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Literature Review On Remote Sensing Environmental Sciences Essay

Paper Type: Free Essay Subject: Environmental Sciences
Wordcount: 4417 words Published: 1st Jan 2015

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Remote sensing is the science or art of acquiring information about the Earths surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. (Dr. S. M. Rahman, 2001).

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Remote sensing makes it possible to collect data on dangerous or inaccessible areas. Remote sensing applications include monitoring deforestation in areas such as the Amazon Basin, glacial features in Arctic and Antarctic regions, and depth sounding of coastal and ocean depths. Military collection during the Cold War made use of stand-off collection of data about dangerous border areas. Remote sensing also replaces costly and slow data collection on the ground, ensuring in the process that areas or objects are not disturbed. Remote sensing exceedingly influences everyday life, ranging from weather forecasts to reports on climate change or natural disasters. As an example, 80% of the German students use the services of Google Earth. (Wikipedia, 2012)

In recent time, with man’s increasing interventions with the environment, the situation is aggravated. The quality of available data is extremely uneven. Land use planning based on unreliable data can lead to costly and gross errors. Soil erosion research is a capital-intensive and time-consuming exercise. Global extrapolation on the basis of few data collected by diverse and non-standardized methods can lead to gross errors and it can also lead to costly mistakes and misjudgements on critical policy issues. So, remote sensing provides convenient solution for this problem. Moreover, voluminous data gathered with the help of remote sensing techniques are batter handled and utilized with the help of Geographical Information System (GIS). (M. H. Mohamed Rinos, 2000)

There are two different approaches that can be adopted for determining the characteristics of landslide from remote sensing data. The first approach determines more ‘qualitative’ characteristics such as number, distribution, type and character of debris flow. This can be achieved with either satellite or air borne imagery collected in the visible and infrared regions of the spectrum. The next approach complements the qualitative characterization, estimating dimensions (e.g. length, width, thickness and local slope, motion, and debris distribution) along and across the mass movement. (V. Singhroy, 2004)

Literature Review on Geographical Information System (GIS)

Geographical Information System (GIS) is used to arrange the computer hardware, software, and geographic data. It helps the people interact, analyze, identify relationship and find the solutions to the problems. The system is designed to capture, store, update, manipulate, analyze, and display studied data and used to perform analyses (ESRI, 2005). Since 1970s, GIS has been used to analyze various environments. But the extensive application of GIS to hydrologic and hydraulic modeling and flood mapping and management begin from early 1990s. (Maidment, 2000).

GIS has the ability to represent elevation in terms of topographic surfaces is central to geomorphological analyses and thus to the importance of representing topography using Digital Elevation Model (DEM). It is through the distribution of soil that the land surface changes over the long term and so the ability to link sediment transfer with DEM changes. (Schmidt, 2000)

ArcView GIS desktop software provided the tools of map features that will affect a property’s value such as crime rates, environmental hazards, and the condition of surrounding neighborhoods and properties. ESRI’s ArcGIS is a GIS which is working with maps and geographic information. ArcGIS software can be used for following functions: creating and using maps, compiling geographic data, analyzing mapped information, sharing and discovering geographic information, using maps and geographic information in a range of applications, and managing geographic information in database. (Wikipedia, ArcGIS, 2012). The ArcGIS provides tools for constructing maps and geographic information.

Literature review on soil erosion

Soil erosion is one form of soil degradation along with soil compaction, low organic matter, and loss of soil structure, poor internal drainage, salinization, and soil acidity problems (Wall, 2003). When the degradation of the soil is getting serious, it will contribute in accelerate the soil erosion. Soil erosion is a natural process; it usually does not cause any major problem to the environment. The soil is carried by the agents such as wind, water, ice, animals, and the use of tools by man. Soil erosion is a very slow process and even unnoticeable sometime, but it may occur at an alarming rate which causing the loss of topsoil.

