Soil Brightness Research Articles

Influence of two management practices in the Canadian Prairies on radiative forcing

Surface albedo and soil carbon sequestration are influenced by agricultural management practices which impact the Earth's radiation budget and climate change. In this study we investigate the impact of reduced summer fallowing and reduced tillage in the Canadian Prairies on climate change by estimating the change in radiative forcing due to albedo and soil carbon sequestration. Seasonal variations of albedo, which are dependent on agricultural management practices and soil colour in three soil zones, were derived from 10-day composite 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data. Using this information, we found an overall increase of surface albedo due to the conversion from summer fallowing to continuous cropping and from conventional tillage (CT) to either no-tillage (NT) or reduced tillage (RT). The increase was dependent on soil brightness, type of vegetation and snow cover. Using data from the Census of Agriculture and taking into consideration both albedo and soil carbon changes, we estimated that from 1981 to 2016, the total radiative forcing for the cropland area in the Canadian Prairies was −405 μW m−2 due to the conversion of CT to either NT or RT and about 70% was due to the change in albedo. During the same period, the total radiative forcing was −410 μW m−2 due to a reduction in the area under summer fallow and about 62% was due to the change in albedo. The equivalent atmospheric CO2 drawdown from these two management changes from albedo change was about 7.8 and 8.7 Tg CO2 yr−1, respectively. These results demonstrate that it is important to consider both the changes of soil carbon and surface albedo in evaluating climate change impacts due to agricultural management practices.

On Evaluating the Depth of Soil Freezing Based on SMOS Satellite Data

The results of a comparative analysis of the brightness temperatures determined from the SMOS satellite and the corresponding depths of soil freezing, measured at weather stations located at the test sites of the Kulunda Plain, are presented. Based on the daily satellite measurement of brightness temperature, the effect of soil freezing on the microwave radiation of the underlying surface was studied. A theoretical calculation of the dependence of soil brightness temperature on the depth of freezing is performed with the model of microwave radiation of a plane-layered inhomogeneous non-isothermal medium. The real parameters of the Kulunda plain soil as well as the climatic characteristics of the sites under study, obtained from the weather stations for the same period, were used as the input parameters of the model. The analysis of satellite, field and model data showed that the evaluation of the depth of soil freezing with satellite microwave radiometry is limited by the need to conduct the contact measurements of physical properties of soil in the areas, for which the SMOS product on the brightness temperature is given.

Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE.

A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence

Solar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon emission, most SIF photons are reabsorbed or scattered within the canopy, preventing their observation remotely. The complexities of the radiative transfer process, which vary across time and space, limit our ability to reliably infer physiological processes from SIF observations. Here, we propose an approach for estimating the fraction of total emitted near-infrared SIF (760 nm) photons that escape the canopy by combining the near-infrared reflectance of vegetation (NIRV) and the fraction of absorbed photosynthetically active radiation (fPAR), two widely available remote sensing products. Our approach relies on the fact that NIRV is resilient against soil background contamination, allowing us to reliably calculate the bidirectional reflectance factor of vegetation, which in turn conveys information about the escape ratio of SIF photons. Our NIRV-based approach explains variations in the escape ratio with an R2 of 0.91 and an RMSE of 1.48% across a series of simulations where canopy structure, soil brightness, and sun-sensor-canopy geometry are varied. The approach is applicable to conditions of low leaf area index and fractional vegetation cover. We show that correcting for the escape ratio of SIF using NIRV provides robust estimates of total emitted SIF, providing for the possibility of studying physiological variations of fluorescence yield at the global scale.

Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

<p>The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI (L = 0.5) where L is a constant of the soil brightness component, EVI2 (improved vegetation index 2), NDWI (standard difference water index), WDRI (wide dynamic range vegetation index) and the Red Edge model that only this one has in Sentinel-2 in this study. Making a classification of the Bofedal ecosystem in satellite images by applying Random Forest, the most important variables with Landsat-8 were EVI2 (37.72%) and SAVI with L = 0.5 (30.97%), while with Sentinel-2 the most important variables correspond to the Red Edge (38.54%) and WDRI (27.06%). With the indices calculated, two categories of analysis were determined: a) wetland integrated by the levels: intervened [1], moderately conserved [2] and conserved [3] and b) other than wetland [4] integrated by areas that do not correspond to this ecosystem. Landsat-8 shows that the percentage of correct classifications of píxels belonging to the wetland category corresponds to: [1] 72.76%, [2] 58.38%, [3] 68.42%, while for the category other [4] were correct 95.15%. With Sentinel-2, the percentage of correct classifications corresponds to [1] 95.00%, [2] 82.60%, [3] 96.25%, while for the category other [4] the correct answers were 98.13%. In this way with Landsat-8 the wetland corresponds to 21.708,54 ha (41.21%), while with Sentinel-2 the wetland represents a total of 20,518 ha (38.95%), of the 52,560 ha that belong to the RPFCH, concluding that Sentinel-2, due to its better spatial resolution, and the incorporation of its new bands in Red Edge, obtains better results in image classification.</p>

