Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

Soil color and mineralogy are used as diagnostic criteria to distinguish different soil types. In the literature, 350–2500 nm spectra were successfully used to predict soil color and mineralogy, but these attributes currently are not mapped for most Brazilian soils. In this paper, we provided the first large-extent maps with 30 m resolution of soil color and mineralogy at three depth intervals for 850,000 km2 of Midwest Brazil. We obtained soil 350–2500 nm spectra from 1397 sites of the Brazilian Soil Spectral Library at 0–20 cm, 20–60, and 60–100 cm depths. Spectra was used to derive Munsell hue, value, and chroma, and also second derivative spectra of the Kubelka–Munk function, where key spectral bands were identified and their amplitude measured for mineral quantification. Landsat composites of topsoil and vegetation reflectance, together with relief and climate data, were used as covariates to predict Munsell color and Fe–Al oxides, and 1:1 and 2:1 clay minerals of topsoil and subsoil. We used random forest for soil modeling and 10-fold cross-validation. Soil spectra and remote sensing data accurately mapped color and mineralogy at topsoil and subsoil in Midwest Brazil. Hematite showed high prediction accuracy (R2 > 0.71), followed by Munsell value and hue. Satellite topsoil reflectance at blue spectral region was the most relevant predictor (25% global importance) for soil color and mineralogy. Our maps were consistent with pedological expert knowledge, legacy soil observations, and legacy soil class map of the study region.

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LAI Retrieval Based on PROSPECT-SAILH Model from Multi-Angular Data of WiDAS Imaging System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.5697 ◽

2012 ◽

Vol 518-523 ◽

pp. 5697-5703

Author(s):

Zhao Yan Liu ◽

Ling Ling Ma ◽

Ling Li Tang ◽

Yong Gang Qian

Keyword(s):

Remote Sensing ◽

Imaging System ◽

Remote Sensing Data ◽

Canopy Reflectance ◽

Area Index ◽

Sensing Data ◽

Spectral Bands ◽

Sensing Technology ◽

Leaf Optical Properties ◽

Angular Data

The aim of this study is to assess the capability of estimating Leaf Area Index (LAI) from high spatial resolution multi-angular Vis-NIR remote sensing data of WiDAS (Wide-Angle Infrared Dual-mode Line/Area Array Scanner) imaging system by inverting the coupled radiative transfer models PROSPECT-SAILH. Based on simulations from SAILH canopy reflectance model and PROSPECT leaf optical properties model, a Look-up Table (LUT) which describes the relationship between multi-angular canopy reflectance and LAI has been produced. Then the LAI can be retrieved from LUT by directly matching canopy reflectance of six view directions and four spectral bands with LAI. The inversion results are validated by field data, and by comparing the retrieval results of single-angular remote sensing data with multi-angular remote sensing data, we can found that the view angle takes the obvious impact on the LAI retrieval of single-angular data and that high accurate LAI can be obtained from the high resolution multi-angular remote sensing technology.

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Analysis of agronomic drought over North Africa using remote sensing satellite data

10.5194/egusphere-egu21-14780 ◽

2021 ◽

Author(s):

Mehrez Zribi ◽

Simon Nativel ◽

Michel Le Page

Keyword(s):

Remote Sensing ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

North Africa ◽

Vegetation Cover ◽

Remote Sensing Data ◽

Study Region ◽

Semi Arid Region ◽

Anomaly Index

This paper aims to analyze the agronomic drought in a highly anthropogenic &#160;semi-arid region, North Africa. In the context of the Mediterranean climate, characterized by frequent droughts, North Africa is particularly affected. Indeed, in addition to this climatic aspect, it is one of the areas most affected by water scarcity in the world. Thus, understanding and describing agronomic drought is essential. The proposed study is based on remote sensing data from TERRA-MODIS and ASCAT satellite, describing the dynamics of vegetation cover and soil water content through NDVI and SWI indices. Two indices are analyzed, the Vegetation Anomaly Index (VAI) and the Moisture Anomaly Index (MAI). The dynamics of the VAI is analyzed for different types of regions (agircultural, forest areas). The contribution of vegetation cover is combined with the effect of soil water content through a new drought index combining the VAI and MAI. A discussion of this combination is proposed on different study areas in the study region. It illustrates the complementarity of these two informations in analysis of agronomic drought.

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Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face

Remote Sensing ◽

10.3390/rs10101518 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1518 ◽

Cited By ~ 3

Author(s):

Stephane Boubanga-Tombet ◽

Alexandrine Huot ◽

Iwan Vitins ◽

Stefan Heuberger ◽

Christophe Veuve ◽

...