Farmers worldwide are losing about 24 billion tonnes of topsoil each year. In developing countries, because of the population pressure forces land to be more intensively farmed, the erosion rates per acre are twice as high as the standard. The soil erosion also will affect the productivity and growth. This is because when the soils are depleted and crops receive poor nourishment from the soil, the food provides poor nourishment to people. The rate of losses soil is faster than the creation of new soil. The difference between creation and loss represents an annual loss of 7.5 to 10 tonnes per acre worldwide. (DeHaan, 1992)

The eroded soil that enters watercourse will reduce the water quality, reduces the efficiency of the particular’s drainage system and also decreases the storage capacity of lakes. Sediment is the eroded soil that settles in the water systems. Accumulation of the sediment will reduce the capacity of a river or reservoirs to hold flood water. Thus, it requires a lot of money to clean the sediment often and manually. Sediment also can block the sunlight for aquatic plant and inhibit fish spawning. The water becomes not safe for drinking if there is runoff of chemical and nutrients from surrounding farmer’s fields.

In Malaysia, soil erosion is a common natural occurrence. This is due to particular topography, soils and corresponding vegetation that predominate and the extensive rainfall that the country experiences. However, accelerated soil erosion is becoming a serious problem in Malaysia because of rapid land use developments. Various forms of erosion control have been proposed to develop the land in ways that are sensitive to its geography. (Abdullah, 2005)

Literature review on Revised Universal Soil Loss Equation

The development of Universal Soil Loss Equation (ULSE) initially was to assist soil conservationists in farm planning. They used ULSE to estimate the soil loss on specific slopes in specific fields. USLE was a guide for the conservationist and farmer to control the erosion if the estimated soil loss exceeded acceptable limits.

Revised Universal Soil Loss Equation (RUSLE) is a science tool that has been improved over the last several years. It is based on USLE and makes some improvement on the equation. The RULSE has improved the effects of soil roughness and the effect of local weather on the prediction of soil loss and sediment delivery. (Revised Universal Soil Loss Equation, 2003). RUSLE can be used for site evaluation and planning purposes and to aid in the decision in selecting erosion control measure. The RUSLE provides numbers to substantiate the benefits of planned erosion control measures and also an estimate of severity of erosion.

A = R.K.LS.C.P

A is annual soil loss (tonnes/ha/year).

R is rainfall erosivity factor. It is an erosion index for the given storm period (MJ.mm/ha/h)

K is soil erodibility factor. It is the erosion rate for a specific soil continuous fallow condition on a 9% slope having a length of 22.1m (tonnes/ha/(MJ.mm/ha/h))

LS are topographic factor. It represents the slope length and the slope steepness. It represents the ratio of the soil loss from a specific site to that from a unit site (9% slope with slope length 22.1m) while other parameters are held constant.

C is the cover management factor. It represents the protective coverage of canopy and organic material in direct contact with the ground.

P is the support practice factor. It includes the soil conservation operations and other measure of control erosion.

Literature review on USLE and RUSLE

Table 2.1 Comparison of USLE and RUSLE (Renard, 1991)





Based on long term average rainfall conditions for specific geographic areas

Data from more weather stations and thus the value are more precise for any given location.

RUSLE computes a correction to R. This is to reflect the effect of raindrop impact for flat slopes striking water ponded on the surface.


Based on soil texture, organic matter content, permeability, and other factors inherent to soil type.

Adjusted to account for seasonal changes such as freezing and thawing, soil moisture, and soil consolidation.


Based on length and steepness of slope, regardless of land use.

Assigning new equations based on the ratio of rill to interrill erosion, and accommodates complex slopes.


Based on cropping sequence, surface residue, surface roughness, and canopy cover, with are weighted by the percentage. Lumps these factor into a table of soil loss ratios, by crop and tillage scheme.

Sub factors (prior land use, canopy cover, surface cover, surface roughness, and soil moisture) are used. Dividing each year into rotation of 15 day intervals, then calculate the soil loss ratio for each period. The value need to recalculate if one of the sub factors change.

RUSLE provides improved estimates of soil loss changes as they occur throughout the year, especially relating to surface and near surface residue and the effects of climate on residue decomposition.


