scholarly journals The monitoring of southern chernozem soil humus content with using multispectral satellite images Landsat: spatial and temporal aspects

2016 ◽  
Vol 17 (1-2) ◽  
pp. 22-30
Author(s):  
S. G. Chornyy ◽  
D. A. Abramov
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Satellite Images ◽  
Humus Content ◽  
Spectral Characteristics ◽  
Landsat 8 ◽  
Erosion Processes ◽  
Landsat 7 ◽  
Spatio Temporal ◽  
Spectral Channels

For rational use of soils it is necessary to possess exact information on soil properties. The traditional methods of monitoring of soils and (or) their separate properties based on local, one-time supervision don’t give an adequate assessment of a current state of a soil cover it should be noted. Transition to spatio-temporal methods with use of modern geoinformation and space technologies is necessary. Remote satellite methods of soil monitoring gain fast distribution, owing to the efficiency, a certain objectivism and relative low cost now, and also because of unique opportunities of one-time coverage by the images received from big height, enough territories, big on the area. For the development of remote monitoring chernozems southern used materials of multispectral scanning multispectral camera ETM + ( «Enhanced Thematic Mapper Plus»), which is on board the satellite «Landsat-7» (data of 2006, 2010, 2012) and OLI («Operational Land Imager»), which is on board the satellite «Landsat-8»(data 2015). The information obtained from them is unified from the point of view of preservation of geometry, calibration, a covering, spectral characteristics, quality of the image and availability of data, despite various carriers of devices ETM+ and OLI. The composite image which has been received from three cloudless satellite images of spring of 2012 (three terms of shooting – 21.04, 30.04, 05.05) has allowed to make the correlation analysis of extent of influence of maintenance of organic matter in a layer of soil of 0–10 cm at a brightness with various spectral channels of the camera ETM+. Such analysis has shown that the closest connection exists between the content of soil organic matter and brightness of the second (green), the third (red) and the fourth (the neighbor infrared) spectral channels. From them three, the greatest value of correlation has dependence between the content of soil organic matter (humus) and brightness of the red spectral channel (r=-0,32). For the purpose of spatio-temporal interpretation of the equation of multiple regressions, 20 agro landscapes in different parts of the Right-bank steppe of Ukraine (The Mykolayiv district and Zhovtnevy district of the Mykolayiv oblast) have been selected. For each agro landscapes was defined content of soil organic matter in the soil using Landsat 7 satellite images taken in 2006 and in 2010 and Landsat images 8 for 2015. The made estimates of maintenance of soil organic matter have shown on processes of fast loss of humus in all layers of soil. Annual losses of soil organic matter in a layer of 0–10 cm from 2006 for 2015 have made 0,16 % in a year, in a layer of 0–50 cm of about 0,13 % in a year, and in a layer of 0–100 cm at 0,10 % in a year. The irrational structure of sown areas and distribution of wind and water erosion processes is the reason of this sad process.

scholarly journals Apport De La Teledetection Pour Le Suivi SpatioTemporel De L’occupation Du Sol Dans La Region Montagneuse Du Tonkpi (Cote D’ivoire)

2017 ◽  
Vol 13 (15) ◽  
pp. 310
Author(s):  
Cyrille Bi Tiesse ◽  
Eboua Narcisse Wandan ◽  
Hyppolite Dibi N’da
Keyword(s):  
Forest Dynamics ◽  
Satellite Images ◽  
Bare Soil ◽  
Likelihood Method ◽  
Landsat 8 ◽  
High Altitudes ◽  
Area Reduction ◽  
Rate Of Increase ◽  
Landsat 7 ◽  
Spatio Temporal

