towards an efficacious method of using landsat tm imagery

YANG et al. 227

Tropical Ecology 48(2): 227-239, 2007 ISSN 0564-3295 © International Society for Tropical Ecology www.tropecol.com

Towards an efficacious method of using Landsat TM imagery to map

forest in complex mountain terrain in Northwest Yunnan, China

XUEFEI YANG1*, ANDREW K. SKIDMORE2, DAVID MELICK1, ZHEKUN ZHOU1 & JIANCHU XU3

1Kunming Institute of Botany, Chinese Academy of Sciences, Heilongtan, Kunming, P.R. China 650204 2International Institute for Geoinformatic Science and Earth Observation, 7500AA, Henglostraat

Enschede, The Netherlands 3World Agroforestry Centre, 12 Zhongguancun Nan Dajie, Mail Box 195, Beijing 100081, China

Abstract: Mapping forest type using Landsat TM images encounters many problems especially when applied in montane landscapes with complex terrain. In this paper we evaluated the effects of selected data inputs and classification methods on the accuracy of forest type mapping in a complex terrain landscape in mountainous southwest China. Results show that the accuracy of a forest type map produced by the original Landsat TM bands data alone is not acceptable, but the integration of topographic data with Normalised Difference Vegetation Index (NDVI) and Principle Components (PCs) improves the mapping accuracy by 15% and 14%, respectively. In addition, the comparison of two-classification methods showed that a GIS expert system (EXPERT) outperforms the maximum likelihood classifier (MLC) by 9%. It is concluded that combination of topographic data together with NDVI or PCs enable production of more reliable and accurate forest maps in landscapes with complex terrain. Where reliable field knowledge is available, expert systems show potential for producing affordable forest type maps as accuracy as those obtained by conventional classifiers.

Resumen: La elaboración de mapas de tipos de bosque por medio del uso de imágenes

Landsat TM enfrenta muchos problemas, especialmente cuando se hace en paisajes montañosos con un terreno complejo. En este artículo evaluamos los efectos que tienen distintos tipos de datos y métodos de clasificación sobre la exactitud de los mapas de tipos de bosque en un paisaje de terreno complejo en el montañoso suroeste de China. Los resultados muestran que la exactitud de un mapa de tipos de bosque producido solamente con los datos de las bandas originales de Landsat TM no es aceptable, pero la integración de datos topográficos con el Índice de Vegetación de Diferencia Normalizada (NDVI) y el Análisis de Componentes Principales (PCs) mejora la exactitud de los mapas en 15% y 14%, respectivamente. Además, la comparación de dos métodos de clasificación mostró que un sistema experto de SGI (EXPERT) se desempeña mejor en 9% que un clasificador de máxima verosimilitud (MLC). Se concluye que la combinación de datos topográficos junto con el NDVI o el PC permite producir mapas del bosque más confiables y exactos en paisajes con un terreno complejo. Cuando se dispone de información de campo confiable, los sistemas expertos tienen un buen potencial para producir mapas asequibles de tipos de bosque tan exactos como los obtenidos por clasificadores convencionales.

Resumo: A elaboração de mapas de tipos florestais com recurso a imagens de satélite

Landsat TM enfrenta muitos problemas, principalmente quando aplicado a paisagens montanhosas com um terreno complexo. Neste artigo avaliam-se os efeitos da imputação de

* Corresponding Author; e-mail: [email protected]

228 LANDSAT FOREST MAPPING IN MOUNTAINS

determinados dados e dos métodos de classificação na precisão dos mapas de tipos florestais numa paisagem de terreno complexo no sudoeste da China. Os resultados mostram que a precisão de um mapa de tipos florestais produzido unicamente com os dados das bandas originais de Landsat TM, não é aceitável, mas a integração dos dados topográficos com o Índice Normalizado das Diferenças de Vegetação (NDVI) e a Análise de Componentes Principais (PCs) melhora a exactidão dos mapas em 15 e 14%, respectivamente. Alem disso, a comparação de dois métodos de classificação mostrou que um sistema inteligente de SGI (EXPERT) tem um desempenho 9% melhor que o classificador de máxima semelhança (MLC). Concluiu-se que a combinação de dados topográficos em conjugação com NDVI e PC permite produzir mapas florestais mais confiáveis e precisos em paisagens em terrenos complexos. Para os locais onde se dispõe de informações de terreno confiáveis, os sistemas inteligentes oferecem um bom potencial para produzir mapas de tipos florestais acessíveis tão exactos quando os obtidos por classificadores convencionais.

Key words: Forest type mapping, GIS expert system, matsutake mushroom habitat,

maximum likelihood classifier.

Introduction

Spatially explicit data sets play an ever-increasing role in understanding and protecting the world’s biodiversity and natural resources. In many cases, forest map is an indispensable tool for forest management; however, this information may be scarce, or insufficient to meet management requirements. In China, the lack of quality data often makes affordable forest mapping difficult. In Northwest Yunnan, there is an urgent need to evaluate the habitat of the economically important Matsutake mushroom, which is being exploited at an accelerated rate. However, most of the mapping practices are only able to identify very broad forest classes, which are little used in modeling Matsutake habitat because the mushrooms grow in a symbiotic relationship with specific forest trees, such as pine and oak (Ogawa 1975; Wang et al. 1997). Therefore, in order to analyze Matsutake habitat more accurately it is essential to develop a relatively detailed forest map that can be used to identify specific tree types. Mapping forest through satellite remote sensing has been intensively studied in a variety of landscapes using different data and classification techniques; however, problems still remain in improving the accuracy, especially in montane area (Cingolani et al. 2004; Dorren et al. 2003). Generally, mapping detail and accuracy can be improved by enhanced spatial and

spectral resolution (Mehner et al. 2004). However, higher cost and time-consuming methods may induce less popular use of these fine data. Despite the fact that the limited resolution of Landsat does not enable the acquisition of floristic details (Austin 2002; Franklin 1995; Guisan & Zimmermann 2000; Mehner et al. 2004), Landsat TM or ETM is still used in forest surveys by developing countries like China. Apart from spatial and spectral limitations, in montane landscapes, heterogeneous terrain make accurate mapping even more difficult. Firstly, the consequence of the terrain variations produces a complex, patchwork-like pattern of vegetation distribution (Horsch 2003). Secondly, it directly modifies the reflectance values detected by sensor (Dymond & Shepherd 2004). This may result in the problems that similar land covers appear with different digital values, or it may be the reverse. Moreover, land covers in shadow areas show less than expected reflectance (Riano et al. 2003). In order to overcome these problems, various models have been developed. These have been extensively reviewed by many researchers like Franklin (1995), Guisan & Zimmermann (2000) and Austin (2002). Some authors (Gu & Gillespie 1998; Riano et al. 2003; Shrestha & Zinck 2001) developed topographic normalization algorithms to mitigate the problem of terrain effects on the mapping accuracy. Nevertheless, ecologists or natural

YANG et al. 229

resource managers have rarely used these complex algorithms in practice. Moreover, the main difficulty in applying topographic corrections is related to the lack of standard and generally accepted models (Riano et al. 2003).

In this study, we assume that terrain features are important factors for forest distribution, hence combining terrain characteristics with Landsat data in classification practice will improve forest type mapping. In order to test this hypothesis, we carried out various classifications with differential data input by using the Maximum Likelihood Classifier (MLC) which is considered as a standard classification approach (Richards 1999). In contrast to previous studies where DEM were considered as ancillary data (Dorren et al. 2003), we treated terrain features as the major inputs for classification. In order to reduce data redundancy and for extracting major information Principal Components (PCs) and Normalized Difference Vegetation Index (NDVI) were used instead of original Landsat TM data. Apart from the pure algorithmic mapping approach such as MLC, there is also an increasing trend of incorporating expert knowledge in mapping processes (Liu et al. 2002;

Skidmore 1989; Skidmore et al. 1996; Vaiphasa et al. 2006; Yang et al. 2006). In this study, we also tested the efficacy of a GIS expert system (EXPERT) against the MLC in terms of mapping accuracy and sampling cost.

Study area

Northwest Yunnan is a unique and diverse natural and cultural landscape recognized as one of the world’s biodiversity hotspot areas (CI 2003). The region lies on the Yunnan-Guizhou Plateau in the southwest of China (Fig. 1). To the west it merges into the Himalayan mountain ranges, to the north onto the Tibetan plateau, while the south borders the temperate and sub-tropical lowlands of central and southern Yunnan. This mountainous region forms the upper reaches of three major river systems, the Nu Jiang (Salween River), the Lancang (Mekong River), and the Jinsha Jiang (the upper reach of the Yangtze River). This wide altitudinal and microclimatic ranges support a diverse flora and fauna. A numerous vegetation types exist in the area including alpine scrubs, alpine meadow, alpine

Fig. 1. Map showing the study area and satellite image.


and sub-alpine shrub, temperate conifer forest, mixed forest, deciduous broadleaf forest and evergreen broadleaf forest (Xu & Wilkes 2004). The area hosts over 7,000 species of vascular plants, 36 amphibians, 59 reptiles, 417 birds and 173 mammals accounting for 20.6%, 12.7%, 16.0%, 33.5% and 29.8%, of total China’s species, respectively (Ji 1999).

Jidi village study site

Jidi village (27°43′24″-28°9′54″ N, 99°32′12″-99°43′17″ E) (Fig. 1) is located in Shangri-La County in Northwest Yunnan. It is an area of approximately 200 km2 with elevations varying from 3,100 m to 4,200 m. The climate is monsoonal, being clearly distinct into dry (November to May) and wet seasons (June to October). Data collected from the Meteorology Observation Station of Shangri-La County (20 km south of Jidi village, elevation 3,200 m) from 1971 to 2002 shows the highest precipitation (162 mm), temperature (13.5°C), relative humidity (80%) and the minimum sunshine hours (101 h) occur in July or August. Total annual rainfall is about 654 mm and total annual sunshine are about 2145 h.

Annual mean temperature is 5.9°C and average relative humidity 69% (Fig. 2). There are 13 villages in the study area with an approximate population of 1600. All the residents are Tibetans, who traditionally manage both agriculture and livestock as agro-pastoralists. Barley is the main crop cultivated.

Major forest types

Vegetation in the study area is mainly alpine conifer forest. The forest type classes are specifically identified for Tricholoma matsutake (matsutake mushroom) habitat analysis in this study. They are classified into six categories, non-forest, broadleaved forest, fir forest, oak forest, oak-pine-mixed forest, and pine forest. Non-forest vegetation types include farmland, pasture, river, and bare soil. Pinus densata (Gaoshansong) is the dominant species in pine forests. This kind of forest generally occupies elevations between 3,200 m and 3,450 m (Wu et al. 1987), and always occurs on the south or southwest aspects. It is a major plantation species because it grows quickly. Oak forest mainly comprises Quercus pannosa

a b

c d

Fig. 2. Climatic conditions in Shangri-La County, China (an average from year 1971 to

2002): (a) average monthly precipitation (mm); (b) average monthly temperature (°C); (c)

average monthly humidity (%); (d) average monthly sunshine hours (h).