Values change depending on the slope ranges with some distinction for various ridge heights. It is based on installation of practices that slow runoff and thus reduce soil movement.

Values are based on hydrologic soil groups, slope, row grade, ridge height, and the 10 year single storm erosion index value.

In RUSLE, it computes the effect of strip-cropping based on the transport capacity of flow in dense strips relative to the amount of sediment reaching the strip.

The P factor for conservation planning considers the amount and location of deposition.

Literature review on landslide

Landslides are a type of soil erosion and major natural geological hazards. Each year, the landslide is responsible for enormous property damage which involves both direct and indirect costs. Malaysia experience frequent landslides. According to the local newspaper report in the years 2006-2009, along east coast highways in Peninsular Malaysia, in Sabah (East Malaysia) and in the island state of Penang, heavy rainfalls triggered landslides and mud flows. (Pradhan, 2009)

Landslides happen when there are changes from a stable to an unstable condition in the stability of a slope. There are natural causes and human causes which contributing to a change in the stability of a slope. Natural causes of landslides include:

Groundwater (pore water) pressure acting to destabilize the slope

Loss or absence of vertical vegetative structure, soil nutrients and soil structure

Erosion of the toe of a slope by rivers or ocean waves

Weakening of a slope through saturation by snowmelt, glaciers melting, or heavy rains

Earthquakes adding loads to barely table slope

Earthquake-caused liquefaction destabilizing slopes

Volcanic eruptions

Landslides that are due to human causes are:

Deforestation, cultivation and construction, which destabilize the already fragile slope

Vibrations from machinery or traffic


Earthwork which alters the shape of a slope, or which imposes new loads on existing slope

In shallow soils, the removal of deep-rooted vegetation that bind colluvium to bedrock

Construction, agricultural or forestry activities which change the amount of water which infiltrates the soil. (Wikipedia, 2012)

Landslides in Malaysia are mainly triggered by tropical rainfall and flash floods. The rainfall and floods cause the rock to fail along fracture, joint and cleavage planes. The geology of Malaysia is quite stable but continuous development and urbanization lead to deforestation and erosion of the covering soil layers thus causing serious threats to the slopes (Pradhan, 2007). Abandoned project at hill sites for a certain period which affecting the maintenance of the slopes could causing the slopes to collapse.

List of landslide events happened in Malaysia:

1 May 1961 – A landslide occurred in Ringlet, Cameron Highlands, Pahang.

21 October 1993 – The man-made Pantai Remis landslide caused a new cove to be formed in the coastline.

11 December 1993 – 48 people were killed when a block of the Highland Towers collapsed at Taman Hillview, Ulu Klang, Selangor.

30 June 1995 – 20 people were killed in the landslide at Genting Highlands slip road near Karak Highway.

6 January 1996 – A landslide in the North-South Expressway (NSE) near Gua Tempurung, Perak.

29 August 1996 – A mudflow near Pos Dipang Orang Asli settlement in Kampar, Perak, 44 people were killed in this tragedy.

15 May 1999 – A landslide near Bukit Antarabangsa, Ulu Klang, Selangor. Most of the Bukit Antarabangsa civilians were trapped.

20 November 2002 – The bungalow of the Affin Bank chairman General (RtD) Tan Sri Ismail Omar collapse causing landslide in Taman Hillview, Ulu Klang, Selangor.

December 2003 – A rockfall in the New Klang Valley Expressway (NKVE) near the Bukit Lanjan interchange caused the expressway to close for more than six months.

31 May 2006 – Four persons were killed in the landslides at Kampung Pasir, Ulu Klang, Selangor.

26 December 2007 – Two villagers were buried alive in a major landslide, which destroyed nine wooden houses in Lorong 1, Kampung Baru Cina, Kapit, Sarawak.

12 February 2009 – one contract worker was killed in a landslide at the construction site for a 43-storey condominium in Bukit Ceylon, Kuala Lumpur.

