Soil Brightness Index Research Articles

Study on the influencing factors and the spatiotemporal heterogeneity of Urban Heat Island effect in Nanchang City of China

ABSTRACT Although some researchers have explored spatiotemporal characteristics of the land surface temperature (LST) and analyzed how the influencing factors impact the LST, the comprehensive influencing factors and specific spatial relationships that explore the Urban Heat Island (UHI) effect from long time scale have rarely received widespread attention. Taking Nanchang as an example, this paper first uses Landsat data to retrieve the LST in years 2000, 2010, and 2019. Then, the Geographically Weighted Regression (GWR) model is utilized to investigate the various influencing factors. The results showed as follows: (1) The UHI effect was intensified in the past 20 years in Nanchang City. From 2000 to 2019, the “heat island” increased from 63.06% to 64.07%, while the “green island” decreased from 36.94% to 35.93%. (2) The spatiotemporal anisotropy of the UHI effect was significant. (3) The GWR model could better reveal the spatial heterogeneity of the factors affecting the UHI effects. The relationship between Soil Brightness Index (NDSI), Normalized Difference Built-up Index (NDBI), and Night-lighting (NL) and the relative UHI effect intensity had significant spatial non-stationarity. This study can provide some references from reasonable urban planning to alleviate the urban heat island.

Remote Sensing Analysis of Agricultural Drone

Farmers have more requirements for the completion of cultivations. Remote sensing is a big technology for reducing this requirement. Now, we need an organic spraying system at a low cost. We have two methods, first one neural network algorithm of quantum geographic information system (QGIS) and another one global positioning system (GPS) with drone. This paper describes the analysis of drone remote sensing using the normalized difference vegetation index (NDVI)/Near-infrared band (NIR) sensor in a multispectral view of agricultural land. NIR and NDVI images had water content values and precision values which is mixed in managing water resources. NDVI sensors are loaded to produce high-density images. Real-time monitoring coupled in NIR imaging geometrically and radiometrically adjusted to measure temperature. Multispectral and hyperspectral views had used for analyzing the tested data. Standard irrigation level is 60% to produce the plant growing. Irrigation techniques followed the treatment of the plant within continuous data per second. The implemented view focused only on growth controlling of plant in-depth irrigation between 30 and 90 cm in 60% deviation. NDVI, green normalized difference vegetation index (GNDVI), soil brightness index (SBI), green vegetation index (GVI), degree of yellow vegetation index (YVI), nitrogen sufficiency index (NSI), perpendicular vegetation index (PVI), transformed vegetation index (TVI), soil adjusted vegetation index (SAVI) and vegetation condition index (VCI) vegetation indices are used to the correlation of plant growth control with managing leaf strength and import python packages display the Vegetation various Real-time value in QGIS. Correlation of plant growth p ≤0.01, r = 0.77 and − 0.77 with conductance. It measured degree and demonstrated GPS view using irrigation techniques to control water stress. It had used to estimate the leaf conductance rate with the variation of atmospherically changing. It can calculate real-time leaf stress analysis. This report provided a drone survey analysis of compost percentage and vegetation indices of agricultural land.

Integration of satellite, UAV and field data for soil analysis in precision viticulture

In precision viticulture, the knowledge of the spatial distribution of soil characteristics is necessary to optimize the agro-cultural practices. In this work a multi-parametric approach, based on different satellite sensors and Unmanned Aerial Vehicle (UAV), is evaluate to detect areas with similar soils characteristics. The Misra Soil Brightness Index (MSBI) and the Normalized Difference Vegetation Index (NDVI) spectral indices are extracted from Sentinel-2 imagery for main distribution of soil and of vegetation characteristics. COmbined Spectral Response Index (COSRI) and Soil Background Line (SBL) spectral indices are calculated from Pléiades data for a more accurate delimitation of soil homogenous areas. Digital Elevation Model, Ortophoto and slope maps retrieval of UAV data are also included within a Geographical Iinformation System for morphological analysis. The analysis of such different thematic layers allow to set up a dedicated and specific grid for soil sampling on each analysed vineyard unit. Finally, spectroradiometric and chemical (Electric Conductivity and pH) laboratory measurements of sampled soils allow to obtain the Normalized Differential Salinity Index (NDSI) and EC map. In accordance with satellite analysis, such analysis delimit soil heterogeneity spatial distribution highlighting areas potentially subjected to salinity stress and, consequently, to a different grapes production and maturity trend.

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.

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.

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.

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.

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.

