Conservation Priority Land Selection in Sangiran Mountainous Dome Area using Remote Sensing Data

The purpose of this study is to describe the features of soil forms on PKS derived from the data which are related to the erosions that happen. Based on this, the location of the priority of the soil conservation can be chosen. The research geomorphologic hazard method applied in this study is the continuation of the previous research which has yielded the study of soil form area. The result of that study is combined to other data to know IBE which covers topography, slope area, the count, it can be determined the priority of the soil conservation based on the delineation of the soil forms which are interpreted from remote sensing data especially the aerial photograph. The conclusion are 1) the choice of the priority of soil conservation and cultivating plants an be done by knowing IBE; 2) the degree of priority of the soil conservation and plant cultivation in PKS depend on the spreads of the soil forms. The soil forms happened is the features of the difference of rock formations, litology, the degree of erosion, landslide, and the process of diafirism. The priority sequences of the loations of the soil conservation and plant cultivation are S3 dan S4 followed by S5, s1, S2, and finally F1.

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Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

Geodesy and Cartography ◽

10.22389/0016-7126-2021-973-7-21-31 ◽

2021 ◽

Vol 973 (7) ◽

pp. 21-31

Author(s):

Е.А. Rasputina ◽

A.S. Korepova

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Spatial Resolution ◽

Baikal Region ◽

Remote Sensing Data ◽

Meteorological Station ◽

Sensing Data ◽

Modis Data ◽

Weather Stations ◽

The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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Inundation Analysis Method for Urban Mountainous Areas Based on Soil Conservation Service Curve Number (SCS-CN) Model Using Remote Sensing Data

Sensors and Materials ◽

10.18494/sam.2020.2769 ◽

2020 ◽

Vol 32 (11) ◽

pp. 3813

Author(s):

Xinyue He ◽

Chao Chen ◽

Yue Liu ◽

Yanli Chu

Keyword(s):

Remote Sensing ◽

Soil Conservation ◽

Remote Sensing Data ◽

Curve Number ◽

Soil Conservation Service ◽

Analysis Method ◽

Mountainous Areas ◽

Sensing Data ◽

Service Curve ◽

Scs Cn

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Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Land ◽

10.3390/land10111279 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1279

Author(s):

Dominik Scholand ◽

Britta Schmalz

Keyword(s):

Remote Sensing ◽

Success Rate ◽

Soil Conservation ◽

Soil Loss ◽

Agricultural Land ◽

Remote Sensing Data ◽

Google Earth ◽

Mountain Range ◽

Sensing Data ◽

P Factor

The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD.

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Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data

Remote Sensing ◽

10.3390/rs13245153 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5153

Author(s):

Liangliang Zhou ◽

Yishao Shi ◽

Jianwen Zheng

Keyword(s):

Remote Sensing ◽

Urban Area ◽

Remote Sensing Data ◽

Residential Areas ◽

Economic Activities ◽

Night Time ◽

Sensing Data ◽

Dbscan Clustering ◽

The Difference ◽

The Impact

The activity of the urban night-time economy is one of the most important indicators reflecting the prosperity of an urban economy. The business circle is an important carrier of urban commercial activities and the core area of urban nightlife. This paper takes the main urban area of Yiwu city as the research object. Based on POI data and night-time light remote sensing data, two-factor mapping, kernel density analysis, DBSCAN clustering, and local contour tree methods are adopted to identify the business circle structure of the main urban area of Yiwu city and analyse the relationship between business circle characteristics and the night-time economy. The following conclusions can be drawn. (1) The spatial superimposition relationship between the night-time remote sensing data and points of interest (POI) data in the main urban area of Yiwu city is good, and the overall coupling results show obvious circle structure characteristics. (2) The spatial distribution of different business combinations has obvious regularity: comprehensive shopping business shows a multicentre distribution pattern and has a hierarchical feature. In contrast, professional food and beverage and leisure and entertainment businesses are close to urban residential areas, and different groups of people live in different places with their own characteristics. (3) From 2015 to 2019, the brightness value of each business circle showed a continuously increasing trend. In 2020, due to the impact of COVID-19, most of them declined. (4) Overall, the difference in business circle tiers reflects the difference in the level of night-time economic activities.

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