Keyword(s):

Remote Sensing ◽

Spectral Resolution ◽

Remote Sensing Data ◽

Field Measurements ◽

Thermal Infrared ◽

Field Studies ◽

Hyperspectral Data ◽

Spectral Bands ◽

Remote Sensing Systems ◽

Viewing Perspective

Remote sensing systems are largely used in geology for regional mapping of mineralogy and lithology mainly from airborne or spaceborne platforms. Earth observers such as Landsat, ASTER or SPOT are equipped with multispectral sensors, but suffer from relatively poor spectral resolution. By comparison, the existing airborne and spaceborne hyperspectral systems are capable of acquiring imagery from relatively narrow spectral bands, beneficial for detailed analysis of geological remote sensing data. However, for vertical exposures, those platforms are inadequate options since their poor spatial resolutions (metres to tens of metres) and NADIR viewing perspective are unsuitable for detailed field studies. Here, we have demonstrated that field-based approaches that incorporate thermal infrared hyperspectral technology with about a 40-nm bandwidth spectral resolution and tens of centimetres of spatial resolution allow for efficient mapping of the mineralogy and lithology of vertical cliff sections. We used the Telops lightweight and compact passive thermal infrared hyperspectral research instrument for field measurements in the Jura Cement carbonate quarry, Switzerland. The obtained hyperspectral data were analysed using temperature emissivity separation algorithms to isolate the different contributions of self-emission and reflection associated with different carbonate minerals. The mineralogical maps derived from measurements were found to be consistent with the expected carbonate results of the quarry mineralogy. Our proposed approach highlights the benefits of this type of field-based lightweight hyperspectral instruments for routine field applications such as in mining, engineering, forestry or archaeology.

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Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data

Geocarto International ◽

10.1080/10106049.2017.1347207 ◽

2017 ◽

Vol 33 (12) ◽

pp. 1281-1306 ◽

Cited By ~ 35

Author(s):

Amin Beiranvand Pour ◽

Mazlan Hashim ◽

Yongcheol Park ◽

Jong Kuk Hong

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Sensing Data ◽

Spectral Bands

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ANALYSIS OF SOIL DEGRADATION AND DESERTIFICATION IN SOUTHERN BALKASH DESERT BASED ON GIS

REPORTS ◽

10.32014/10.32014/2020.2518-1483.30 ◽

2020 ◽

Vol 2 (330) ◽

pp. 41-48

Author(s):

A.G. Gabdykadyr ◽

G.T. Issanova ◽

Y.Kh. Kakimzhanov ◽

Long Ma

Keyword(s):

Remote Sensing ◽

Land Cover ◽

River Flow ◽

Remote Sensing Data ◽

Geological Structure ◽

Anthropogenic Factors ◽

Economic Activities ◽

Spectral Bands ◽

Indirect Sign ◽

Tasseled Cap Transformation

Desertification and degradation provide a clear picture of global environmental and socio-economic issues. Most of Kazakhstan is located in a desert region, including the suburbs of South Balkhash. The reason is that desertification of the region has a strong influence on natural and anthropogenic factors. To consider the geomorphological state of the region and the problem of desertification of the territory, it is necessary to determine the importance of the process of relief of geological structure and relief of tectonics. In recent years, the environmental situation in Balkhash has deteriorated sharply not only as a result of river flow regulation, but also as a result of non-commercial economic activities. Therefore, it is very important to assess the situation of desertification and degradation in the Balkhash region. Desert vegetation has been identified, since information in the spectral range is often insufficient to describe the state of plants, plant indices often develop by combining two or more spectral bands. Land cover index is the percentage of vegetation over a given surface area. Remote sensing information was used to detect the entire land cover. Remote sensing with time and space limitations is widely used to classify vegetation cover. In this work, the proportion of vegetation was estimated by NDVI. The proportion of land cover is based on the relationship between NDVI (NDVIS) and NDVI (NDVIV) in the soil. Using the NDVI index, land cover zones were determined based on satellite images of 2006 and Landsat-5 from 2011. TCT (Tasseled Cap Transformation) coefficients are used in the widest range of problems solved using Earth remote sensing data: from recognition of the coastline of water bodies to determination of forest disturbances. Stressful vegetation may be an indirect sign of the presence of salt in soils. Saline soils are usually characterized by poorly planted areas. A normalized differential salinity index (NDSI) was also determined.

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Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields

Computational Methods for Agricultural Research ◽

10.4018/978-1-61692-871-1.ch004 ◽

2011 ◽

pp. 50-72

Author(s):

Luciana Romani ◽

Elaine de Sousa ◽

Marcela Ribeiro ◽

Ana de Ávila ◽

Jurandir Zullo ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Sugar Cane ◽

Computational Models ◽

Fractal Theory ◽

Remote Sensing Data ◽

Real Data ◽

Multiple Time ◽

Climate Data ◽

Sustainable Usage

This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and remote sensing images. This kind of data has been used for researching on climate changes, as well as to help on improving yield forecasting of agricultural crops and increasing the sustainable usage of the soil. The authors present three techniques based on the Fractal Theory, data streams and time series mining: the FDASE algorithm, to identify correlated attributes; a method that combines intrinsic dimension measurements with statistical analysis, to monitor evolving climate and remote sensing data; and the CLIPSMiner algorithm applied to multiple time series of continuous climate data, to identify relevant and extreme patterns. The experiments with real data show that data mining is a valuable tool to help agricultural entrepreneurs and government on monitoring sugar cane areas, helping to make the production more useful to the country and to the environment.

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Linking glacial lake expansion with glacier dynamics: An assessment of the South Lhonak lake, Sikkim Himalaya

10.5194/egusphere-egu2020-685 ◽

2020 ◽

Author(s):

Saurabh Kaushik ◽

Pawan Kumar Joshi ◽

Tejpal Singh ◽

Anshuman Bhardwaj

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Remote Sensing Data ◽

Surface Flow ◽

Meteorological Station ◽

Glacial Lake ◽

Climate Data ◽

Glacier Dynamics ◽

Heterogeneous Behavior ◽

Multitemporal Remote Sensing

The Himalayan Cryosphere is imperative to the people of south and central Asia owing to its water availability, hydropower generation, environmental services, eco-tourism, and influences on overall economic development of the region. Additionally, this influences the energy balance of the earth and contributes significantly to the sea level rise. Therefore Himalayan Cryosphere remains center of attraction for scientific community.&#160; Glacier dynamics, seasonal snow and glacial lakes are studied at various scales using a combination of remote sensing and field observations. The existing literature reveals heterogeneous behavior of Himalayan glaciers which is largely influenced by climate change, debris cover and presence of glacial lake at the terminus. There are very limited studies that attempt to comprehend glacier dynamics and lake expansion in the Eastern Himalayan region. Therefore the present study aims to demonstrate link between glacier dynamics and lake expansion of South Lhonak glacier which is situated in the northern Sikkim. Multitemporal remote sensing data (Landsat, 1979-2019) and climate data (1990-2017) observed at Gangtok meteorological station are used in the study. The results reveal that the lake has expanded with a rate of 0.026 km2 yr-1 during the last four decades. The preliminary results show strongly imbalanced state of glacier, as glacier has deglaciated (area and length), and surface flow velocity and ice thickness have reduced significantly. The statistical analysis (Mann Kendall and Sens slope) of climate data measured at Gangtok meteorological station shows an accelerated trend of mean maximum (0.031&#176;C yr-1) and mean minimum (0.043&#176;C yr-1) temperatures (95% confidence interval). Whereas, no significant trend in total annual precipitation was observed. Inference can be drawn from study that glacier slow down and retreat contribute significantly to the glacial lake expansion under the influence of climate change, such lake expansion pose anticipated risk of glacial lake outburst in the region.

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Mapping Flood-Based Farming Systems with Bayesian Networks

Land ◽

10.3390/land9100369 ◽

2020 ◽

Vol 9 (10) ◽

pp. 369 ◽

Cited By ~ 1

Author(s):

Issoufou Liman Harou ◽

Cory Whitney ◽

James Kung’u ◽

Eike Luedeling

Keyword(s):