Objective: This study aims to characterize and to map vegetation cover in the mountainous region of Tonkpi in order to evaluate forest dynamics by remote sensing. Methodology and Results: This work relied on four satellite images of the scene 198-55 Landsat 4 TM 1990 from Landsat 7 ETM+ in 2000, Landsat 5 TM 2011 and Landsat 8 OLI-SHOTS 2015. Based on field data, classification was conducted using the maximum likelihood method in satellite images (OLI-SHOTS, ETM + and TM) in order to map vegetation from 1990 to 2015. These maps were superimposed to the Digital elevation model produced from an ASTER image in order to assess forest dynamics at altitude levels. The diachronic study of land use revealed a change in the landscape with a significant reduction of dense forest (-35.6%) and crop lands and fallow (-42.14%), and an increase of degraded forests (15.13%) and bare soil land and houses (10.46%). Moreover, high altitudes occupy a large proportion (37.7%) of the landscape (12,284 km2 ), followed by medium altitudes (34.6%), and low altitudes (27.7%). Concerning bare soil and homes, there is a large increase of the area at low altitudes (88, 96%) while concerning crops and fallow, the area reduction was more pronounced at high altitudes ( -53.64%). The medium altitudes have the highest rate of increase (51.14%) in degraded forests, and the highest decrease (-43.25%) in the dense forests. Conclusion and Application: The study shows that high altitudes occupy the largest proportion of the total area of the region and a change in the landscape due to human activities. Based on the results, we recommend a spatio-temporal monitoring of the net erosion on the mountains in order to maintain its agricultural potential and fight effectively against erosion.

Soil organic matter estimation by using Landsat-8 pansharpened image and machine learning

2020 ◽  
Author(s):  
Abdelkrim Bouasria ◽  
Khalid Ibno Namr ◽  
Abdelmejid Rahimi ◽  
El Mostafa Ettachfini
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Landsat 8

scholarly journals Effect of systematic application of mineral fertilizers and long-term aftereffect of liming on the organic matter of light-grey forest soil

2020 ◽  
Vol 21 (2) ◽  
pp. 160-168
Author(s):  
N. A. Kodochilova ◽  
T. S. Buzynina ◽  
L. D. Varlamova ◽  
E. A. Katerova
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Forest Soil ◽  
Humus Content ◽  
Total Carbon ◽  
Mineral Fertilizers ◽  
Fertilized Soil ◽  
Grey Forest Soil ◽  
Nizhny Novgorod Region

The studies on assessment of changes in the content and composition of soil organic matter under the influence of the systematic use of mineral fertilizers (NPK)1, (NPK)2, (NPK)3 against the background of the aftereffect of single liming in doses of 1.0 and 2.0 h. a. (control – variants without fertilizers and lime) were conducted in the conditions of the Nizhny Novgorod region in a long – term stationary experiment on light-grey forest soil. The research was carried out upon comple-tion of the fifth rotation of the eight-field crop rotation. The results of the study showed that for 40 years (from 1978 to 2018) the humus content in the soil (0-20 cm) decreased by 0.19-0.52 abs. % in variants as compared to the original (1.60 %); though, humus mineralization was less evident against the background of long-term use of mineral fertilizers compared to non-fertilized control. The higher humus content in the topsoil was noted in the variants with minimal (NPK)1 and increased (NPK)2 doses of fertilizer – 1.41 and 1.25 %, respectively. The humus content in non-fertilized soil and when applying high (NPK)3 doses of mineral fertilizers was almost identical – 1.08-1.09 %. The predominant group in the composition of humus were humic acids, the content of which in the experiment on average was 37.8 % of the total carbon with an evident decrease from 42.6 % in the control to 31.8% when applying increased doses of mineral fertilizers. The aftereffect of liming, carried out in 1978, was unstable and did not significantly affect the content and composition of soil organic matter.

scholarly journals Spatio-Temporal Deforestation Measurement Using Automatic Clustering

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Erlyn Wina Rachmawan ◽  
Ali Ridho Barakbah ◽  
Tri Harsono
Keyword(s):  
Experimental Study ◽  
Satellite Images ◽  
New Approach ◽  
Deforestation Rate ◽  
Automatic Interpretation ◽  
Automatic Clustering ◽  
Landsat 7 ◽  
Spatio Temporal ◽  
Temporal Measurement ◽  
World Records

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

Regional mapping of soil organic matter content using multitemporal synthetic Landsat 8 images in Google Earth Engine

CATENA ◽  
2022 ◽  
Vol 209 ◽  
pp. 105842
Author(s):  
Chong Luo ◽  
Xinle Zhang ◽  
Xiangtian Meng ◽  
Houwen Zhu ◽  
Chunpeng Ni ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Organic Matter Content ◽  
Google Earth ◽  
Matter Content ◽  
Landsat 8 ◽  
Regional Mapping ◽  
Soil Organic Matter Content ◽  
Google Earth Engine

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.0)

2015 ◽  
Vol 8 (10) ◽  
pp. 8481-8518
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed e.g. in case of the frequently used Normalised-Difference Snow Index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user but not by reproducible independent information. PRACTISE V.2.0 addresses this important aspect and does show additional improvements. The Matlab based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilised as in-situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results have shown that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

scholarly journals PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1)

