Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale

Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

Field spectroscopy has been suggested to be an efficient method for predicting soil properties using quantitative mathematical models in a rapid and non-destructive manner. Traditional multivariate regression algorithms usually regard the modeling of each soil property as a single task, which means only one response variable is considered as the output during modeling. Therefore, these algorithms are less suitable for the prediction of several key soil properties with low concentrations or unobvious spectral absorption signals. In the current study, we investigated the performance of a linear multi-task learning (LMTL) algorithm based on a regularized dirty model for modeling and predicting several key soil properties using field spectroscopy (350–2500 nm) as an integrated approach. We tested seven key soil properties including available nitrogen (N), phosphorus (P) and potassium (K), pH, water content (WC), organic matter (OM), and electrical conductivity (EC) in drylands. The model performances of LMTL models were compared with the commonly used single-task algorithm of the partial least squares regression (PLS-R). Our results show that the LMTL models outperformed the PLS-R models with the advantage of shared features; the ratio of performance to deviation (RPD) values in the validation set improved by 10.24%, 4.93%, 25.77%, 11.76%, 6.74%, 53.13%, and 3.15% for N, P, K, pH, WC, OM, and EC, respectively. The best prediction was obtained for OM with RPD = 2.29, indicating high accuracy (RPD > 2). The prediction results of N, P, WC, and pH were categorized as of moderate accuracy (1.4 < RPD < 2), while K and EC were categorized as of poor accuracy (RPD < 1.4). However, the explanatory power of the LMTL models was moderate due to fewer features being selected by the regularization algorithm of the LMTL approach, which should be further studied in the soil spectral analysis. Our results highlight the use of LMTL in field spectroscopy analysis that can improve the generalization performance of regression models for predicting soil properties

Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread

Structural Changes of Desertified and Managed Shrubland Landscapes in Response to Drought: Spectral, Spatial and Temporal Analyses

Drought events cause changes in ecosystem function and structure by reducing the shrub abundance and expanding the biological soil crusts (biocrusts). This change increases the leakage of nutrient resources and water into the river streams in semi-arid areas. A common management solution for decreasing this loss of resources is to create a runoff-harvesting system (RHS). The objective of the current research is to apply geo-information techniques, including remote sensing and geographic information systems (GIS), on the watershed scale, to monitor and analyze the spatial and temporal changes in response to drought of two source-sink systems, the natural shrubland and the human-made RHSs in the semi-arid area of the northern Negev Desert, Israel. This was done by evaluating the changes in soil, vegetation and landscape cover. The spatial changes were evaluated by three spectral indices: Normalized Difference Vegetation Index (NDVI), Crust Index (CI) and landscape classification change between 2003 and 2010. In addition, we examined the effects of environmental factors on NDVI, CI and their clustering after successive drought years. The results show that vegetation cover indicates a negative ∆NDVI change due to a reduction in the abundance of woody vegetation. On the other hand, the soil cover change data indicate a positive ∆CI change due to the expansion of the biocrusts. These two trends are evidence for degradation processes in terms of resource conservation and bio-production. A considerable part of the changed area (39%) represents transitions between redistribution processes of resources, such as water, sediments, nutrients and seeds, on the watershed scale. In the pre-drought period, resource redistribution mainly occurred on the slope scale, while in the post-drought period, resource redistribution occurred on the whole watershed scale. However, the RHS management is effective in reducing leakage, since these systems are located on the slopes where the magnitude of runoff pulses is low

Losing ground: projections of climate-driven bloom shifts and their implications for the future of Californias almond orchards.

Climate change is expected to impact the spring phenology of perennial trees, potentially altering the suitability of land for their cultivation. In this study, we investigate the effects of climate change on the bloom timing of almond orchards, focusing on California, the worlds leading region for almond production. By analyzing historical climatic data, employing a model that considers hourly temperatures and fall non-structural carbohydrates to predict bloom dates, and examining various Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios, we assess the potential impacts of climate shifts on plant phenology and, consequently, on land suitability for almond farming. Our findings reveal that, within the next 30 years, the land suitable for almond production will not undergo significant changes. However, under unchanged emission scenarios, the available land to support almond orchard farming could decline between 48 to 73% by the end of the century. This reduction corresponds with an early shift in bloom time from the average Day of Year (DOY) 64 observed over the past 40 years to a projected earlier bloom between DOY 28-33 by 2100. These results emphasize the critical role climate shifts have in shaping future land use strategies for almond production in Central Valley, California. Consequently, understanding and addressing these factors is essential for the sustainable management and preservation of agricultural land, ensuring long-term food security and economic stability in the face of a rapidly changing climate

Assessment of plant species distribution and diversity along a climatic gradient from Mediterranean woodlands to semi-arid shrublands

Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species ‎richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients

Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

Land-use changes as a result of residential development often lead to degradation and alter vegetation cover (VC). Although these are worldwide phenomena, sufficient knowledge about anthropogenic effects caused by various populated areas in dryland ecosystems is lacking. This study explored anthropogenic development in rural areas and its effects on the conservation of protected areas in drylands, focusing on the change in VC, the reasons, extent, and the drivers of change. We propose a novel framework for exploring VC change (VCC) as a function of environmental and human-driven factors including different types of populated areas in drylands. As a case study, we used a 30-year time series of Landsat satellite images over the arid region of Israel to analyze spatiotemporal VCC. The temporal analysis involved the Contextual Mann-Kendall significance test and spatial analysis to model clustering of VCC. A Gradient Boosted Regression machine learning algorithm was applied to study the relative influence of environmental and human-driven factors on VCC. In addition, we used ANOVA to examine differences between the effects of three types of populated areas on the spatiotemporal trends of VC. The results show that the most influential environmental variable on VCC was elevation (relative contribution of 17%), followed by slope (14.8%) and distance from populated areas (14.6%). Moreover, different types of populated areas affected VC differently with varying distances from residential centroids. The nature reserves increased VC positively and significantly, while livestock settlements had a negative effect. Change in vegetation was mostly confined to the stream network and occurred in lower elevations. The study demonstrates how different land-use practices alter the landscape in terms of VC and differ in their extents, patterns, and effects. With the expected growth in population and residential development worldwide, the proposed framework may assist conservation managements and policy makers in minimizing environmental degradation in drylands

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale