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Natural resources evaluation by the use of remote sensing and GIS technology for agricultural development
Adv. Space Res. Vol. 13, No. 11, pp. (11)117—(1 1)121, 1993
Printed in Great Britain. All rights reserved.
0273—1177/93 $6.00 + 0.00 Copyright © 1993 COSPAR
NATURAL RESOURCES EVALUATION BY THE USE OF REMOTE SENSING AND GIS TECHNOLOGY FOR AGRICULTURAL DEVELOPMENT C. Pham Viet* and M. Nguyen Phuong** * Institute of Geology and Geoinformatics, Free University Berlin, Malteserstrasse 74-100, W-1000 Berlin 46, Germany ** FAO Representation Hanoi, 3 Nguyen Gia Thieu, Hanoi, Vietnam
ABSTRACT
A multilevel Geographic Information System based on Remote Sensing and GIS—Technology is established to assess erosion susceptibility and select suitable land for soil conservation and regional planning, management. A cross analysis between the thematic maps and field data is done to examine the relationship between natural condition and land suitability for agriculture. The Land resources evaluation models are affective for understanding the cultivation possibility and can be used as a regional project to be applied in various Vietnam regions for agricultural development. INTRODUCTION Soil conservation and land use are becoming a major issue in developing countries, especially under high agricultural pressure. Vietnam is an agricultural developing country with a high population density. The problem of negative exploitation of forest and agriculture resources that creat soil erosion is of major concern, especially in hilly and mountainous area. In a report preapared in 1989 by Ministery of Forestry and General Department of Land Use and Management, it was estimated that every year more than 100.000 ha (about 1,2 % of total area of forest land) had been encroached. To resolve this problem in Vietnam at present, the trend is to promote agricultural infrastructure development using remote sensing and regional geographic information system. In most instances, the regional plan—makers carry out simultaneous interpretation, analysis and evaluation of multiple thematic maps in the decision making process. However, a successful extraction of such spatial information (maps or imagery) is often difficult due to the complexity of the information structure and a large numbers of data contained therein. Therefore a spatial analysis using sophisticated computers has been introduced. The objectives of this study were to define an operational method using remote sensing and GIS technology to overlay various thematic data in one standard reference system and evaluate land resources for agriculture development. It was applied in examples of erosion susceptibility mapping and selecting suitable land for paddy fields at the scale of 1:50000 to assist the planing of a land conservation for Bavi district. THE STUDY AREA Location, The study area was Bavi district, situated about 60 km to the South-West of Hanoi capital. The geographical coordinates are 21°.01’ — 21° .18’ latitude north and l05°.18’ — 105°.29’ longtitude east. This area was selected as it incorporates a complete catena of landforms and their related land cover/use types: from the mountains via the float slopes down into the plain with crops, paddy fields and swamps. The mountain Bavi, with a 1296 a Tanvien peak, is covered with rain forest, and many hills spread into the West, North, and East directions. Data used. The following data were used for the study area: —
-
Satellite data (Spot, Landsat MSS) and aerial photographs. The topographic map was used as a base map at the scale 1:50000, and in addition thematic maps in the same scale of geomorphology, soil, soil depth, and hydrology covered the study area. A number of field data, survey reports, crops yield, crops calendar, soil loss observation... were available for the evaluation. (11)1 17
(11)118
C. PhamVietandM.NguyenPhuong
THFJ4ATIC MAPS PRODUCTION AND GEOGRAPHIC DATABASE The land cover/use map It was produced from digital and visual analysis of Spot data and aerial photographs. Reliable results on the land cover/use were obtained by classifying separately into three main landscapes, already presented in the description of the study area: — — —
the mountainous forest zone 200 a — 1300 a the gently sloping hilly zone 50 m — 200 m the plain intensively cultivated zone 0 a — 50 m
This can be explained by the fact that special signature of a particular land use class varies quite a lot according to its environment (influence of the soil, exposition, climate) and therefore can be well characterized only within a rather homogeneous landscape. A maximum likelihood supervised approach, using ERDAS image processing software, was applied for classification. Particular attention was given to finding a procedure to improve the classification accuracy, by: — —
subdividing each thematic class into several appropriate spectral classes selecting each spectral class from 3 to 4 training areas spreading all over the study area.
These spectral classes were recoded into their land cover/use classes. Four trial classifications were performed before reaching a good classification result. The mapping accuracy obtained for the classes was estimated visually and generally found good ( average 80% ). The main confusions observed were crops with pasture; open forest, recultivated forest with bushes sometimes with some settlement areas. The final result was well obtained in spite of overlaying digital classification details in visual classification contour. Slove maps Both slope angle and length maps were constructed automatically in the geographic information System ARCINFO/ERDAS. The topographic map was digitized for the ARCINFO system for each 100 a
and 60m, 40 m, 20 a and 0 a elevation lines. A slope angle map was produced automatically using ERDAS software, Terrain Analysis, Slope. Calculation of the slope angle for each triangle was made from the length of the steepest slope and the altitude differece between the highest and the lowest point. Slope directions were generalized into 8 expositions: North, West, South, East, NE, NW, WS, SE. Slope length in this exposition were measured through scan slope lenght unit. Other thematic maps Other thematic maps (geomorphology, hydrology, ground water, soil, soil depth) existing from various maps already produced by traditional methods were checked with field data. They were then digitized with the HDG—digitizer (high resolution digitizer) into HOG—file. Each class was defined as an identificator and was created in HOD—file. To be used by the ERDAS system, they were converted in DIG—files. Then, the DIG—files were gridded into a GIS—file with the ERDAS system and registered to the data base. METHOD USED FOR ASSESSING SUSCEPTIBILITY TO SOIL EROSION
Model construction Several studies on problem related to soil erosion in Vietnam were carried out by traditional methods using data from some soil erosion observation stations. However the obtained results were limited in the qualitative estimation of potential soil erosion and were costly. The obtained results can be used only for small typical area. In general, for the hilly and mountainous areas of the study area, a well-known parametric equation, the USLE is currently used to assess soil loss due to water erosion. The USLE is written A = RKLSCP. This equation, explained in detail in Wishmayer and Smith [3] describes soil loss (A) as a function of: —
Rainfall factor (R) was after V.C.Lai calculated [ 4 1: R
=
~
(0.258 P
1 I~ — 0.149)
amount of i-rainfall greatest Intensity in the first 30 minuts of i—rainfall
(11)119
NaturalResouitesEvaluation
-
— — -
Soil erodibility factor (K) A monogram developed by Wishaeier et al (1971) was used to obtain the value of the K—factor related to texture, percentage organic matter, soil structure and permeability of the soil. The following erodibility categories were used as input for the erosion susceptibility model: Slope gradient (S) and length (L) Land cover/use factor (C) Land management factor (P)
However these factors used for the study were considered in tropical monsoon regions like Thailand, South China,.. .and checked out by field observation data and visual method for assessing susceptibility to erosion (4] This study covers the application of remotely sensed data and a geographic information System using the ERDAS software package. By combination of different parameters; climate, slope, soil erodibility, land cover/use, land management; an erosion susceptibility rate from 1.class, 1—05 ton/ha/annual (i.e. no erosion) to 6.class, over 200 t/ha/a (very high erosion) was attributed to each pixel. At an initial stage of the work, an analysis of loss measurement experiments in the surrounding study area, with similar conditions such as Bavi was realized: — Soil loss under different soil tillage systems for rice.., cultivation. —
Soil loss and management practices for coffee,... cultivation.
Computation of soil loss assesse.ent and discusion The simple modeling function defined as a product of each factor was applied. It was computed using the ERDAS software, module ALGEBRA to obtain the final soil loss assessment for each pixel (SOn * 50m). The result was then recoded into erosion classes as shown table 1. Figure 1: Soil erosion map
TA&E 1: Soil Erosion
~iaM 0. 1. 2. 3. 4. 5. 6.
No erosion Very slight Slight Moderate Moderate to strong Strong Very strong
Soil loss IT/tm/al 0 1 6 21 51 151
— — — — —
>
5 20 50 150 200 200
Area [hal 2130.25 1341.50 2398.25 2848.75 4238.75 960.50 2320.00
As a result, the maximum erosion risk seems to occur in the hilly areas, where the forest has been cleared for cultivation of crops and paddy fields. The erosion map in Figure 1 was then checked against some training areas and was found typical for the erosion process which was observed on the ground. These observations were also completed by means of interviews with
(11)120
C. Pham Viet and M. Nguyen Phuong
farmers. A few questions which arose were due to the errors of misinterpretation made on the land cover/use and land management. METhOD USED FOR SELECTION OF SUITABLE LAND Land resources evaluation: The evaluation method is performed by the following procedure (shown in Figure 2). A matrix analysis between thematic maps and training data (fields data) is done to calculate the probability of the necessary thematic maps data on paddy fields with different yields (high, medium and low yields). This probability calculation, and the planners’ knowledge concerning rice yields and agricultural suitability, are then chosen and used as training data to make appropriate evaluation criteria. Thematic data were recoded from the evaluation criteria into 4 ordinal suitable classes (good, normal, bad and unsuitable) appropriating the high, medium, low and no yields for consistency and ease in processing. Figure 2: Data evaluation Probability calculation on rice fields with different yields
