基于遥感和GIS的土地利用评价与土壤质量评估

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Assessing Land Cover and Soil Quality 土地利用评价是评估土地质量和土地利用方式的重要方法。随着全球变化和环境污染的日益严重，土地利用评价变得越来越重要。为了评估土地质量，需要结合遥感和GIS技术，以获取更加准确的结果。本文将详细介绍土地利用评价的重要性、遥感和GIS技术在土地利用评价中的应用，以及土地质量评估的挑战和限制。土地利用评价的重要性土地利用评价是评估土地质量和土地利用方式的重要方法。土地质量的变化会对环境和生态系统产生重要影响。例如，土壤污染和退化会导致生态系统的崩溃，影响生物多样性和生态平衡。因此，土地利用评价是评估土地质量和土地利用方式的重要方法，可以帮助我们更好地理解土地的变化和发展趋势。遥感技术在土地利用评价中的应用遥感技术是评估土地质量和土地利用方式的重要工具。遥感技术可以获取土地的遥感图像，通过图像处理和分析，可以获取土地的土地利用信息、土壤质量信息和植被信息等。遥感技术可以快速、准确地获取土地信息，且可以覆盖大面积的土地，实时监测土地的变化。 GIS技术在土地利用评价中的应用 GIS技术是评估土地质量和土地利用方式的重要工具。GIS技术可以将土地的空间信息与属性信息结合，生成土地利用图层，进行土地利用评价。GIS技术可以快速、准确地获取土地信息，且可以进行复杂的空间分析和模拟。土地质量评估的挑战和限制土地质量评估存在一些挑战和限制。例如，土壤质量的评估需要考虑多种因素，包括土壤的物理、化学和生物性质等。同时，土地利用评价也需要考虑多种因素，包括土地利用方式、土地质量和环境因素等。此外，土地质量评估也存在一些技术限制，例如遥感图像的分辨率和准确性问题等。结论土地利用评价是评估土地质量和土地利用方式的重要方法。遥感和GIS技术是土地利用评价中的重要工具，可以快速、准确地获取土地信息。然而，土地质量评估也存在一些挑战和限制，需要进一步的研究和发展，以提高土地质量评估的准确性和可靠性。

majority of the population residing within urban areas. The rapid

population rise has repercussions such as enhanced risks of climate

change effects, excessive use of energy resources, water scarcity, pol-

lution, loss of wildlife and agricultural land, and lack of employment

or underemployment among others. Due to heterogeneity in land

cover and sealing of soil surface, the SOC estimates within urban en-

vironments are unknown, or are difﬁcult to measure. The massive

SOC losses from urban soils (Lal, 2003a,b), will require accurate SOC

estimation to make science driven decisions aimed at conserving

the soil and water resources (Platt, 1994).

2. Development of methods for SOC estimation

Laboratory methods such as the Walkley and Black (1934), and

the dry combustion (DC) (Kalembasa and Jenkinson, 1973; Nelson

and Sommers, 1982) have been the standard approaches for SOC de-

termination. The SOC pool can also be quantiﬁed in situ, for example

through the Inelastic Neutron Scattering (INS) method (Wielopolski

et al., 2011). However, determination of SOC using laboratory or

ﬁeld based methods may be expensive and time consuming especially

for C inventory over large spatial extents. Remote sensing technology

is emerging as a promising tool for environmental and public sector

applications, given the availability of historical data, the reduction in

data cost, and increased resolution from satellite platforms (GOFC-

GOLD, 2012; Rowland et al., 2007). Remote sensing exploits the fact

that objects on the earth's surface reﬂect, absorb, and emit electromag-

netic radiation in a different way (i.e., each object has a speciﬁc spectral

response) depending on their molecular composition, texture, size and

shape. A major limitation in most remote sensors especially when used

to acquire information in-situ is that they provide only spectral re-

sponse of objects on the earth's surface. However, analytical spectral de-

vices (ASD) e.g., the FieldSpec exist which may be used to acquire

spectral reﬂectance of soils below the earth surface in the laboratory.

Remotely sensed data may be acquired using ground, aerial or

space based sensors. Ground based systems although applicable for

small area assessments are not only accurate, but have relatively

less atmospheric errors due to decreased atmospheric path length,

compared with aerial or satellite sensors which in turn are suitable

for regional mapping. Ground based sensors are useful for calibrating

airborne or satellite sensor data. Table 3 provides some examples of

the relative instrumentation cost of ground based sensors e.g., the

ASD FieldSPec (350–2500 nm) and CropScan 16 handheld multispec-

tral radiometer (MSR), airborne and satellite sensors, all of which

vary depending on the spatial, spectral or temporal resolution speciﬁca-

tions (Ben-Dor et al., 2008; Chang et al., 2005; Reeves, 2010; Rossel et

al., 2009). Coarse spatial resolution satellite data are generally less ex-

pensive than higher resolution, exception include the moderate spatial

resolution satellite sensors such as Landsat which are currently free

(Roy et al., 2008).

Depending on the energy sources involved in the data acquisition, re-

mote sensing imaging instruments may be classiﬁed as active (i.e. emits

and detects own energy, to and from target), or passive (i.e. sun is the

energy source). The use of passive optical remotely sensed spectral

band data from the visible and near infra-red is being proposed to

characterize soil quality (Bricklemyer and Brown, 2010; Croft et al.,

2012; McCarty et al., 2002; Stevens et al., 2008). However, among the

principal limitations of individual spectral bands are that they are not

only highly correlated, but also vary with solar zenith, and topographic

slope (Holben and Justice, 1981; Huete, 1987; Huete and Tucker, 1991).

An alternative would be to use spectral indices which are neither highly

correlated nor inﬂuenced by solar zenith (

Haboudane et al., 2004; Huete

et al., 1994). Spectral indices are computed from a combination of

individual spectral bands from at least two different regions within the

electromagnetic spectrum.

The SOC, a proxy of soil quality may be measured in the laboratory

or ﬁeld and upscaled to cover large spatial extents using aerial or sat-

ellite data (Fig. 1). This can be undertaken for example, by measuring

the spectral reﬂectance of surface and sub-surface soils in the labora-

tory, and then developing models that relate SOC concentration at

different depths with reﬂectance. These developed models can then

be applied directly on to the satellite data to extrapolate over large

spatial extents. Alternately, the temporal variability in crop yield may

also be a useful indicator of soil quality variability across space and time.

Validation of remotely sensed products with actual ﬁeld measure-

ments may be conducted through statistical analysis, for example by

regression analysis or analysis of variance (ANOVA). If a large disagree-

ment exists between modeled estimates and actual measurements, the

algorithm or model is modiﬁed and re-applied to generate another SOC

estimate which is evaluated, and the process of model and product de-

velopment repeated until satisfactory results are achieved. The accuracy

of the ﬁnal SOC values is noted for comparison, or reference. A discus-

sion of the methods applied to measure SOC an indicator of soil quality,

and limitations are provided in the next sub-section and summarized in

Table 4.

2.1. Laboratory methods

The Walkley and Black (1934) method is a chemical oxidation

procedure for measuring SOC concentration. Although the Walkley–

Black procedure is simple, rapid with minimal equipment needs, the

results may vary depending on the land uses, soil depth, and soil tex-

ture (De Vos et al., 2007). On the other hand, the dry combustion (DC)

instruments e.g., the Vario Max CN analyzer (Nelson and Sommers,

1996), give the C/N ratios, and determine the SOC concentration of

air-dried and sieved soils (e.g., through 2 mm diameter) at 900 °C. Al-

ternately, the weight loss on ignition method is a DC approach that

gravimetrically determines SOC from soil samples heated in a furnace

at 430 °C, for 24 h (Chatterjee et al., 2009). The Walkley–Black, and

the weight loss on ignition method provide SOC estimates that vary

widely with soil depths. Tivet et al. (2012) demonstrated that the

DC method provides less uncertainty in SOC estimates for different

land uses and depth compared with Walkley–Black, and proposed con-

version equations from Walkley–Black to DC.

Laboratory methods such as the DC compute SOC as mass fraction

by weight (g kg

− 1

)(Smith and Tabatabai, 2004a). For estimating the

spatial variation in SOC, it is important to initially determine the soil

Table 3

Estimated acquisition cost for remotely sensed data.

Ground sensor (spatial resolution depends

on height of sensor above ground)

Wavelength

range (nm)

Purchase price

for sensor(US $)

ASD Fieldspec Pro JR

350–2500 42,000–70,000

Apogee/StellarNet SPEC-PAR/NIR 350–950 3600

StellarNet EPP2000-NIR-InGaAs 580–1700 17,000

Cropscan 16 MSR 450–1750 3000

Airborne sensors

Spatial

resolution (m)

Imagery cost

(US $ km

− 2

)

Aerial photo (scanned, non rectiﬁed) 1 4000–5000

Aerial photo (orthorectiﬁed) 1 10,000–15,000

Satellite sensors

Very high resolution b 4 3000–10,000

High resolution passive optical b 20 120–400

Medium resolution optical 20–250 0.08

Coarse resolution passive optical >250 0

Analytical spectral device (ASD). Source: http://www.usu.edu/cpl/PDF/Comparison

%20of%20Spectrometers.pdf.

Modiﬁed from Hyde (2005), Rogan and Chen (2004),andhttp://land.umn.edu/

documents/FS3.pdf. Prices do not include pre-ﬂight (e.g. transportation or GPS) costs

or post-ﬂight processing (optical airborne and satellite sensors acquire data in spectral

bands between 400 and 2500 nm wavelength range).

80 V. de Paul Obade, R. Lal / Catena 104 (2013) 77–92

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