05SPATIAL RELATIONSHIP BETWEEN LAND-USE LAND COVER WITH SOIL EROSION IN
THE NARMADA BASIN USING SENTINEL 2 IMAGE: ASSESSING THE PERFORMANCEOF RF MACHINE LEARNING CLASSIFIER
Vinay Raikwar1, Pramod Pagare1 and Aminu Abdulwahab2
1Dept. of Geography, Govt. Mahatma Gandhi Smrati PG College, Hoshangabad, MP, India
2Department of Natural Resources, TERI School of Advanced Studies, New Delhi, India
2Department of Surveying and Geoinformatics, Adamawa State, Nigeria

Email: vinay.utd@gmail.com

How to cite this paper:
Vinay Raikwara, Pramod
Pagarea,
Aminu
Abdulwahab (2022) Spatial
Relationship Between LandUse Land Cover with Soil
Erosion in the Narmada
Basin Using Sentinel 2
Image:
AssessingthePerformance of RF Machine
Learning Classifier, Journalof Global Resources, Vol. 08
(02)
DOI:
10.46587/JGR.2022.v08i02.005

Received: 15 May 2022
Reviewed: 25 May 2022
Revised: 05 June 2022
Final Accepted: 26 June 2022

Abstract: In the past decades, increasing demand for
cultivable soil, deforestation and land conversion in theregion has led to severe soil and natural resources
exhaustion. The grade and rate of the soil and land-use landcapacity connection has been determined using soil
parameter data together with geographic information
systems, utilizing machine learning methods (random
forest).The analysis shows high and substantial land
coverage over Grass Land area with 52,330.59 sq. km/54.5
percent, with an Urban growth of 19,255.36 sq.km/20.0
percent, 12,400.51 sq.km/12.9 percent for Agriculture,
Forest land with 8,828.67 sq.km/9.2 percent, 1,970.382
sq.km/2.0 percent for water and the smallest land coveramong the classes was cropland with 1,337.51 sq.km/1.4
percent area coverage. Random forest algorithms show thatsoil erosion for the whole Upper catchment was lower
compared to the bottom catchment region and high soil
erosion in line with the land conversation of naturalvegetation. This analysis includes extra significant
information and differences between kinds of land use soil
erosion with a major and very severe area 38.337.55 sq.km/ 39.9 percent; the severely eroded soil covers 29.346.59
sq.km / 30.5 percent; the modest soil erosion area
(15.372.16 sq.km / 16.00 percent); the lowest or slightest soil
erosion zone was 13066.73 sq.km/13.6 percent. To
strengthen the status and use of the collected resources asa result of sustainable land use activities that benefit local
rural life, inclusive and viable land use is needed
immediately. The area where very severe soil erosion andthe land cover has been correlated to determine therelationship with the existing land covers in the area,
decreases of soil erosion indicates the change in the land
uses. The correlation between the LULC and the soil erosion
was 0.53, with an RMSE of 0.033 and a p-value of 0.000063.
Key words: Spatial Relationship, Land-Use Land Cover,
Soil Erosion, Narmada Basin

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Introduction
In many regions of the world, soil erosion is among the most significant soil degradations thatcause environmental problems (Luetzenburg et al., 2020; Li et al., 2020). Soil erosion implies
moving and conveying soil from numerous sources, including liquid, storm and movement ofvolume (Bullock, 2005). Land degradation is a significant type of erosion in wet and semi-damp
climate areas, such as northern Iran “(Esmaeeli G. & Gholami, 2012; Khaleghi & Varvani,2018)”. Human activities may also lead to erosion by two means, directly by soil movement,such as the carriage, and indirectly by destabilizing slope materials, such as agriculture, andspeeding up erosion by the wind, water or gravity. This eliminates land soil degradation bylowering agricultural productivity and plant loss in rainforests and fields, and it has significant
ramifications for agriculture, food, and water supplies and leads to substantial repercussionsof contamination and sedimentation of dam reservoirs on agriculture, food, and water
resources “(Pierson et al., 2007; 2008; Gholami and Khaleghi, 2013; Akay et al., Aldrich et al.,2005;). Various variables effect soil erosion (Masson, 1971; Pastor and Castro, 1995; M.
Kasasnovas, 1998), Di Stephano at al. (2000) and Uson (1998))” include precipitation
amount/intensity. Pickup & M., (2000). Differing influences include the quantity and intensity ofthe precipitation. Several researches have been conducted on soil erosion. The soil erosion
degree of Descroix and Poulenard (1995) was demonstrated (27 percent). Bohm & G., (1995)
have showed the level of vegetation been highest than the soil and route gradient features.
Kirkby et al. (2005) found that the slope-vegetation mix often impacts the hillside soil Erosion
rate. Various methods are available on the hillside for monitoring soil erosion, soil erosion or
runoff of certain precipitation events are estimated or within a certain period. The usage of theplots allows the exact time intervals to be chosen from a storm to a monthly or annually. Current
care and recuperation are required for fields which make the process time consuming andexpensive. Another possibility is the application of pins in erosion measurement, which wereeasier and more successful in prior studies. “Shumm and al, (1974); Shi et al. 9(2013); Haigh,(1978); (Boardman et al, 2015). (Chumm (1956); Emmett, (1964)”. Sedimentary pins arecheaper and easier for users in comparison to field plots and are appropriate for large-scale
applications such as an annual erosion rate. The use of erosion pins is therefore difficult inshort-term estimates (e.g., a given precipitation, or a given day) (Ireland, 1939; Emmett, 1965;
Haigh, 1977; Loughran, 1989; Hancock et al., 2008; Kearney et al., 2017 & Boardman and F.,(2016)). Depending on the kind of erosion, actual accuracy and objective precision of
measurement, the choice of methodology and strategy.
Several studies using erosion pins to measure various forms of ground erosion havebeen undertaken. “(Favis-Mortlock and B., 2016). Haigh (1977)” indicated that the usage oferosion pins was made in 1956 by Colbert (1956) and Schumm in the U.S. for the measuringof soil erosion. They employed erosion markers to calculate the rate of erosion in the Badlands.
The riparian pines employed for erosion are also estimated to include riparian erosion (Lawler,
1978, 1991; 1992; 1993), lowlands (Streeter, 195; Summer, 1986) and Blackland erosion(Clarke, and Rendell, 2007; Nadal-Ronmero and others, 2011: Bardman et al., 2015; Hancockand Lowry, 15), and riparian erosion (2015). (Harvey,1974). Erosion pin is a powerful device
for assessing hillside soil erosion in several case studies (Tervuren, 1990; Harden et al., 2009;
Keay- Bright and Boardman, 2009). For example, in the Mediterranean Badlands, NadalRomero et al. (2011) employed and assessed several ways of monitoring sediments. Erosion
pins were employed by Kumar Ghimire et al. (2011) to evaluate several forms of erosion in the
Himalayas (E.g., erosion of the gullies, rill erosion, landslides and bank erosion). They showedthat the Himalayan erosion rate in comparison to other places is notably high. Measurementof soils erosion is a costly and time-consuming operation that cannot be carried out across alarge region of investigation. Field-based measuring models can be employed to resolve thisproblem. The purpose of these models is to relate the expected soil exposure (as source or as
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endpoint) to the influence on the rest of the field of study and to the influence of soil erosion
factors (for example, slopes, plant covers, soil characteristics) of the plot region or of theerosion pins. Therefore, methods including neural artificial networks (ANNs) and field
information may reduce costs and workloads and allow us to predict the soil erosion processacross the research region. ANN has been frequently employed in hydrological processes andenvironmental modelling “(Anctil and Rat, 2005; Isik et al., 2013; Gholami et al., 2018; Sahour
et al., 2020; Alshehri et al., 2020)”. The relationship between land use and soil usage and soil
erosion, between policy-making trees and ANN was examined by Rosa et al. (1999). As aparadigm alternative to quantifiable, process-based erosion models for the South Downs ofEngland, Harris and Boardman (1998) have employed expert systems in conjunction with theANN. In addition, GIS would be a valuable mapping, analysis and severity for soil erosion,
when associated with ANN's geographical information systems. This research seeks toanalyse, depict/evaluate soil erosion and the link between land usage and land use by meansof RF algorithms in the GIS mode in Google Earth and to identify the association between an
area of soil erosion and a land cover in software R programming.
Study Area
The Narmada River is seventh biggest river with the Ganga, Brahmaputra, Godavari, Krishna,
Indus, and Mahanadi being the only preceded by river network. Narmada River Basin (NRB)
located in the Central Park of India with an area of the drainage area of 98,796 km2 at a mean
altitude of 760 m between 72°32’ E to 81°45’ E Longitude and 21°20 N to 23°45’ N Latitude. It
has a length of 1312 km. The watershed area of the river encompasses the administrative
states of Madhya Pradesh (86.18 percent), Gujarat (11.6 percent). Narmada and the Tapi
Rivers, by contrast, flow westwards to the Arab Sea combined with other peninsular rivers that
go into the Bay of Bengal in India (Godavari, Cauvery, Mahanadi). The river Narmada
originates in the district of Amarkantak in the district of Anuppur and flows into the Bharuch,
Arabian sea on the eastern coast (Fig. 1). At 1057 meters above sea level, it emerges from asmall pond (Narmada Kund). The region is surrounded by northern Vindhyan, east by Maikala,south by Satpura and west by the Arabian Sea. The area is largely below the 500-meter-highnormal sea level. On the left banks and on the right banks, on 19, the largest prosperity areas
with a catchment area of approximately 3,500 sq.km are in Burhner, Banjar, Hiran Tawa, Chota
Tawa, Orsang and Kundi
Approximately 35 percent of the territory in the basin lies in the forests and 60 percent
in arable land, and 5 percent in pastures, wastelands, etc. The basin has a population of over
16 million people; however, the urban population is only around 20 percent of the total (CPCB,
1994). However, the major river banks on the left and right banks, with a catchment area ofalmost 3,500 km, are in Burhner, Banjar, Hiran Tawa, Chota Tawa, Orang and Kundi. In
summer, temperatures vary between 40˚C and the 42˚C to the 8˚C and the 13˚C. In thesummer (April to June) the evaporation rates are 6 mm to 28 mm and in winter (1 mm – 9 mm)(October to March). On the Narmada River and its tributaries, several dams have been erected.
The basin is in operation with about 4,000 water-specific projects of different size andobjectives. The important projects of the basin are “Bargi dam, Barna, Indra Sagar, Kolar,
Omakareshwar, Maheshwar, Bhagwant Sagar, Tawa and Sardar Sarovar dam”. The Sardar
Sarovar Dam, has a height of 110.64 m and reservoirs of 3,700 ·106 m 3, is the biggest of the
29 big dams scheduled for Narmada.
Deccan Basalt, followed by the Vindhyan and Gondwana Groups is the principal
lithological part of the basin. The meta group Bijawar, the small component contributing tobasin geology is the gneissic complexes of the Archaean-Proterozoic period. The alluvium is
primarily restricted to the Narmada valley. The river Narmada occupies a mega-tectonic zoneof the sound line of Narmada and Tapi function. This area comprises multiple longitudinal,
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faulty, vertical and lateral blocks with episodic history (Shankar 1991). Smectite clays with highhydraulic capacity are generally dark soils. In the upper basin, black soils are commonly mixed
with sandy or laterite red soils. The soil profile is rather low and extends over the mountains
and plateau. Red soils derive from the strong chemical weathering of basalt, leaching all therock minerals except silica, iron and aluminium oxides. The soils demonstrate good drainage
characteristics due to extensive leaching. The middle basin area Vindhyan and Satpura varies
from shallow black grounds to medium black soils. The main soil types in the bottom part ofthe basin are black soils. On the other hand, the northern plateau contains black/red soils,
Pliocene rocks and the latest alluvium characterize the Narmada basin (CWC 1996–99).
Figure 01: Location of the Narmada River Basin in Central-Western India

Raster Image Acquisition and Pre-Processing
Google Earth Engine (GEE)The google earth engine (GEE) employs a multi-petabyte database of geospatial data andimaging satellites utilizing a planetary analytic capacity to make changes, map trends andquantify the variations on the earth's surface available and readily available for developers,scientists and researchers (Google Earth Engine Team). It is a tool to analyse geographicalinformation utilizing a Python and JavaScript application programming interface (API). The
approaches are developed here with JavaScript (Zeltner, 2016). A variety of data sets are
displayed in the GEE data catalogue. The user gets access to imagery of the shuttle radar
topography missions from several satellite image suppliers MODIS, LANDSAT, SENTINEL ordigital elevation data (SRTM). Included in the GEE data catalogue are raw satellite data, Orthocorrects, top-of-atmosphere and surface reflections (Gorelick et al., 2017).
Satellite Data
GEE was used to acquire the sentinel 2 datasets ESA API Reflectance datasets for 2021 overpart of the Narmada region. The code was configured to collect data over the region with acloud cover condition of less than 10 percent as shown in figure (2). Also, the image wasdownloaded and exported to the drive and these image bands were further opened in Erdas
Imagine for radiometric and atmospheric correction before the Random Forest (RF) land use
and land cover classification using the coding in RStudio programming software.
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Figure 02: Sentinel 2 Image Collections with less than 10 percent Cloud effect from
GEE, Atmospheric Correction

