scholarly journals APLICAÇÃO DO MODELO LINEAR DE MISTURA ESPECTRAL PARA MAPEAMENTO DE PASTAGENS DEGRADADAS E SOLO EXPOSTO NA AMAZÔNIA

Nativa ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 352-360
Author(s):  
Fabrício Assis Leal ◽  
Maila Pereira de Almeida ◽  
Glória Da Silva Almeida Leal
Keyword(s):  
Hot Spot ◽  
Bare Soil ◽  
Spectral Unmixing ◽  
Landsat 8 ◽  
Soil Fraction ◽  
Degradation Rates ◽  
Degraded Pasture ◽  
Pasture Area ◽  
Spectral Mixture ◽  
Degraded Pastures

Este trabalho se ocupou no mapeamento e quantificação de pastagens degradadas/solo exposto em propriedades rurais de São Félix do Xingu/PA, em descobrir agrupamentos espaciais para taxas de pastagens degradadas, além de descrever o perfil das propriedades que estavam inseridas nestes agrupamentos. Foram utilizadas cenas do satélite Landsat 8 em 2017, classificadas para obtenção de áreas de pastagens. Depois foi utilizada análise de mistura espectral para obtenção da fração solo. Essa fração solo foi classificada e a primeira classe representou as áreas de pastagens degradadas/solo exposto. Essas áreas foram intersectadas com as propriedades rurais e depois classificadas em relação as taxas de pastagens degradadas/solo exposto. Sequencialmente as propriedades foram agrupadas espacialmente por meio de análise hot spot. A área total de pastagens em 2017 representou 49,8%, já as áreas de pastagens degradadas/solo exposto somaram 21.621 hectares (2,7%) e tiveram presentes em 3.643 (64%) das propriedades rurais (5.691), sendo que 3.004 delas possuíam tamanho de até 500 ha, sendo caracterizadas como pequenas propriedades. Foram três os agrupamentos hot spot encontrados que concentraram 982 propriedades rurais. Dessas 982 propriedades, 878 delas (89,4%) também estavam no grupo de até 500 ha. As pastagens degradadas e solo exposto estavam predominantemente concentradas nas pequenas propriedades rurais.Palavras-chave: análise espacial; imagem fração-solo; pecuária. LINEAR SPECTRAL UNMIXING FOR MAPPING DEGRADED PASTURES AND BARE SOIL IN THE AMAZON ABSTRACT:This work focused on the mapping and quantification of degraded pastures / bare soil in rural properties of São Félix do Xingu/PA, and in discovering spatial clusters for degradation rates, and describing the profile of the properties that were inserted in these clusters. We used scenes from the Landsat 8 satellite in 2017, classified to obtain pasture areas. Then spectral mixture analysis was used in the images to obtain the soil fraction. This fraction was classified and the first class represented the degraded pasture/exposed soil areas. These areas were intersected with the farms and then classified for degraded pasture/exposed soil rates. Sequentially the properties were spatially grouped by hot spot analysis. The total pasture area in 2017 represented 49.8%, while degraded pasture / exposed soil areas totaled 21,621 hectares (2.7%) and were present in 3,643 (64%) of rural properties (5,691), and 3,004 of them had a size of up to 500 ha, being characterized as small properties. There were three hot spot clusters found that concentrated 982 rural properties. Of these properties, 878 of them (89.4%) were also in the group of up to 500 ha. Degraded pastures/exposed soil were predominantly concentrated on small farms.Keywords: Spatial analysis, soil-fraction image, livestock.

scholarly journals Infiltração de água e resistência do solo à penetração em sistemas de cultivos integrados e em área de pastagem degradada

2020 ◽  
Vol 16 (5) ◽  
pp. 82-95
Author(s):  
Leonnardo Cruvinel Furquim ◽  
Epitácio José de Souza ◽  
Nelmício Furtado da Silva ◽  
Daniel Noe Coaguila Nuñez ◽  
Juliana Silva Rodrigues Cabral ◽  
...  
Keyword(s):  
Infiltration Rate ◽  
Water Infiltration ◽  
Integration System ◽  
Double Ring ◽  
Degraded Pasture ◽  
Pasture Area ◽  
Stocking Rates ◽  
Resistance To Penetration ◽  
Degraded Pastures

The objective of this study was to quantify water infiltration and resistance to penetration in a Latossolo Vermelho Distrófico (Typic Haplustox) cultivated with different land use systems and in a degraded pasture area. The studied areas are located in Rio Verde, state of Goiás (Brazil), where seven treatments were evaluated: T1 -Degraded; T2 -Fertilized pasture; T3 -Conventional; T4 -Crop-forest integrationsystem(CFI); T5 -Livestock-forest integrationsystem(LFI); T6 -crop-livestock-forest integration system (CLFI -hay); and T7 -crop-livestock-forest integration system (CLFI-silage). The water infiltration speed curves and the respective basic infiltration rate (BIR) values for the areas under study were determined. The infiltration of water into the soil was determined “in situ” by the double ring infiltrometer method and empirically through models proposed by Kostiakov and Kostiakov-Lewis. The soil resistance to penetration, up to a depth of 0.3 m, was performed using an impact penetrometer. The greatest infiltration in relation to time occurred in treatment T7. The highest values of BIR occurred in treatment T5. The model proposed by Kostiakov showed greater adjustment to the infiltration speed data obtained in the field. The lower resistance of the soil to penetration is provided by the diversity of species in the T4 treatment. The different management systems for recovering degraded pastures influenced the soil quality indicators studied, but there is a need for further studies to adjust the stocking rates in integrated systems in order not to return to the degradation scenario.

Landscape Change in the Steppe of Algeria South-West Using Remote Sensing

2018 ◽  
Vol 18 (1) ◽  
pp. 41-52
Author(s):  
Tayeb Sitayeb ◽  
Ishak Belabbes
Keyword(s):  
Remote Sensing ◽  
Social Systems ◽  
Bare Soil ◽  
Climatic Conditions ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Natural Ecosystems ◽  
Stipa Tenacissima ◽  
Steppe Species ◽  
Sparse Vegetation

Abstract Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil.

scholarly journals A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 231
Author(s):  
Can Trong Nguyen ◽  
Amnat Chidthaisong ◽  
Phan Kieu Diem ◽  
Lian-Zhi Huo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Areas ◽  
Near Infrared ◽  
Urban Landscape ◽  
Bare Soil ◽  
Critical Element ◽  
Landsat 8 ◽  
Climate Conditions ◽  
Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

scholarly journals Can explicit convection improve modelled dust in summertime West Africa?

2018 ◽  
Author(s):  
Alexander J. Roberts ◽  
Margaret J. Woodage ◽  
John H. Marsham ◽  
Ellie J. Highwood ◽  
Claire L. Ryder ◽  
...  
Keyword(s):  
West Africa ◽  
Land Surface ◽  
Dust Emission ◽  
Bare Soil ◽  
Explicit Representation ◽  
Saharan Dust ◽  
Cmip5 Models ◽  
Cold Pool ◽  
Moist Convection ◽  
Soil Fraction

Abstract. Global and regional models have large systematic errors in their modelled dust fields over West Africa. It is well established that cold pool outflows from moist convection (haboobs) can raise over 50 % of the dust over the Sahara and Sahel in summer, but parameterised moist convection tends to give a very poor representation of this in models. Here, we test the hypothesis that an explicit representation of convection improves haboob winds and so may reduce errors in modelled dust fields. The results show that despite varying both grid-spacing and the representation of convection there are only minor changes in dust aerosol optical depth (AOD) and dust mass loading fields between simulations. In all simulations there is an AOD deficit over the observed central Saharan dust maximum and a high bias in AOD along the west coast: both features consistent with many climate (CMIP5) models. Cold pool outflows are present in the explicit simulations and do raise dust. Consistent with this there is an improved diurnal cycle in dust-generating winds with a seasonal peak in evening winds at locations with moist convection that is absent in simulations with parameterised convection. However, the explicit convection does not change the AOD field significantly for several reasons. Firstly, the increased windiness in the evening from haboobs is approximately balanced by a reduction in morning winds associated with the breakdown of the nocturnal low-level jet (LLJ). Secondly, although explicit convection increases the frequency of the strongest winds, these are still weaker than observed, especially close to the observed summertime Saharan dust maximum: this results from the fact that although large mesoscale convective systems (and resultant cold pools) are generated, they have a lower frequency than observed and haboob winds are too weak. Finally, major impacts of the haboobs on winds occur over the Sahel, where, although dust uplift is known to occur in reality, uplift in the simulations is limited by a seasonally constant bare soil fraction in the model, together with soil moisture and clay fractions which are too restrictive of dust emission in seasonally-varying vegetated regions. For future studies, the results demonstrate 1) the improvements in behaviour produced by the explicit representation of convection, 2) the value of simultaneously evaluating both dust and winds and 3) the need to develop parameterisations of the land surface alongside those of dust-generating winds.

scholarly journals Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

2022 ◽  
Vol 14 (2) ◽  
pp. 348
Author(s):  
Yashon O. Ouma ◽  
Lone Lottering ◽  
Ryutaro Tateishi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Degradation ◽  
Bare Soil ◽  
Landsat 8 ◽  
Arid Environments ◽  
Degradation Index ◽  
Erosivity Index ◽  
Loss Index ◽  
Very High

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better that the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.

Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2019 ◽  
pp. 1372-1382
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

Linear spectral mixture modelling of arctic vegetation using ground spectroradiometry

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Anna Mikheeva ◽  
Anton Novichikhin ◽  
Olga Tutubalina
Keyword(s):  
Satellite Imagery ◽  
Ground Level ◽  
Spectral Unmixing ◽  
Atmospheric Effects ◽  
Arctic Vegetation ◽  
Mixture Modelling ◽  
New Information ◽  
Level Data ◽  
Multispectral Satellite Imagery ◽  
Spectral Mixture

ABSTRACTAn experimental linear mixture modelling using ground spectroradiometric measurements in the Kola Peninsula, Russia has been carried out to create a basis for mapping vegetation and non-vegetation components in the tundra-taiga ecotone using satellite imagery. We concentrated on the ground level experiment with the goal to use it further for the classification of multispectral satellite imagery through spectral unmixing. This experiment was performed on the most detailed level of remote sensing research which is free from atmospheric effects and easy to understand. We have measured typical ecotone components, including Cetraria nivalis, Betula tortuosa, Empetrum nigrum, Betula nana, Picea abies and rocks (nepheline syenite). The result of the experiment shows that the spectral mixture is indeed formed linearly but different components have different influence. Typical spectral thresholds for each component were found which are significant for vegetation mapping. Spectral unmixing of ground level data was performed and accuracy was estimated. The results add new information on typical spectral thresholds which can potentially be applied for multispectral satellite imagery when upscaling from high resolution to coarser resolution.

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