On evaluation of depth of soil freezing based on Smos satellite data

The results of a comparative analysis of the brightness temperatures determined from the SMOS satellite and the corresponding depths of soil freezing, measured at weather stations located at the test sites of the Kulunda Plain, are presented. Based on the daily satellite measurement of brightness temperature, the effect of soil freezing on the microwave radiation of the underlying surface was studied. A theoretical calculation of the dependence of soil brightness temperature on the depth of freezing is performed with the model of microwave radiation of a plane-layered inhomogeneous non-isothermal medium. The real parameters of the Kulunda plain soil as well as the climatic characteristics of the sites under study, obtained from the weather stations for the same period, were used as the input parameters of the model. The analysis of satellite, field and model data showed that the evaluation of the depth of soil freezing with satellite microwave radiometry is limited by the need to conduct the contact measurements of physical properties of soil in the areas, for which the SMOS product on the brightness temperature is given.

Anthropogenic nest sites provide warmer incubation environments than natural nest sites in a population of oviparous reptiles near their northern range limit.

Oviposition site choice affects a host of offspring phenotypes and directly impacts maternal fitness. Recent evidence suggests that oviparous reptiles often select nest sites where the landscape has been altered by anthropogenic activity, whereas natural nest sites are less often used. We leverage a long-term study of snapping turtle (Chelydra serpentina) to identify natural nest sites and anthropogenic nest sites and to compare habitat variables among nest site types. Natural and anthropogenic nest sites did not differ in average canopy closure, distance to nearest water, substrate composition, or aspect. However, anthropogenic nest sites had less ground-level vegetation and greater soil brightness, and were 3.3°C warmer than natural nests during incubation. We used the Schoolfield model of poikilotherm development to assess differences in development rate between natural and anthropogenic nests. Because of the difference in temperature, embryos in anthropogenic nests were predicted to have undergone nearly twice as much development as embryos in natural nests during incubation. We outline why the evolution of fast embryonic development rate cannot compensate indefinitely for the low temperature incubation regimes that become increasingly prevalent at northern range margins, thereby underlining why maternal nest site choice of relatively warm anthropogenic sites may help oviparous reptiles persist in thermally constrained environments. Future research should aim to quantify both the thermal benefits of anthropogenic nest sites, as well as associated fitness costs (e.g., increased adult mortality) to elucidate whether anthropogenic disturbance of the landscape can be an ecological trap or serve a net benefit to some reptiles in northern environments.

Oasis Landscape Pattern Dynamics in Manas River Watershed Based on Remote Sensing and Spatial Metrics

Constant alterations of oasis landscape pattern, triggered by specific natural and human driving forces, have aroused some heated discussions in the field of regional environmental change research especially in arid areas. In this study, the Normalized Difference Vegetation Index and the Soil Brightness Index have been selected to successfully identify discrete land cover classes derived from a series of Landsat satellite images of Manas River watershed in 1989, 1998, and 2014. FRAGSTATS and principal components analysis were used to recognize independent components of landscape structure. Nine land cover classes combined with ten landscape metrics indicated variations in the composition and configuration of landscape pattern. Results related to historical data were analyzed to identify driving forces, mainly including population growth, agricultural modernization, urbanization, and water resource provision. The results revealed a trend toward less landscape fragmentation, as indicated by a steady decrease in the number of patches as well as an increase in mean patch size and contagion. To be more specific, we found substantial increases in cropland (72.3%) as well as built-up land (43.7%) with larger and more compact patches, while water (64.1%), snow cover (52.0%), and saline–alkali soil (48.5%) dropped sharply. Our findings suggested that population influx and reclamation of saline–alkali soil were two major driving forces critical to those landscape pattern changes.