YANG et al. 231

(Huangbeili), a slerophylous evergreen species that generally occurs between 3,500 m to 3,700 m on a south slope (Jin 1981; Wu et al. 1987). Oak-pine-mixed forest is a transition between pine and oak forest types. The major trees in the fir forest are Picea likiangensis (Lijiangyunshan) and Abies georgei (Changbaolengshan). P. likiangensis occurs at the lower elevations, ranging from 3,100 m to 3,800 m, while A. georgei occupies higher elevations from 3200 m to 4100 m (Wu et al. 1987). Both of these species prefer northern aspects and areas with higher soil moisture. Fir-oak-mixed forest rarely occurs and was considered to be a part of fir forest. Broadleaved forest is a secondary forest type resulting from the degradation of primary oak forests; it is mainly found in gullies and covers only a small area in the study site. The major broad-leaved tree genera are Populus and Betula.

Methodology

Ground truthing and sample arrangement

The field survey was conducted during August to October 2003. A stratified sampling approach was employed with consideration on the undulating, dissected landscape to give an even distribution of plots across the topography. A total of 216 plot (size 25×25 m) records was made for the forest types, major topographic and landscape features. As almost half of the sampled plots were covered with pine forest we equalized the sample size among forest types and only used 163 samples (Table 1), of which 98 samples (60% of total samples) were randomly selected for training the MLC. For the expert system, no training sample was required. The remaining 65 samples were used for accuracy assessment.

Table 1. Number of sample used for forest type mapping.

Forest type Field

sample Training sample

Testing sample

Non-forest 25 15 10 Broadleaved 13 11 2 Fir forest 18 11 7 Oak forest 26 15 11 Oak-pine-mixed forest 16 9 7 Pine forest 118 37 28 Total 216 98 65

Data preparation

Landsat 7 TM images (path/row: 132/41, passing date 13th, Feb. 2002) were provided by the Kunming Institute of Botany, the Chinese Academy of Sciences. Among seven spectral bands, only six bands were used; band 6-thermal infrared wasn’t used because of its coarser spatial resolution (Table 2). A winter image was used, as only this was cloud-free. Images were geometrically corrected using scanned topographic maps. The geographic coordinate system was set as GCS_Krasovsky_1940 and projection system as Transverse Mercator (zone 47N).

Table 2. Correlation coefficients for six Landsat TM bands.*

Band 1 2 3 4 5 6

1 1.00

2 0.99 1.00

3 0.95 0.98 1.00

4 0.94 0.94 0.90

5 0.82 0.85 0.88 0.89 1.00

6 0.81 0.84 0.88 0.86 0.99 1.00

*Since the original band 6 was not used in this study due to its coarse spatial resolution, band 6 corresponds to the original band 7. Other bands remain the same as for the original Landsat TM bands code.

The DEM was generated using digitized

contour lines and high points from topographic map sheets for deriving slopes and aspects. Further, topographic features such as channels, ridges and plains were calculated with DEM, slope and aspect using the “topographic feature” model in ENVI environment (ENVI 4.0). Aspect was combined with DEM, to better indicate the likelihood of a certain forest type occurring at particular place. For example, oak always occured in south slopes above 3500 m, but this was not the case at lower elevations.

PCs and NDVI were used as surrogates of the original satellite image in order to reduce the number of bands used for the classification because four topographic datasets were added. For the GIS expert system classifier, however, it was easier to formulate the classification rules using NDVI rather than the satellite image and PCs. The first two principal components out of six principal components (PCA1 and PCA2) were used since they explained 98% of the total variance (Table 3).


Table 3. Eigenvalue and percentage of variance of six Principal Components.

Band 1 2 3 4 5 6

1 0.40 0.33 0.35 0.50 0.48 0.34 2 0.48 0.31 0.17 0.20 -0.63 -0.46 3 0.14 0.25 0.53 -0.77 -0.03 0.22 4 -0.70 0.09 0.62 0.28 -0.11 -0.16 5 -0.10 0.06 -0.11 0.17 -0.59 0.77 6 -0.30 0.85 -0.42 -0.08 0.10 -0.07

Principal component

1 2 3 4 5 6

Eigenvalue 5.508 0.357 0.106 0.024 0.003 0.001 Percentage of the total variance

91.80 5.95 1.77 0.40 0.05 0.00

Classification scheme and GIS EXPERT

system

MLC is one of the most commonly used classifiers that has been well described by

Richards (1999). Expert systems are computer programs that simulate behaviour of human expertise which are generally used in solving the problems of the geographic information systems (Skidmore et al. 1996; Stock 1987). The GIS expert system was programmed under ENVI environment by Natural Resource Department at the International Institute for Geo-informatics Science and Earth Observation (ITC), The Netherlands. It uses Bayesian theory (Aspinall 1992, 1993; Skidmore 1989; Skidmore et al. 1996) as the inference engine.