21 May 2011 – 16 people mostly 15 children and a caretaker of an orphanage were killed in a landslide caused by heavy rains at the Children’s Hidayah Madrasah Al-Taqwa orphanage in FELCRA Semungkis, Hulu Langat, Selangor. (Wikipedia, 2012)

A scientific analyses of landslides need to be carry out to predict landslide-susceptible areas, and thus reduce landslide damages through proper preparation and mitigation. So, understanding landslides and preventing them is a serious challenge across worldwide.

Literature review on past research and studies


The application of GIS-based logistic regression for landslide

susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan


Lulseged Ayalew, Hiromitsu Yamagishi, 2005


Kakuda-Yahiko Mountains and their surroundings.


To study the landslide risk around the Kakuda-Yahiko Mountains.

To study the use of logistic regression.

To demonstrate the combination bivariate statistical analyses (BSA) to simplify the interpretation of the model.


Analytical approaches

In LR or even in linear regression, it does little good to combine data with different measuring scales.

Make sure that data have been normalized in a manner LR needs. Failure to do so generally leads to problems during the interpretation of the final results.

Statistical results

Overall model statistics of the regression conducted in this study using IDRISI.

Coefficient positive indicating that they are positively related to the probability of landslide formation through the log transformation.

Prediction probabilities and the construction of the susceptibility map

In addition to the model statistics and coefficients, the final result of the regression process in IDRISI is a predicted map of probability defined by numbers that are constrained to fall between 0 and 1.

The more these numbers are close to 1, the better they indicate the likelihood of finding the mapped landslides.

Depending on the independent parameters considered, the landslide inventory map and the statistical approach used, the best predictor parameters and the predicted probability map of a logistic regression can vary considerably.


Landslides are portrayed according to the types of movements namely slide, fall, flow, spread and topple.

The principle of logistic regression (LR) rests on the analysis of a problem, in which a result measured with dichotomous variables such as 0 and 1 or true and false, is determined from one or more independent factors.


Assessment of soil erosion and sediment delivery ratio using remote sensing and GIS


Weifeng ZHOU and Bingfang WU, 2008


Upstream Chaobaihe River catchment, north China.


To develop monitoring of soil losses in the upstream Chaobaihe River Catchment.

To develop a model by using Geographic Information System tools.

To compute sediment delivery ratio (SDR) per hydrological unit.


Data Collection

Remote sensing data, digital elevation model (DEM), and land use and land cover GIS data were used.

Universal Soil Loss Equation (USLE)

Simple empirical model, based on regression analyses of soil loss rates on erosion plots in the USA.

The model is designed to estimate long-term annual erosion rates for agricultural fields.

A = R·K·L·S·C

A represents mean (annual) soil loss, R is the rainfall erosive factor, K is the soil erosibility factor, L is the slope factor, S is the slope length factor, and C is the cover management factor.


The work indicated there are a number of advantages in using the modified USLE equation including the ability to combine it with a raster-based GIS to produce a cell-by-cell basis for mapping spatial patterns of soil erosion rates.

The advantage of using a GIS raster based framework is that it allows one to quantify the impact of a single factor on the overall result and it can also easily be updated with improved



Soil erosion hazard evaluation – An integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies


Md. Rejaur Rahman, Z.H. Shi, Cai Chongfa, 2009


Within the Danjiangkou County, with an area of 3115.58 km2 and located in the north-western part of Hubei province of China.


To develope numerical model for soil erosion hazard assessment

Tto analyze soil erosion by attempting to estimate the volumes or masses of soil loss


Analysis of study area

The selected area is within the Danjiangkou County, with an area of 3115.58 km2 and located in the north-western part of Hubei province of China.

Sandy clay loam, silt loam and sandy loam on the study area play a dominant role in soil erosion by water.

Data acquisition and preparation

Prepare and analyze the different types of data in soil erosion prediction and hazard assessment as there are many factors that affect soil erosion status.

Soil erosion estimation

Models are needed to predict soil erosion rates under different resource and land-use conditions.

Empirical erosion prediction models continue to play an important role in soil conservation planning and are widely used to predict soil erosion.