Proposing an early-warning system for optimal management of protected areas (Case study: Darmiyan protected area, Eastern Iran)

An early-warning system is a general idea that can act as a functional and inexpensive tool to ease the access to the global strategic goals of protection and sustainable development. Before the crisis happens, this system can provide effective information by using known resources, which creates an awareness of probable dangers and necessary actions. This study proposes an optimal method to cover the shortages in protected-area management. The proposed early-warning system is based on the pressure-state-response (P-S-R) approach and ecological security index. Twelve environmental indicators in three different criteria (P = 4, S = 5, R = 3) were chosen and the ecological security index of the protected area was generated. Based on the ecological security index status in the study area, statistical analysis, and expert opinions, three indicators (precipitation, vegetation covering status, and soil brightness) were chosen as the main and final indicators, to be used in the early-warning system. Eventually, with the calculation of the thirty-year average of the mentioned indicators in the area under study, the confidence interval for each of these indicators with a confidence factor of 95% was achieved. According to the results of this research, some parts of the south-west and east of the area under study were in warning status, which requires special management decisions. Based on the field visit and expert review in these parts, there was an obvious breakdown in the selected indicators. In fact, we’ve prevented the ecological disturbance in the ecosystem which plays an important role in preserving species of international importance by using technologies. We’ve made a shortcut to achieve the managerial goals in less developed countries in further studies in this field; the importance of each early-warning system indicator can be determined, so warning regions can be divided into more categories. This research can be applied in other climates, and the result can be compared to this paper, but the choice of general and effective indicators remains the most important part of the approach reported here.

Prediction of cotton lint yield from phenology of crop indices using artificial neural networks

A primary utility of satellite remote sensing technology is monitoring and assessment of agricultural lands for determining the area, amount, type, and quality of crop production. Since the mid-1970s agricultural scientists have sought to advance this utility through development of precision agriculture (PA) methods and technologies. Consequently, PA has taken advantage of freely available medium-spatial resolution remote sensing technology and instrumented fields to monitor crop biomass, phenology, and yield of crops at the sub-field to larger scales. The main goal of this study was to determine cotton lint yield in a 73-ha irrigated field in western Tennessee using remote sensing technology. We used two growing seasons (2013 and 2014) of Landsat 8 transformed to 8 input predictors including Red, near infra-red (NIR), the simple ratio (SR), normalized difference vegetation index (NDVI), green NDVI (GNDVI), and the tasselled cap transformation’s greenness, wetness, and soil brightness indices: GI, WI, and SBI, respectively, as proxies for cotton lint yield and crop phenology (in this study all input predictors are being referred to as crop indices, CIs). We used artificial neural network (ANN) approach to generate 61,200 models relating individual CIs and CI phenology to field estimates of lint yield to predict and map the field’s cotton lint yield in two cropping seasons. The correlation between cotton lint yield and CIs ranged from -0.20 to 0.60 in 2013 and from −0.79 to 0.84 in 2014. The best ANN models were in 2013 (r = 0.68 and the normalized MAE = 11%) and 2014 (r = 0.86 and the normalized MAE = 8%) growing seasons. The WI and GI were the best CI predictors of cotton lint yield, and overall for the early to mid-season prediction, CI phenologies had better performance than single date CI models. Consequently, we recommend the use of Landsat 8 derived WI or GI phenology to predict crop yields.

Drainage morphometric analysis using open access earth observation datasets in a drought-affected part of Bundelkhand, India

In this study, a part of Yamuna and Ken basin was selected for quantitative estimation of morphometric parameters through open access earth observation datasets. Further, spectral indices, namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Soil Brightness Index (SBI), have been calculated to know the relationship with the morphometric parameters. The area has sub-dendritic to dendritic drainage pattern and designated as fifth order stream and drainage density (0.564 km/km2). The stream order is mainly controlled by physiographic and lithological conditions of the area. Spectral indices have been employed to understand the vegetation, wetness, and soil moisture and soil characteristics in the area. The water retention curves of the area have been developed. The spectral indices and morphometric parameters (Dd) shows highly positive correlation. The result has been validated with the aid of borehole/lithology datasets. These quantitative estimates will help in achieving and developing long-term strategies for combating hydrological drought, meteorological drought, climate-related drought, agricultural drought, water mismanagement-related drought, and affected region for preparation of suitable groundwater management plans.