2016 ◽  
Vol 9 (1) ◽  
pp. 307-321 ◽  
Author(s):  
S. Härer ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Threshold Value ◽  
Landsat 8 ◽  
Snow Covered Area ◽  
Covered Area ◽  
Independent Information ◽  
Landsat 7 ◽  
Normalized Difference Snow Index ◽  
Definition Of

Abstract. Terrestrial photography combined with the recently presented Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has proven to be a valuable source to derive snow cover maps in a high temporal and spatial resolution. The areal coverage of the used digital photographs is however strongly limited. Satellite images on the other hand can cover larger areas but do show uncertainties with respect to the accurate detection of the snow covered area. This is especially the fact if user defined thresholds are needed, e.g. in case of the frequently used normalized-difference snow index (NDSI). The definition of this value is often not adequately defined by either a general value from literature or over the impression of the user, but not by reproducible independent information. PRACTISE V.2.1 addresses this important aspect and shows additional improvements. The Matlab-based software is now able to automatically process and detect snow cover in satellite images. A simultaneously captured camera-derived snow cover map is in this case utilized as in situ information for calibrating the NDSI threshold value. Moreover, an additional automatic snow cover classification, specifically developed to classify shadow-affected photographs, was included. The improved software was tested for photographs and Landsat 7 Enhanced Thematic Mapper (ETM+) as well as Landsat 8 Operational Land Imager (OLI) scenes in the Zugspitze massif (Germany). The results show that using terrestrial photography in combination with satellite imagery can lead to an objective, reproducible, and user-independent derivation of the NDSI threshold and the resulting snow cover map. The presented method is not limited to the sensor system or the threshold used in here but offers manifold application options for other scientific branches.

Soil Organic Matter and Water Erosion Processes

2003 ◽  
pp. 141-152
Author(s):  
D. Gabriels ◽  
P. Michiels
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Water Erosion ◽  
Erosion Processes

Space Technologies in Agri-Environmental Monitoring System

2014 ◽  
Vol 1 (1) ◽  
pp. 3-12 ◽  
Author(s):  
V. Petrychenko ◽  
O. Tarariko ◽  
O. Syrotenko
Keyword(s):  
Soil Erosion ◽  
Land Tenure ◽  
Humus Content ◽  
Spectral Characteristics ◽  
Climatic Conditions ◽  
Landsat 8 ◽  
Training Samples ◽  
Environmental Monitoring System ◽  
Effective Use ◽  
Land Tenure Systems

The contemporary development of the remote space survey syst ems and elements of geoinformation t echnolo gies o ffers the fundamentally new possibilities of control, forecasting and interpretation of the data obtained from agroecological monitoring. Aim. To describe natural and climatic conditions of the various zones within the territory of Ukraine, its agricultural acquirement and risk of the soils’ erosive degradation manifestations in the meaning of climate changes. To determine the factors infl uencing upon the spectral characteristics of the eroded soils for their identifi cation, deciphering, and also the cultivated lands and land tenure systems degradatio n monitoring according to satellite data. Methods. The logical model of water erosion determination and identifi cation according to the data of the Earth remote sensing (ERS) of high spatial resolution is developed on the basis of classifi cation in basic deciphering signs and the procedure of molding of the training samples forming. The materials of the Landsat 8, SPOT, ASTER and RapidEye space surveys, map materials and data of full-scale ground observations on the test objects were used for identifi cation of the processes of sheet and linear erosion. The soil erosion was determined according to two approaches. The fi rst one is based on the plowed soil and the second – on soil covered with plants. The soil erosion class was determined according to the spectral characteristics and humus content, while gully rate – by reference to gullies’ length and square. Results. The humus content in so il was proposed to be determined according to the spatial distribution of spectral characteristics within the limits of uniform regions and corresponding mathematical-statistical models. The opportunities of linear and sheet erosion classifi cation according to the ERS data, and also their use in the system of monitoring and evaluating the ecological state of agrolandscapes and land tenure systems are shown. C onclus ions. The space mo nitoring data of the soils erosive degradation and agrolandscapes in whole provide the opportunity of more effective use of soil resources due to the strategic determination of degradation processes with the subsequent planning and workout the measures for th e optimization of the erosive dangerous agrolandscapes structure, and also introduction of the ground water-guarding systems of soil management.

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