Quantitative inversion of soil salinity and analysis of its spatial pattern in agricultural area in Shihezi of Xinjiang

Salinization has been a serious threat to continuous farming and grain production security, therefore, the rapid inversion of soil salinity is particularly important. The monitoring and prevention of soil salinization are of great significance to agricultural sustainable development in Shihezi, Xinjiang. In this study, we took the remote sensing image of Landsat-8OLI and the soil salinity of 72 samples measured in the field as a data base. Then we introduced the soil brightness index, soil moisture index, soil cover degree index and soil radiation level index. Finally we built feature remote sensing index to invert quantitatively the soil salinity of Land Reclamation District of Shihezi in Xinjiang, and analyzed the spatial pattern of it. The results showed that: 1 The accuracy of characteristic remote sensing index value of image pixel to invert soil salinity which corresponded to the ground was better. The inversion method was feasible. REE and RMSM, the precision validation index of inversion model were 3.07 and 0.34 respectively, and retrieval accuracy was 66%; 2 The distribution map of soil salinity with obvious grades and clear levels presented the pattern of‘one stripe and one region??. One stripe referred to the stripe with high salinization formed by Manas basin,mostly located in the alluvial plains, low- lying area, and reservoir areas. And one region referred to the region with serious salinization caused by soil texture. 3 In the farming areas,the non- salinization cotton area accounted for 14.02%, the mild salinization cotton area accounted for 21.38%, the moderate salinization cotton area accounted for 21.27%, severe salinization cotton area accounted for 29.99%, and saline accounted for 13.30% in cotton area.Some 35.40% of the cotton field was suitable for cultivation and 64.60% of the cotton field which needed to be improved. It is urgent to control soil salinization of farming areas.

Remote Sensing and GIS Based Suitability Modeling of Rubia Cordifolia L. in West Kameng District of Arunachal Pradesh [India

Rubia cordifolia L. also known as Indian madder in English and Manjista in Hindi is a tonic, antidysentric, antiseptic and the roots are used internally in the treatment of abnormal uterine bleeding, internal and external haemorrhage, bronchitis, rheumatism and stones in the kidney, bladder and gall. A transact survey was carried out mainly in the secondary forests along the roads and in the vicinity of the settlements. A total of 97 GPS points of the Rubia cordifolia were recorded in the study area. Digital Elevation Model [DEM] of the Shuttle Radar Topographic Mission [SRTM] was downloaded from the seamless server of United States Department of Agriculture [USDA]. It was used for deriving topographic parameters like altitudinal zones, slope angle, slope aspect, generic landforms and Topographic Wetness Index [TWI] which are important for the modeling of plant. Linear Image Scanning Sensor [LISS] III was used for deriving land use / land cover, Normalized Difference Vegetation Index [NDVI], Normalized Differential Water Index NDWI], Soil Brightness Index [SBI], etc. Besides, the climatic parameters like annual average rainfall, annual average temperature and annual average humidity and others like road and settlement distance were used. The whole raster data cube was submitted to Spatial Multi-Criteria Evaluation [SMCE] module of ILWIS 3.4 [GIS software developed by ITC, Enschede, and The Netherlands] for suitability modeling of targeted species. The GPS points of the plant recorded in the field were used for verifying the results. The result was satisfactory as about 89% of GPS points fall under the moderately suitable and highly suitable categories together that constitutes about 29% of the total area. This zone is the potential area of the occurrences and regeneration of Rubia cordifolia in the natural habitat.

A method aimed at automatic landslide extraction based on background values of satellite imagery

The vegetation and soil layers of land surfaces become damaged during landslides. When viewed in remote-sensing images, these post-landslide vegetation and soil layers appear significantly different from the surrounding background. This article proposes an integrated method for calculating remote-sensing indices to extract background information of areas that have undergone landslides by increasing the spectral difference between bare land and other surface features, such as vegetation, areas of shadow, settlements, and waterbodies. Based on the background values of satellite images, the indices include vegetation index, soil brightness index, the first component (PC1) of the principal component transformation of the image, and also terrain data. An automatic extraction model of landslides is proposed based on an improved multi-peak histogram threshold algorithm and mathematical morphology filtering algorithm. Landslide information is extracted and verified using an experimental system built by the Environment for Visualizing Images (ENVI) and Interface Description Language (IDL). The experiment includes manually and automatically recognized landslides. The experimental results show that the proposed method not only effectively removes non-landslide and other interference information, but also significantly improves the efficiency and accuracy of the automated landslide extraction process.