Detection of damaged areas caused by the oil extraction in a steppe region using winter landsat imagery

Oil production in a steppe region disturbs the landscape and damages the steppe ecosystem. The objective of this research was to detect areas damaged by oil production in an oil field within the Russian Volga–Ural steppe region using winter Landsat imagery. We developed a practicable and effective approach using winter snow season multispectral Landsat satellite imagery. To this end, we applied seven algorithms of spectral or texture-based transformation: K-means, maximum likelihood estimation, topsoil grain size index, soil brightness, normalized differential snow index, tasselled cap, and co-occurrence measures. The co-occurrence texture measure variance shows the optimal result of identifying damaged areas. The unique feature of our method is that it can differentiate damaged areas from the bare soil of cropland within a cold steppe region where the area damaged by oil production is mixed with bare (fallow) croplands that have a polygonal shape similar to well pads. Such similarities can lead to confusion in object-based classification. Using the co-occurrence measures, we found that from 1988 to 2015, damaged area is nearly three times as big in the peak period of the oil field development (2001 and 2009) as in 1988. Landscape fragmentation also peaked in 2001 and 2009. Our approach for this project is useful and cost effective regular monitoring of damages from oil production for both the Volga–Ural steppe region and other cold steppe regions.

Modelling habitat and planning surveillance using Landsat imagery: a case study using Imported Red Fire ants

This paper presents a Bayesian mixture model approach for detecting areas of habitat that are suitable for S. invicta infestation, aiding the ongoing surveillance for early detection of these exotic pests. We show that Landsat imagery is an affordable and valuable tool to assist in determining an informed surveillance strategy. In this paper, we use Landsat band 3 (visible red), Landsat band 6 (mid infrared) and a soil brightness index, in various combinations, to assess the probability that the area associated with each pixel is habitable terrain, either in a multivariate analysis, or as a univariate spatial temporal model. The multivariate analysis allows researchers to create meaningful clusters that reflect the sometimes complex combinations of conditions of conditions that form suitable habitat, rather then relying on single derived indices.

Remote sensing of species diversity using Landsat 8 spectral variables

The application of remote sensing in biodiversity estimation has largely relied on the Normalized Difference Vegetation Index (NDVI). The NDVI exploits spectral information from red and near infrared bands of Landsat images and it does not consider canopy background conditions hence it is affected by soil brightness which lowers its sensitivity to vegetation. As such NDVI may be insufficient in explaining tree species diversity. Meanwhile, the Landsat program also collects essential spectral information in the shortwave infrared (SWIR) region which is related to plant properties. The study was intended to: (i) explore the utility of spectral information across Landsat-8 spectrum using the Principal Component Analysis (PCA) and estimate alpha diversity (α-diversity) in the savannah woodland in southern Africa, and (ii) define the species diversity index (Shannon (H′), Simpson (D2) and species richness (S) – defined as number of species in a community) that best relates to spectral variability on the Landsat-8 Operational Land Imager dataset. We designed 90 m × 90 m field plots (n=71) and identified all trees with a diameter at breast height (DbH) above 10 cm. H′, D2 and S were used to quantify tree species diversity within each plot and the corresponding spectral information on all Landsat-8 bands were extracted from each field plot. A stepwise linear regression was applied to determine the relationship between species diversity indices (H′, D2 and S) and Principal Components (PCs), vegetation indices and Gray Level Co-occurrence Matrix (GLCM) texture layers with calibration (n=46) and test (n=23) datasets. The results of regression analysis showed that the Simple Ratio Index derivative had a higher relationship with H′, D2 and S (r2=0.36; r2=0.41; r2=0.24 respectively) compared to NDVI, EVI, SAVI or their derivatives. Moreover the Landsat-8 derived PCs also had a higher relationship with H′ and D2 (r2 of 0.36 and 0.35 respectively) than the frequently used NDVI, and this was attributed to the utilization of the entire spectral content of Landsat-8 data. Our results indicate that: (i) the measurement scales of vegetation indices impact their sensitivity to vegetation characteristics and their ability to explain tree species diversity; (ii) principal components enhance the utility of Landsat-8 spectral data for estimating tree species diversity and (iii) species diversity indices that consider both species richness and abundance (H′ and D2) relates better with Landsat-8 spectral variables.

THE ARCHAEOLOGICAL APPLICATION OF MULTI-SENSOR REMOTE SENSING DATA IN QIN YONGCHENG SITE

Abstract. Remote sensing archaeology bases on the use of remote sensing images and interpretation of the principles. The historic relics and sites are resulted from human activities and have constantly caused influences on the soil component, moisture content, temperature, vegetation growth and so on. Based on this thought, this paper proposed a new remote sensing model and approach that integrates optical and thermal infrared remote sensing data for archaeology. Taking the Qin Yongcheng site as the example, this method conducts a comprehensive analysis of key factors affecting archaeological application, that is, LST estimated with Landsat TM data, soil brightness, humidity and greenness obtained from GF-1 data, and then interprets the potential site targets. By conducting the field verification, it is shown that the interpreted potential sites are well consisted with the field investigations and have a high interpretation precision. It can provide a guide for further archaeological research.