A great deal of research has been done on the issue of image correction foratmospheric effects. Those efforts lead to a range of atmospheric codes (models) of radiativetransfer which can provide a practical assessment of the effects of atmospheric dispersion andabsorption on satellite imagery. Once these effects have been detected for a particular imaging
date, the effects of scattering and/or absorption may be removed in a band and/or pixel in theimage, the image is then regarded as corrected against the atmospheric effects. The Erdas
imagine was used for the analysis of the atmospheric effect on the downloaded Sentinel 2
datasets. The application of the atmospheric corrections module on the Erdas Imagine
program eliminated any noise and haziness in the image. The final corrected image of 2021
was used in R studio for Random Forest (RF) classification algorithms and it was further
exported to the ArcGIS for final graphical visualization.
Random Forest
Breiman's (2001) Random Forest RF is an ensemble training technique for enhancing
regression/classification trees (CART) by the combination of several rule trees. For the mostcommon class assignment to the input data, each tree contributes one vote. According to (Linet al. 2010), the program benefits from two key strategies: the random selection and bagging
of the subspace. First, RF builds several binary classification trees (tree) with many bootstrap
samples by using original data to replace them. These trees contribute to the vote by units,
and a majority vote is taken from all forest trees. The appropriate classification. Samples forthe bootstrap are known as "out-of-bag." The OOB-sample may assess a misclassification
error and variable significance (about 37 per cent of the entire data). Second, each node
selects a number of input (tree) variables from a random sub-set of characteristics. This
collection of qualities can be used for the optimal division. No cutting is undertaken and allforest trees have been maximized trees, to ensure that they are less similar or low distinctions“(Genuer, Poggi, and Tuleau-Malot 2010; Lin et al. 2010; Breiman 2001)”. To improve theprecise categorization the RF parameters (i.e., mtree and ntree) must be defined “(Breiman
2001; Mutanga, Adam, and Cho 2012)”. The default numbers are 500, but the default value is
500 and the default value of the number of spectrum bands is 500. (m-try).Ideal combinationof the two parameters was found by search strategy based on the OOB mistake estimation(Tian et al. 2009). The grid search value for mtree was between 1 and 5, with an interval of
500 varying in the grid search value for the ntree parameter between 500 and 10,000. A totalof 100 ntree and mtree combinations were achieved during grid search. Image RF input was
setup in the EnMAP box to identify Rapid Eye imagery for RF parameters (mtrees and ntrees)(Waske et al. 2012).
Tuning of RF parameters
The turning results show the lowest OOB error rate with the default mtree value of 0.14 incombination with the true value of 1000 (14 per cent). On the other side, with the combinationof 6 mtree value with 800 ntree value, the greatest OOB error rate (6.4 per cent) was achieved.
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The input parameters were selected to form the RF algorithm for classifying LULC classes,
mtree value 4 and ntree value of 1000.
LULC Categories Definition and Reference Data Collection
The original classes of LULC were established based on the old existing Narmada LULC map
created with Sentinel 2 and the NRSC LULC images from Atlas of India (and use land cover).
The LULC categories were initially stratified using training datasets, which formed 6 spectralclasses from seven inter-spectral strips on sentinel 2. Six spectral groups were then divided
into all major LULC types (Table 1). A preceding field dataset for the LULC map of Narmada
was created in 2021 based on a ground field data of the study area “(Lowry et al. 2007; Knornet al. 2009)”. Data were acquired for training and validation. The data was gathered by using
a stratified random sample approach to sample LULC classes based on their representative
size in the study region. Basic field tests were gathered via the research field of each class tofind fields meeting the pre-selected homogenous content requirements of at least 20 m2 (Lowry
et al. 2007). The data was then randomly divided into training (70 per cent) and validation data
sets (30 per cent). The training classification data collection (RF) is used to verify accuracy
and to evaluate accurately the validation data set. With maximal optimisation, the RF settings
have improved. (Duro, Dubé 2012 and Franklin, 2012).
Annual Mapping of GIS soil Erosion Rate
The RF and GIS algorithms were utilized for simulating and mapping annual rates of earth
erosion according to the modelling method (training, cross-validation, and testing). The RF
algorithm is the best model for the soil erosion network. Our approach included a connection
between RF and GIS, which would identify soil erosion rates as well as the relationship to soil
erosion over the research region. Inputs and erosion rates in a raster format were calculated
and input and validated RF model was simulated the whole research region. Then simulated
exposure rates in raster format were returned to the GIS to show the region of soil erosion andthe rates of LULC. To verify the correctness of the mapping, we have compared the soil erosion
levels of all validation pins to the corresponding simulated values in the final map. In GIS, DEM
and the final map were created from the longitudinal patterns of the pitch analysed and annual
erosion of the soil. The longitudinal profile throughout the research field was shown in fig. 3.
Figure 03: Longitudinal Profile of the Narmada Basin