In the GIS expert system, we infer the presence of a certain forest type at a certain location (a hypothesis) based on a set of available evidence. The system requires users to input a set of rules which link hypothesis to evidence. These rules include a priori estimates of probability and the initial conditional probability. A priori

probability can be assigned based on knowledge (Skidmore 1989) or from estimation by an expert

Table 4. A priori estimates of probability and the initial conditional probability (given evidence for a forest type).

Hypothesis

Non-forest Broad-leaved Fir Oak Oak-pine-mixed Pine

0.10 0.15 0.15 0.15 0.10 0.35

-1.0 – 0 0.99 0.01 0.01 0.01 0.01 0.01

0 - 0.1 0.80 0.20 0.01 0.01 0.01 0.01

0.1 - 0.3 0.30 0.40 0.30 0.30 0.30 0.30

NDVI

0.3 -1.5 0.01 0.01 0.90 0.90 0.90 0.90

<3300 0.40 0.50 0.10 0.10 0.01 0.20

3301-3400 0.15 0.15 0.20 0.20 0.50 0.60

3401-3600 0.05 0.05 0.30 0.45 0.65 0.30

3600-3700 0.05 0.01 0.80 0.60 0.50 0.05

DEM

>3700 0.01 0.01 0.70 0.40 0.10 0.05

0 – 5 0.70 0.40 0.01 0.01 0.01 0.10

6 – 30 0.05 0.25 0.30 0.25 0.25 0.25

31 -40 0.05 0.01 0.30 0.45 0.45 0.40

Slope

41- 90 0.05 0.01 0.10 0.10 0.10 0.10

Ridge 0.01 0.05 0.10 0.10 0.10 0.10

Plane 0.60 0.20 0.30 0.30 0.30 0.20

Topographic features

Channel 0.10 0.99 0.20 0.40 0.40 0.20

<3500, N 0.05 0.20 0.05 0.01 0.05 0.20

<3500, S 0.10 0.30 0.05 0.05 0.25 0.30

<3500, no aspect 0.60 0.30 0.01 0.01 0.10 0.20

>500, N 0.01 0.01 0.70 0.20 0.30 0.15

>3500, S 0.05 0.01 0.20 0.60 0.60 0.15

Evi

dence

DEM+aspect

>3500, no aspect 0.05 0.01 0.20 0.20 0.20 0.20

YANG et al. 233

assigned on field survey. For instance, a priori probability for pine forest was assigned to 0.35 because approximately 35% of the study area was covered by pine forest. The initial conditional probability is the probability estimated by users that a certain forest type (hypothesis) Sa (a=1,…, n) occurs at location (or pixel) Xij with evidence. In this study our estimation was based on field observation and existing reports (Jiang 1980a, 1980b; Jin 1981; Wu et al. 1987; Yang 1990). The rules are given in Table 4 whose methodology details are reported by Skidmore (1989) and Skidmore et al. (1996).

MLC was carried out three times with different data inputs using: (a) original Landsat TM (MLC1); (b) a combination of topographic data with principal components (MLC2); and (c) a combination of topographic data with NDVI (MLC3). The GIS expert system (EXPERT) used the same input data as MLC3 (Table 5).

Table 5. Input data layers for four forest type classifications.