Remote Sensing ◽

Bayesian Network ◽

Spatial Data ◽

Probabilistic Reasoning ◽

Expert Knowledge ◽

Agricultural Research ◽

Remote Sensing Data ◽

Farming Systems ◽

Spatially Explicit ◽

Sensing Data

Many actors in agricultural research, development, and policy arenas require accurate information on the spatial extents of cropping and farming practices. While remote sensing provides ways for obtaining such information, it is often difficult to distinguish between different types of agricultural practices or identify particular farming systems. Stochastic system behavior or similarity in the spectral signatures of different system components can lead to misclassification. We addressed this challenge by using a probabilistic reasoning engine informed by expert knowledge and remote sensing data to map flood-based farming systems (FBFS) across Kisumu County in Kenya and the Tigray region in Ethiopia. Flood-based farming is an important form of agricultural production employed in regions with seasonal water surplus, which can be harvested and used to irrigate crops. Geographic settings for FBFS vary widely in terms of hydrology, vegetation, and local practices of agronomic flooding. Agronomic success is often difficult to anticipate, because the timing and amount of flooding usually cannot be precisely predicted. We generated a Bayesian network model to describe the FBFS settings of the study regions. We acquired three years (2014–2016) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra spectral data as eight-day composite time series and elevation data from the Shuttle Radar Topography Mission (SRTM) to compute 10 spatial data metrics corresponding to 10 of the 17 Bayesian network nodes. We used the spatial data metrics in a fully probabilistic framework to generate the 10 spatial data nodes. We then used these as inputs for the probabilistic model to generate prior and posterior spatial estimates for specific metrics along with their spatially explicit uncertainties. We show how such an approach can be used to predict plausible areas for FBFS based on several scenarios. We demonstrate how spatially explicit information can be derived from remote sensing data as fuzzy quantifiers for incorporating uncertainties when mapping complex systems. The approach achieved a remarkably accurate result in both study areas, where 84–90% of various FBFS fields sampled were correctly mapped as having a high chance of being suitable for the practice.

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Enhancement and Application of WorldView-2 to Geological Interpretation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1010-1012.1237 ◽

2014 ◽

Vol 1010-1012 ◽

pp. 1237-1242 ◽

Cited By ~ 1

Author(s):

Jia Jing Zhou ◽

Shu Fang Tian ◽

Na Wang ◽

Xiao Hu

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Good Effect ◽

Geological Interpretation ◽

Spectral Bands ◽

Texture Information ◽

Kunlun Mountain ◽

West Kunlun

By comparing WorlView-2 with other remote sensing data in the characteristics of spectral bands and spatial resolution, we found that all the eight bands of WorldView-2 are sensitive to lithology and helpful to distinguish them; besides, WorldView-2 provides a richer texture information with a high spatial resolution of 0.46m, which is also very important in geological interpretation of remote sensing. Therefore, WorldView-2 data has a strong advantage in geological applications. In the geological interpretation of the Kezile area in West Kunlun Mountain, different enhancement methods based on the spectrum, texture and geomorphology/vegetation were applyed to enhance the lithology information of WorldView-2 image, and it achieved a good effect. With the enhanced images of Kezile area, we subdivided the Jurassic, Cretaceous, Paleogene and Neogene into lithologies in detail, and completed the remote sensing geological interpretation map.

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Time-series modelling of dengue incidence in the Mekong Delta region of Viet Nam using remote sensing data

Western Pacific Surveillance and Response ◽

10.5365/wpsar.2018.9.2.012 ◽

2020 ◽

Vol 11 (1) ◽

pp. 13-19

Author(s):

Nga TT Pham ◽

Cong Nguyen ◽

Maria Ruth Pineda-Cartel

Keyword(s):

Remote Sensing ◽

Time Series ◽

Mekong Delta ◽

Remote Sensing Data ◽

Viet Nam ◽

Data Sets ◽

Delta Region ◽

Study Region ◽

Monthly Data ◽

Sensing Data

Objective: This study aims to enhance the capacity of dengue prediction by investigating the relationship of dengue incidence with climate and environmental factors in the Mekong Delta region (MDR) of Viet Nam by using remote sensing data. Methods: To produce monthly data sets for each province, we extracted and aggregated precipitation data from the Global Satellite Mapping of Precipitation project and land surface temperatures and normalized difference vegetation indexes from the Moderate Resolution Imaging Spectroradiometer satellite observations. Monthly data sets from 2000 to 2016 were used to construct autoregressive integrated moving average (ARIMA) models to predict dengue incidence for 12 provinces across the study region. Results: The final models were able to predict dengue incidence from January to December 2016 that concurred with the observation that dengue epidemics occur mostly in rainy seasons. As a result, the obtained model presents a good fit at a regional level with the correlation value of 0.65 between predicted and reported dengue cases; nevertheless, its performance declines at the subregional scale. Conclusion: We demonstrated the use of remote sensing data in time-series to develop a model of dengue incidence in the MDR of Viet Nam. Results indicated that this approach could be an effective method to predict regional dengue incidence and its trends.

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