drought disturbance. However, knowledge about the influences of soil moisture drought on WUE is still lacking. Therefore, this paper aims to quantify the lagged effect and impact of soil moisture drought on terrestrial ecosystem WUE from 1982 to 2018 using ERA5 and Global Land Surface Satellite (GLASS) datasets. Drought conditions are described by the soil moisture anomaly percentage index (SMAPI). The lagged effect of drought on WUE is measured by the month with the maximum significant correlation between SMAPI and WUE. The impact of drought on WUE is estimated through the relative change of WUE during drought and non-drought periods. The results showed that: (1) Drought had an approximately 4-month lagged effect on WUE, which was observed in 70.87% of the global vegetated areas. The lagged effect of drought on WUE was a short period (1–4 months) for shrubland and sparse vegetation, middle and long periods (5–12 months) for forest. (2) When drought occurred, WUE decreased by 36.95% in the Tibetan Plateau and 24.93% in West Africa, while WUE in North Europe, Alaska/N.W. Canada, and West Asia increased by 14.64%, 8.83%, and 8.53%, respectively. The negative and positive impacts of drought on WUE in each vegetation type were commensurate. Our results provide useful information for understanding the response of ecosystem carbon and water cycles to drought..
► Large scale temporal stability is season and location independent. ► Intra season and inter-annual temporal stability is stronger than inter-season. ► Medium scale temporal stability depends on the ...landform elements. ► Scale and location of temporal stability depends on depth.
Different factors and processes operating in different intensities and at different space–time scales result in strong spatio-temporal variability in soil water storage. However, there is similarity between the overall spatial patterns of soil water storage measured at different times, which has been identified as time stability. The objective of this study was to examine the scales and locations of time stability of soil-water storage spatial patterns at different seasons and depths in a hummocky landscape. Soil water storage was measured up to 140
cm depth over a 4-year period using time domain reflectometry and a neutron probe along a transect in the St. Denis National Wildlife Area, Saskatchewan, Canada. The transect was 576
m long with 128 sampling points (4.5
m sampling interval) and traversed several knolls and depressions. There were high Spearman’s rank correlation coefficients between any two-measurement series, indicating strong time stability of the spatial pattern of soil water storage. The spatial patterns of soil-water storage from the same season (intra-season) had stronger time stability than those from different seasons (inter-season). Strong time stability was also observed between the measurement series from a season of 1
year and a measurement series from the same season of another year (inter-annual). Wavelet coherency analysis indicated that the large-scale (>72
m) spatial pattern was time stable irrespective of seasons due to the alternating knolls and depressions in the study area. There were also near replica spatial patterns at small (<18
m) and medium (18–72
m) scales during summer and fall, possibly resulting from evapotranspiration of vegetation established in mid-summer. The inter-season time stability was only present within large depressions with long slopes and fewer landform elements. However, intra-season and inter-annual time stability was observed at all scales and locations. The time stability of surface soil water storage was different from that of the whole soil profile, indicating a depth dependence of time stability. The change in the scales and locations of spatial pattern of soil water storage indicates the change in the hydrological processes, which can be used to identify the change in the sampling domain.
Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized ...difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world.
•A setup containing a smartphone and a dark chamber was used for predicting soil texture.•Soil images were acquired using the setup and analyzed via computer vision, RF, and CNN models.•Both RF and ...CNN showed high prediction accuracy for clay and sand, with moderate prediction accuracy for silt.•Image-extracted color features showed the maximum influence on the RF model performance.•An Android app using the calibrated CNN model was able to predicted soil textural values with satisfactory accuracy.
The rapid and non-invasive prediction of soil sand, silt, and clay is becoming increasingly attractive given the laborious nature of traditional soil textural analysis. This study proposed a novel and cheap setup comprising a smartphone, a custom-made dark chamber, and a smartphone application for predicting soil texture of the dried, ground, and sieved samples. The image acquisition system was used to capture triplicate images from 90 mineral soil samples, representing a wide textural variability from sand to clay. Local features, color features, and texture features were extracted from the cropped images and subsequently used in different combinations to predict laboratory-measured clay, silt, and sand via random forest (RF) and convolutional neural network (CNN) algorithms. Results indicated high prediction accuracy for clay (R2 = 0.97–0.98) and sand (R2 = 0.96–0.98) and moderate prediction accuracy for silt (R2 = 0.62–0.75) using both algorithms. Color features outperformed all other image-extracted features and showed the maximum influence on RF model performance. The better performance of the color features can be attributed to the color features of mineral matter and soil organic matter (SOM). An Android-based smartphone application based on the calibrated CNN model was able to predict and return soil textural values. These results exhibited the potential of the proposed system as a proximal sensor for rapid, cost-effective, and eco-friendly soil textural analysis using computer-vision and deep learning. More research is warranted to augment the setup design, develop a standalone mobile application, and measure the impacts of soil moisture and high SOM on the model prediction performance to extend the approach for on-site prediction of soil texture.
The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different ...conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
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