Results
Soil Erosion Rate in the Narmada Catchment as affected by Land Use
The erosion rate varied from 13.6 percent, 13066.73 sq.km to 39.9 percent in the whole
Narmada basin and 38337.55 sq.km, respectively, for lower to higher areas. For thedominating terrain (flat to hilly landscape) the mean total value for the basin was (96123 sq.km)
regarded quite high. One of the regions with substantially greater topographical features which
concentrate runoff water producing serious soil erosion was the catchment studies. The studyof the variance test shows that for the investigated region, the max (p-value < 0.05) coefficientof the variance is substantially higher (P-value < 0,05) for 2021. As large changes in LULC and
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42high rainfall erosion were the outcomes of climate change in recent years. The changes in soil
loss and the distribution of water in LULC and climate are presented.
Climate change is having a major influence on rainfall distribution, affectinghydrological cessation of the watershed, including surface runoff, evapotranspiration of
streams and loss of soil erosions (Neupane and Kumar, 2015). Mallick et al. (2014) and Mondal
et al. (2017) have however said that LULC alterations with other variables, such as rainfall
erosivity, land type, human ageing and so on, may result, from place to location and from time
to time, in geographical variations in soil loss. The (Table 3) results indicate that there is asignificant variation in cropland (1337.51 sq.km), and agricultural land (12400.51 sq. km, 12.9
per cent), between croplands and forest land with a high mean soil erosion (8828.67 sq.km,
9.2 per cent) with the forest area, which is in line with the finding of (Serpa et al. (2015)), which
reported LULC change in soil erosions. Interestingly, high erosion levels similar to the robust
countries of East Africa, such as Ethiopia, are also very important (Elias, 2017). This indicatesthe strong soil eroding and precipitation erosive variables faced with fast agricultural andindustrial land conversions.
Figure 04: Soil Erosion Map by RF Model

Accuracy Assessment
A separate test data set for both RF classifiers and detailed RF classification accuracy
confusion matrices was used to evaluate the provision performance of the trained models. The
RF classification was more precise. The RF's total accuracy was 99.2 percent, with a kappa of
0.96. All LULC classifications generally obtained an accuracy of more than 99 per cent, except
cropland (CL) and grassland (GL, respectively), which had 89.0 percent and 88 percent. The
RF classification has also been shown to be more than 98 per cent accurate for most LULC
classes and the map results of the RF model are shown in figure 4. The mature cropland class
was confused with grassland using this classification. In general, both RF LULC and soil
erosion maps indicated the relationship between the two class areas and the soil erosion levelwithin the research area (figure 5). The analogous areas collected by RF also confirm similar
RF classifications. Between croplands, land and grassland there was confusion. The area ofinvestigation is dominated by indigenous forests and grasslands, rocky soils and water basins.
On the other side, settlements and coastal sand are less noticeable.
LULC Classification RF Performance
The application of RF resulted in six LULC classifications (Figure 3). As can be observed in
the overall accuracy results, the LULC maps produced using both classifiers were not distinctly
differentiated. The rocky shores pixels northeast of the maps showed a modest change (Figure3). The method for RF classification measures the relevance of the variables in theclassification process. The determination of the relevance of factors in this study enabled theidentification of significance in the LULC categorization of each of Sentinel 2 bands (n = 5)
(Figure 4). In the categorizing procedure, the Red Edges Belt is of major importance andJournal of Global Resources, July 2022, Volume 08 (02) V. Raikwara, Pramod Pagarea , Aminu Abdulwahab P a g e|

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modelling accuracy in Sentinel 2 Band Assessment (n=5). The total accuracy of the LULC
classification reduces by 1.4 percent, as seen in Figure 4, when the red band is omitted fromthe model. The infrared band is most beneficial in distinguishing bodies of water when theutility of every band for a particular category of LULC has been studied, whereas the blue bandhelps least in defining indigenous timber.
Figure 05: Land use Land Cover Map by RF Model