Classification of tree species using high-resolution QuickBird-2 satellite images in the valley of Ui-dong in Bukhansan National Park

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as <TEX>$Pinus$</TEX> <TEX>$densiflora-P.$</TEX> <TEX>$rigida$</TEX> and <TEX>$Quercus$</TEX> <TEX>$mongolica-Q.$</TEX> <TEX>$serrata$</TEX>, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of <TEX>$Pterocarya$</TEX> <TEX>$stenoptera$</TEX>, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. <TEX>$P.$</TEX> <TEX>$densiflora-P.$</TEX> <TEX>$rigida$</TEX> and <TEX>$Q.$</TEX> <TEX>$mongolica-Q.$</TEX> <TEX>$serrata$</TEX> were classified as the same species because they did not show significant differences in terms of spectral patterns.

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.

Prediction of spatial ET-fluxes using remote sensing and field data of selected areas in the Eastern Part of Abu Dhabi, United Arab Emirates

Evapotranspiration (ET) is a major source of water depletion in arid and semi-arid environments; and it is a poorly quantified variable in the hydrological cycle. Remote sensing has the potential application to quantify this variable especially at large scale. The present study reports methodology useful to determine whether derived variables from remotely sensed data, such as vegetation and soil brightness indices, could be used to predict ET. To achieve this goal, various regression analyses were conducted between data derived from satellites, field meteorological stations, and ET values. Selected sub-scenes of Landsat Enhanced Thematic Mapper images free of cloud were used to derive Normalized Difference Vegetation Index (NDVI) and Soil Brightness Index using ER-Mapper and JMP software packages. From the obtained relationship between NDVI and ET, it was observed that ET increases sharply with increase in NDVI. The predicted ET results obtained from the multiple regression functions of field ET, NDVI, solar radiation, wind velocity, and/or temperature are comparable with the ET values obtained by Penman-Monteith method. The results showed that a remotely sensed vegetation index could be used, indirectly, to determine ET values. However, there is still considerable work to be done before simple and full automated extraction of ET from the reported methods can be achieved for large-scale applications.

Detection of Levee Slides Using Commercially Available Remotely Sensed Data

Surficial failures or slides in the levees along the Mississippi River have been recorded since 1964. The current method of detecting these slides involves a physical survey that is neither time nor cost efficient. This research was designed to evaluate the potential of commercially available high-resolution multispectral and hyperspectral imagery as a tool to detect levee slides. QuickBird, IKONOS, and CASI II imagery were used to evaluate a 20-mi (32-km) levee reach near Gunnison in Bolivar County, Mississippi. Field investigations were combined with image processing techniques including pan-sharpening, ISODATA clustering, Spectral Angle Mapper classification, and Tasseled Cap transformation. Field observations and processing of both multispectral and hyperspectral imagery suggest that it is possible to detect slides by the nature of the vegetation and land use in the slide area. Certain types of plants were found in slide-affected areas and appear to be a good indicator of slides in the field and in imagery. QuickBird and IKONOS imagery are both suitable for detecting slides by visual inspection and for monitoring recorded slides. Hyperspectral CASI II imagery can be used to identify slide indicative vegetation. Soil Brightness Index and Greenness Index obtained from a Tasseled Cap transformed IKONOS imagery was used to develop a slide detection model. The slide detection model did not determine the exact location of all slides but was able to narrow the search area for slides.

Detection and delineation of various desert terrain features using Landsat-TM derived image transforms

Globally, deserts cover an estimated 36·3 per cent of the earth's land surface. Of this, 0·32 million km 2 occurs in India. Timely and reliable information on various natural resources of deserts and the processes operating within the region are prerequisites for combating desertification and for checking further encroachment into adjoining fertile lands. Spaceborne multispectral data offer a great potential in providing such information. Landsat-TM data over part of Jodhpur district, Rajasthan was used to generate various spectral indices such as principal components transform, perpendicular vegetation index and soil brightness index. Their potential in delineating various features is evaluated. The methodology and merits and limitations of each transform are discussed in detail.

Saline degradation of Indian agricultural lands : A case study in Khambhat Taluka, Gujarat state (India), using satellite remote sensing

Abstract In certain agricultural fields of Khambhat Taluka in Gujarat State, the salinity has increased considerably rendering the land completely infertile. The occurrence of salinity in this area can be attributed partly to subsurface sea‐water ingress and partly to improper land and water management practices prior to implementation of irrigation. Landsat MSS or TM and IRS IA LISS II data was used to test the feasibility of delineating saline soils by both visual image interpretation and digital analysis. The study of saline soils using multi‐temporal Landsat images of the year 1977, 1983, and 1987, indicated an evident increase in saline areas in past few years. The Soil Brightness Index (SBI) generated from the IRS‐IA data by the application of MSS equivalent coefficients brought out different categories of soil degradation. The supervised classification scheme aided in generating various salinity levels. The analysis of the soil samples of the above area exhibited increasing values of Electrical Con...