Classification Input data layers

MLC1 Landsat TM of 6 bands MLC2 PCA1, PCA2, DEM, slope, topographic

feature, combined DEM and aspect MLC3 NDVI, DEM, slope, topographic feature,

combined DEM and aspect EXPERT NDVI, DEM, slope, topographic feature,

combined DEM and aspect

Accuracy assessment and comparison

A set of randomly selected testing samples (n=65) was used for accuracy assessment. Confusion matrices for each classification were produced to show the agreement between the classification and testing samples. The proportion of the correct classification, including individual forest type accuracy and overall accuracy (Congalton 1991), Kappa coefficient and its variance (Cohen 1960; Congalton 1991; Skidmore et al. 1996) were computed. Differences between classifications were tested through a Z-statistics using Kappa coefficients (Cohen 1960; Congalton 1991; Skidmore et al. 1996). Apart from statistical evaluation, a further visual assessment based on field information was undertaken for investigating on how much of the actual ground situations were reflected in a produced map.

Results

Improved mapping accuracy

The overall mapping accuracies of MLC1, MLC2 and MLC3 are 51%, 66% and 65%, respectively. Both the MLC2 and MLC3 provided higher overall mapping accuracies over MLC1 by about 14% and 15% (Tables 6 & 7). The Z-test for Kappa shows that MLC2 and MLC3 are significantly different from MLC1 (Table 8). This suggests that the combination of topographic data with PCA and NDVI significantly improves the mapping accuracy from Landsat TM alone. Results showed that differences between MLC2 and MLC3 were not significant.

Comparison of MLC3 and EXPERT

The EXPERT yielded the highest overall mapping accuracy (75%), outperforming the MLC3 (66%) by 9% (Tables 6 & 7). However, the Z-test suggested that EXPERT and MLC3 classifications were at the same level.

Mapping accuracy for individual classes

Error matrices for the four classifications are presented in Table 6 (a-d). Comparing mapping accuracy by individual classes showed that the non-forest, oak forest and pine forest could be correctly classified. Four classifications (MLC1, MLC2, MLC3 and EXPERT) identified non-forest with accuracy of 100%, 100%, 100% and 80%; oak forest with accuracy of 45%, 91%, 100% and 73%; and pine forest with accuracy of 61%, 64%, 68% and 93%, respectively. However, all the classifications yielded unsatisfactory results for fir forest and pine-oak mixed forest. As for the broadleaved forest, comparison was not applicable since it had only two testing samples.

Discussion

The effect of integrating topographic data on

mapping accuracy

The map produced by MLC1 showed a great degree of fragmentation. Forest types appeared as numerous small and scattered patches, however, the patches became progressively more amalgamated with the MLC2 and MLC3 maps (Fig. 3). Fragmentation of the MLC1 was


unrealistic as judged from the field information. Furthermore, the low mapping accuracy (50.7%) and Kappa (0.30295) also suggested that the MLC1 produced a poor result. This low accuracy was probably due to the inability of Landsat TM alone in classifying the forest type at species level, since the spectral signatures of various forest

types were similar. These similarities are clearly illustrated in Fig 4. Franklin (1995) also pointed out this type of drawbacks when detailed floristic composition was mapped solely relying on Landsat TM.

In comparison with the MLC1, both the statistical and visual assessments supported that

Table 6. Error matrices for four classifications: (a) MLC1; (b) MLC2; (c) MLC3; and (d) EXPERT. Values in parenthesis are accuracies for individual forest type.

No of pixels from Reference a. MLC1 Non-forest Broadleaved Fir Oak Oak-pine-

mixed Pine Total

Non-forest 10(100%) 10 Broadleaved 1(50%) 1 Fir (0%) 1 1 2 Oak 2 5(45%) 10 17 Oak-pine-mixed 1 1 (0%) 2 Pine 5 4 7 17(61%) 33

No

of p

ixel

s fr

om

Cla

ssific

atio

n

Total 10 2 7 11 7 28 65 Overall accuracy: 50.77%

No of pixels from Reference b. MLC2 Non-forest Broadleaved Fir Oak Oak- pine-

mixed Pine Total

Non-forest 10(100%) 10 Broadleaved 1(50%) 1 Fir 3(43%) 1 4 Oak 3 10(91%) 9 22 Oak-pine-mixed 1 (0%) 1 2 Pine 1 7 18(64%) 26

No

of p

ixel

s fr

om

Cla

ssific

atio

n


No of pixels from Reference c. MLC3 Non-forest Broadleaved Fir Oak Oak- pine-

mixed Pine Total

Non-forest 10(100%) 10 Broadleaved 2(100%) 1 3 Fir 1(14%) 3 4 Oak 5 11(100%) 3 5 24 Oak-pine-mixed (0%) Pine 1 4 19(68%) 24

No

of p

ixel

s fr

om

Cla

ssific

atio

n


No of pixels from Reference d. EXPERT Non-forest Broadleaved Fir Oak Oak- pine-

mixed Pine Total

Non-forest 8(80%) 8 Broadleaved 1 2(100%) 0 Fir 3(43%) 2 5 Oak 1 8(73%) 2 11 Oak-pine-mixed 2(29%) 2 Pine 1 3 1 5 26(93%) 39

No

of p

ixel

s fr

om

Cla

ssific

atio

n


YANG et al. 235

the mapping accuracy was significantly improved by combining topographic data with PCs and NDVI. The increased accuracy can be attributed to the use of either topographic data or PCs and NDVI. However, use of PCA and NDVI alone were unlikely to have much improved accuracy. Earlier work has shown that NDVI cannot exempt the influence by terrain characteristics (Burgess et al. 1995). Rather, these data treatments were mainly used as surrogates for the original satellite data. Therefore, it seemed that increased accuracy of MLC2 and MLC3 was due to the topographic data sets. Similar results were also reported by Horsch (2003) which indicated that the use of topographic variables for forest habitats was very promising. Forest distribution was understandably related to topographic features as they determine the fundamental ecological site factors, such as nutrients, water content and light available. DEM was thought to be related with radiation, temperature, wind, relative humidity. Slope was related with soil moisture, wind, radiation, temperature, process intensity and frequency. Aspect was related to radiation, temperature and moisture (Horsch 2003).

Table 7. Overall mapping accuracy, Kappa values and Kappa variance for 4 forest type classifications.

Classification Overall accuracy

K K - variance

MLC1 50.77% 0.30295 0.00146

MLC2 64.62% 0.51945 0.00409

MLC3 66.15% 0.54675 0.00372

EXPERT 75.38% 0.64877 0.00373

Table 8. Z test results for pair-wise comparison of error matrix among four forest type classifications (significant difference at 95% confidence interval). Values in parenthesis are the Z-statistics.

MLC1 MLC2 MLC3 EXPERT

MLC1 -

MLC2 S (2.91) -

MLC3 S (3.39) NS (0.31) -

EXPERT S (4.80) NS (1.46) NS (1.18) -

S-significant; NS-not significant

Model performance and limitation -

comparision of MLC and EXPERT

Frequently, nonparametric classifiers were found to yield higher accuracies than parametric classifiers (Rogan et al. 2002). In this study, we compared the knowledge-based classifier, the EXPERT, with the MLCs. Although the Z-test for Kappa doesn’t suggest the GIS expert system to be significantly better than the MLC3, we still considered it’s output to be more reliable resulting from: (a) produced higher mapping accuracy; (b) better identified rare forest types, i.e. broadleaved forest, and difficult forest classes such as oak-pine-mixed and fir forest; and more importantly (c) best suits the real situation of the study area when compared the obtained map with field information.