The Z-score analysis with GIS and selected parameters, provided a hazard assessment of soil erosion of the area. The methodology of combining the Z-score with GIS provided an improved method for the synthetic evaluation of soil erosion hazard, which extended the GIS capability of spatial analysis and the Z-score capability of multi-layer analysis.


Spatial Prediction of Landslide Hazard Using Discriminant Analysis


Peter V. Gorsevski, Paul Gessler, Randy B. Foltz, 2000


Rocky Point, a small watershed of the Clearwater River Basin in central Idaho.


To study the concept of Discriminant Analysis and GIS.

To analyze the landslide hazard area on Rocky Point.


Principal Component Analysis (PCA)

Help to analyze the multivariate data set.

Discriminant Analysis

Classify presence and absence of landslides using principal component scores.

Discriminant analysis is a multivariate technique that is used to build rules that can classify landslide hazard into appropriate class.


Estimate the probabilities of misclassification.

Cross-validation method removes each observation vector from the calibration data set at a time, forms the discriminant rule based on all the remaining data to classify the removed observation, and notes whether the observation is correctly classified.


provided a detailed basis for spatial prediction of landslide hazard.


Hazard map generated.

Graph of multivariate normal probability plot for the principal component scores.


Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia.


Biswajeet Pradhan, 2010


Penang, Cameron and Selangor.


To generate cross-validation of a multivariate logistic regression model using remote sensing and GIS for landslide hazard analysis.


Data and material

Interpreting aerial photographs and satellite images (SPOT 5 and Landsat TM) of study area.

These aerial photographs were taken during 1981-2006 and were acquired from Malaysian Remote Sensing Agency data archives.

Data analysis using ARC/INFO GIS software package and a Digital Elevation Model (DEM) was constructed.

These data are related to the primary e¬€ects (impact of debris or inclusion of a¬€ected site from previously occurred landslides) of a wide variety of landslide types

Model Approaching

Traditional approach using a multivariate logistic regression model implemented in a GIS framework.

The landslide hazard analysis is a function of a variety of variables that include slope, aspect, curvature, topography, distance from drainage, land cover, soil texture and types, geology and distance from lineament, rainfall precipitation, and the normalized di¬€erence vegetation index (ndvi)

The coefficient applied to the study area, for landslide hazard mapping.

Multivariate logistic regression model

Easier to use than discriminant analysis when have a mixture of numerical and categorical regressors , because it includes procedures for generating the necessary dummy variable automatically.

Application of multivariate logistic regression model on landslide hazard mapping.

Validation of the model.


The validation results showed a satisfying agreement between the hazard maps and the landslide locations verified in the field.


GIS Application in Landslide Hazard Analysis


Chyi-Tyi Lee, 2009


Shihmen Reservoir Catchment Area in Northern Taiwan.


To analyze the landslide hazard area using GIS application.


Image and data collection

The basic data utilized included a 5m x 5m grid DEM, SPOT5 images, 1/500 photo-based contour maps, 1/50000 geologic maps and hourly rainfall data.

Establish of event-based landslide inventory

To develop susceptibility model, only considered new landslides triggered by typhoons.

Landslides triggered by Typhoon Aere were interpreted and delineated by comparing SPOT5 images taken before and after thetyphoon.

Determination of causative factors and triggering factors

These factors are then statistically tested and y effective factors selected for susceptibility analysis.

10 factors are selected:

Lithology, slope gradient, NDVI, slope roughness, profile curvature, total slope height, relative slope height, topographic wetness index, distance to a fault, maximum rainfall intensity.


Construction of model via logistic regression.

Logistic regression to determine a linear function of factors for interpreting the landslide distribution from a set of training data.

The linear function is used to calculate the landslide susceptibility index (LSI) for each cell.

The LSI used to establish a probability of failure to LSI curve and determine the spatial probability of landslide occurrence at each cell.

Landslide susceptibility mapping

The landslide hazard area could be for the prediction of future landslides providing a scenario rainfall distribution is given.


Successfully predict landslide location, area and volume in a drainage basin or catchment area using GIS.


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