Detection of water abundance in Baluran National Park with landsat satellite imagery analysis

Indonesia is one of the mega-biodiversity countries that have a great responsibility in maintaining the balance of the global climate and forest ecosystems. Drought causes shifting of ecosystems causing disturbances on animal life leading to death of species. Alongside fires in the savanna, drought is a recurrent problem in the park, which occurs every year. This study aims to detect the abundance of water by using satellite imagery in Baluran National Park (BNP). The research analyzed using Landsat satellite imagery ETM7 + in 1999 and 2010 and three (3) main factors that have great potential abundance of water, are: (1) plant density (GI = Greenness Index), (2) soil moisture (WI = Wetness Index), and (3) soil conditions (SBI = Soil Brightness Index). Three factors are summed and divided by three to get 5 levels of water abundance: 1) Very abundant, 2) Abundant, 3) Medium, 4) Few, and 5) Very little. The results showed that the abundance of water decreased between 1999 and 2010 for moderate conditions from 85% to 38%, if the abundance of low water (slightly) increased from 15% to 60%. The level of accuracy of the abundance of water in the field of more than 80% is exactly 91%. The extreme drought conditions will be very dangerous for the survival of flora and fauna in Baluran National Park that are in desperate need of water and potentially in danger of a fire. Construction of water reservoirs and water supply continuously using a water tank in the dry season is very necessary in the Baluran National Park.

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400–2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m−1 and an RPD of 2.21, and was more suitable for detecting salinization (10–20 dS·m−1) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

Climate Change Vulnerability Analysis of Baluran National Park

Every ecosystem has a different level of susceptibility to environmental disturbances it receives, both from natural factors or anthropogenic disturbance. National Park (NP) Baluran is one national park that has a representation of a complete ecosystem that includes upland forest ecosystems, lowland forests, coastal forests, mangroves, savanna and evergreen forest. The objective of this study is to get a formula calculation of vulnerability analysis of constant and dynamic factors. Baluran NP vulnerability assessment to climate change done by looking at the dynamic and fixed factors. Vulnerability remains a vulnerability factor to the condition of the original (control), whereas vulnerability is the vulnerability of the dynamic change factors which affected the condition from the outside. Constant Vulnerability (CV) in Baluran NP dominated resistant conditions (61%), meaning that the geomorphology and other fixed factors (slope and slope direction/aspect, then the condition in Baluran NP sufficiently resilient to climate change. Dynamic Vulnerability (DV) is the vulnerability of an area or areas that change because of pressure from external factors. DV is influenced by climatic factors (WI = Wetness Index), soil (SBI = Soil Brightness Index), and vegetation (GI = Greenness Index). DV in Baluran NP from 1999 to 2010 shifted from the original category of being (84.76%) and shifted to the susceptible (59.88%). The role of remote sensing for the analysis of raster digital system, while the geographic information system to display the results of cartographic maps.

Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution.

Spatial heterogeneity of soil biochar content affects soil quality and wheat growth and yield

Biochar (BC) is a carbonaceous material obtained by pyrolysis of organic waste materials and has been proposed as a soil management strategy to mitigate global warming and to improve crop productivity. Once BC has been applied to the soil, its imperfect and incomplete mixing with soil during the first few years and the standard agronomic practices (i.e. tillage, sowing) may generate spatial heterogeneity of the BC content in the soil, which may have implications for soil properties and their effects on plant growth. We investigated how, after two agronomic seasons, the spatial heterogeneity of olive-tree prunings BC applied to a vertisol affected soil characteristics and wheat growth and yield. During the second agronomic season and just before wheat germination, we determined the BC content in the soil by an in-situ visual categorization based on the soil darkening, which was strongly correlated to the BC content of the soil and the soil brightness. We found a high spatial heterogeneity in the BC plots, which affected soil characteristics and wheat growth and yield. Patches with high BC content showed reduced soil compaction and increased soil moisture, pH, electrical conductivity, and nutrient availability (P, Ca, K, Mn, Fe, and Zn); consequently, wheat had greater tillering and higher relative growth rate and grain yield. However, if the spatial heterogeneity of the soil BC content had not been taken into account in the data analysis, most of the effects of BC on wheat growth would not have been detected. Our study reveals the importance of taking into account the spatial heterogeneity of the BC content.