The advantage of the EXPERT can be attributed to its ability in integrating the expert knowledge into the classification process. This has been explained by Nangendo et al. (2007) and especially it mitigates the illogical and unrealistic classification results by setting certain rules. In this study, it mainly helped at identifying difficult and rare forest types. For instance, we found that oak-pine-mixed forest was overestimated by MLC3, especially in northwest part of the study area in the form of a big patch of this forest (Fig. 3). However, our field information shows that oak-pine-mixed forest was a transition type from pine to oak, and it was sparsely distributed occuring mainly between 3300 m to 3700 m elevation range. At higher elevations (for instance in the northwest corner of the study area where the elevation is above 3700 m), there was less chance that this forest will occur, yet the MLC3 failed to correctly classify these areas. By comparison, the EXPERT avoided such a mistake by integrating field information by setting the rules appropriately. For identifying fir forest, elevation and aspect were two important factors determining its distribution. At elevations above 3700 m, the forest was dominated by fir regardless of the slope aspect, while at elevations between 3500 m and 3700 m fir forests were clearly demarcated from oak forest by the slope aspect; where the fir forests occurred on north aspects while the oak forests on south aspects. Because these distribution rules could not be incorporated in MLC3, this classification failed to identify fir forest in contrast to the EXPERT method that correctly identified larger areas of fir forest.


Fig. 3. Forest type map produced from 4 classifications: (a) MLC1; (b) MLC2; (c) MLC3; and (d) EXPERT.

YANG et al. 237

Fig. 4. Spectral profiles of forest types. Sample quality was a potential limiting factor

for MLC. This classification requires good representative samples of each class; however, obtaining good samples was not easy in practice as homogenous samples were frequently rare in nature (Cingolani et al. 2004). Moreover, due to interference of nearby classes, the spectral reflectance of site margins may alter the characteristics of the target class, thus it requires the researcher to select central sites in the sample class. Selection of central sites are difficult and increases time required for obtaining samples which seems to be hardly achievable in a mountain area with undulating terrain. In cases where a class distribution was long and narrow, or small in area (such as was the case with broadleaved forest and oak-pine-forest in the present study), it was even more difficult to obtain a representative sample. The dependence on good discrete samples was less important in the EXPERT system. Nevertheless, in terms of model limitations, the GIS expert system depends greatly on the validity of the expert knowledge. As we incorporate different sources of information and knowledge together and if they disagree then some form of subjective assessment was necessary. Thus, for the EXPERT system, it was important to evaluate and control the quality of the knowledge acquired. Azadi et al. (2007) has shown a plausible way for solving the problems of multi-source-expert-knowledge (heterogeneous experts’ opinions) through a multi-fuzzy model.

Conclusions

Complex terrain landscapes are often very

important in terms of biodiversity and resource management, yet the very nature of the landscape makes the acquisition of reliable mapping and survey data extremely difficult. This paper has illustrated the success of combining topographic information with PCs and NDVI as input data for improving map accuracy over the original satellite data. Our results suggest that the relatively simple incorporation of topographic data offer a potential method for developing acceptable forest maps in Northwest Yunnan. Furthermore, advantages of GIS EXPERT system in mapping forest types were evident. This enabled obtaining a better forest map in complex terrain landscapes. This method provides an efficacious way of using the most easily available information (namely, Landsat data together with acquired local knowledge) to produce affordable and accurate forest type maps in the mountainous regions.

Acknowledgements

This study was jointly sponsored by the Netherlands Fellowship Program and Knowledge Innovation Program of Chinese Academy of Sciences (Project Grant No. KSCX2-1-09-06). It was undertaken as part of MSc research at International Institute for Geo-informatics Science and Earth Observation, The Netherlands and Kunming Institute of Botany, Chinese Academy of Sciences, China. Sincere thanks are due to many professionals for their immense help during the research. They are: Mr. Nawang Norbu, Mr. Henk van Oosten, Dr. Jan de Leeuw, Dr. Iris van Duren, Dr. P. S. Roy, Dr. P.K. Joshi, and local villagers for their great helps and hospitalities during our field work.

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towards an efficacious method of using landsat tm imagery

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