Figure 06: Box Plot for Soil Erosion and LULC

Figure 07: Correlation Between Soil Erosion and LULC

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Figure 08: Correlation Graph

Discussion
The recent past has accomplished many success stories in LULC mapping using several typesof (fine, medium and coarse) spectral and spatial resolution for optical sensors. However, there
are still questions about accuracy “(Foody 2002; Lu and Weng 2007)”. Furthermore, the costs
and availability of remotely sensed data with sufficient spatial, spectral and radiometricresolutions continue to hamper large LULC maps. The normal LULC maps produced byordinary multi-spectral sensors (10m to 100m) are not sufficient for practical use (Foody 2002;
Lu and Weng 2007). In contrast, fine-space resolution sensors (<5m) are limited by cost,
availability, treatment, and a high level of data dimensions (Dalponte et al. 2009; Mutanga,
Adam, and Cho, 2012). Locally constraints on remotely detected and medium-sized data limit
the utilization and integration of field data with remotely sensed variables (Lu and Weng 2007).
This study was intended in this context to evaluate the new-generation multi-spectral sensor
performance (Sentinel 2).
The findings of the classification in this study showed that RF and classification images
from Sentinel 2 is beneficial for mapping and comprehending complicated terrain. Highclassification precision was achieved using RF (Tables 2 and 3). Based on RF variable
significance, the relevance of each Sentinel 2 band on classification output (Figure 4), which
is a substantial contribution to the resolution of the error of classification commonly associatedwith multi-spectral usage, was further effectively identified using remotely sensed data (Foody
2002; Congalton and Green 2008; Congalton 1991; Oetter et al. 2001). RF was able tocategorize the LULC classes stated above. The overall accuracies of RF and SVM werecomparable. RF obtained more accuracy in categorization than around 1 percent. The Sentinel
2 data collection has the geographical and spectral properties that enable us to divide sugar
cane regions into three LULC classes: mature, young and clear. In degraded grasslands andgrasslands, we have effectively segregated sections covered by grass. In South Africa efforts
have been unsuccessful to yet for the separation of such coverage groups using traditional
multispectral information (SPOT and Landsat) (Fairbanks et al. 2000).
The accuracy of the class revealed that the accuracy of each grade was constantly
increased. The tree classification combination in RF ensures that each classification
determines the values of each sampled random vector. These random vectors have the same
distribution for all forest classifications and each tree votes for one unit of the most popular
input (Breiman 2001). This distribution and casting strengthen RF algorithms rather than noiseand externalization (Watts et al. 2009). The high accuracy of classifications derived from thisstudy gives trustworthy information on the numbers and scope of major land groups and may
be used to evaluate agricultural production as well as ecosystem function, value, andproductivity in terms of environmental deterioration.
Therefore, this knowledge is crucial in managing one of India’s most precious
ecosystems. However, these results can only be interpreted as initial as the additional study isrequired to expand the use of Sentinel 2 data to map LULC on different landscapes. Since highprecision in large LULC classes has been achieved, further research has to be done to develop
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a way to examine the lessons and detailed vegetation classes, because the red-edge bandhas the most significant influence. The considerable spectral variance in the same LULC classand the high spatial resolution on the red band resulted in an incorrect ranking specifically inthe signal groups for vegetation. This calls for more study by evaluating the alternative way ofclassifying objects to deal with this challenge.
Conclusion
This investigation assessed the capability to identify covers types in a heterogenous Narmada
basin environment with a relatively new, spatial multispectral sensor, Sentinel 2, with asophisticated RF classification algorithm. This work results in fresh insights into theperformance of Sentinel 2 imaging by mapping an intricate environment with RF classifiers.
The RF classification has been proven to have a considerable influence on all total
classification and accuracy particularly in the vegetation zones. Comparing soil erosion with
LULC, results showed that the RF LULC was comparable to that of soil erosion with R square
0.53 and a p-value of 0.000063 and RMSE of 0.033. The soil erosion was well defined. RF isa more robust approach that may be utilized for measuring and classifying variable relevance.
It is straightforward to use because just two parameters must be optimised. In thisinvestigation, errors in the spectrum variation assigned LULC classes in HR imagery may beascribed as in previous relevant investigations. Therefore, alternative methodologies should
be further studied, such as object classification.
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