User criteria
I
I
E1evation~ ISlopel
I
I
___
_______
____
Land use
Geo— morphology
I
I
____
existing condition
Soil
___
Soil depth
I
_____
potential productivity
_____
Distance to river
_____
Water availability
I Rice’ suitability
I
1I
Comparing Yield—suitability
For the evaluation, seven kinds of thematic maps given in Figure 2 were used. First stage evaluation can be done before obtaining a final results (second stage evaluation) to simplify the complicated procedure. One of the intermediate evaluations is the existing condition that indicates land workability/cover. Another is potential productivity, which is associated with soil and geomorphology condition. The next is water availability, which shows the water supply for rice fields. By the integration of these intermediate evaluation data, the land suitability for rice fields can be evaluated. Every area unit was evaluated into one of the four classes for rice suitability: good, normal, bad and unsuitable. Rice suitability map It is clear from the rice suitability map (see Figure 3) that rice can be cultivated well in the alluvial area. The lowland area Is evaluated to be moderately good through lacking irrigability. Hilly lands are suitable for rice but are difficult to work due to the land slope. The highlands and moutainous areas are unsuitable to rice fields. As a result, 979,8 ha (6,2% of the total areas) are very suitable to rice fields, 2169,8 ha (13,8%) are moderately suitable, 1268,8 ha (8,1%) are bad suitable and 15734,5 ha (71,9%) are unsuitable. Table 2 shows the evaluation result of rice suitable areas in comparison to the rice yields map. Certainly rice yields depend not only on natural conditions, but also on other social and economic conditions. Rice fields should be selected to be grown on the very suitable fields. Therefore, there are sufficient areas. Among 979,8 ha found good suitable in the evaluation map there area only 543,6 ha (about 39,6% of total 1371,6 ha rice fields) used for rice cultivation. Out of which 296,3 ha were used for rice cultivation with high yields, 218,8 ha with medium yields and 28,5 ha with low yields. The other 60,4% (828,0 ha) of total rice fields are found in moderately and poorly suited areas of the result map.
Natural Resoumes Evaluation
(11)121
From the comparison map between rice suitable areas and existing rice fields with different yields, it can be seen that rice fields can be relocated into highly suitable areas. Fimure 3: Rice suitability map
TA&.E 2:
Evaluation result: Rice suitability in area comparing with rice yield (in ha)
Suitability Good
Normal
Bad
Unsuitable
Total
Yield High
296,3
43,0
0,3
88,2
427,8
Medium
218,8
287,2
14,8
99,2
620,0
28,5
26,5
22,0
146,8
223,8
No
436,2
1813,1
1231,7
10981,9
14462,9
Total
979,8
2169,8
1268,8
11316,1
15734,5
Low
OGNCLUSION At present, disorderly exploitation of forest and agricultural resources and soil erosion is a major concern in Vietnam. These models and the methodology used in this study are useful for the determination of soil erosion and the selection of suitable land for paddy fields. The basic methodology (remote sensing data and GIS in combination with observation data and existing map data) developed in this study are highly adaptable to the conditions of developing countries and, therefore, could be rather easily transferred to other areas in Vietnam. it is recommended that for erosion protection and the rational use of forest and agricultural resources, appropriate methods should be introduced to local farmers in the areas which are heavily affected by erosion. REFERENCES 1. ERDAS Field guide, version 7.4, Jan. 1990, Raster GIS Modeling Example, ERDAS, mc, Suite, Atlanta. 2. Manu Omakupt, 1989, Soil Erosion Mapping Using R.S. Data and GIS, Proc. of 10th Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia 3. Smith and Wishmeler, 1972, Rainfall erosion, Agronomy 109—148 W 4. V.C. Lai, 1991, Soil erosion mapping in Vinh phu, Proc 3th science and technology, Hanoi, 5. C. Pham Viet, H. Wirth 1991, Fernerkundungs- und GIS-Technologien zur Uberwachung der Landnutzung eines tropischen Gebietes in Vietnam, Proc 6th remote sensing and monitoring, Pot adam, Germany.
Printed in Great Britain. All rights reserved.
0273—1177/93 $6.00 + 0.00 Copyright © 1993 COSPAR
NATURAL RESOURCES EVALUATION BY THE USE OF REMOTE SENSING AND GIS TECHNOLOGY FOR AGRICULTURAL DEVELOPMENT C. Pham Viet* and M. Nguyen Phuong** * Institute of Geology and Geoinformatics, Free University Berlin, Malteserstrasse 74-100, W-1000 Berlin 46, Germany ** FAO Representation Hanoi, 3 Nguyen Gia Thieu, Hanoi, Vietnam
ABSTRACT
A multilevel Geographic Information System based on Remote Sensing and GIS—Technology is established to assess erosion susceptibility and select suitable land for soil conservation and regional planning, management. A cross analysis between the thematic maps and field data is done to examine the relationship between natural condition and land suitability for agriculture. The Land resources evaluation models are affective for understanding the cultivation possibility and can be used as a regional project to be applied in various Vietnam regions for agricultural development. INTRODUCTION Soil conservation and land use are becoming a major issue in developing countries, especially under high agricultural pressure. Vietnam is an agricultural developing country with a high population density. The problem of negative exploitation of forest and agriculture resources that creat soil erosion is of major concern, especially in hilly and mountainous area. In a report preapared in 1989 by Ministery of Forestry and General Department of Land Use and Management, it was estimated that every year more than 100.000 ha (about 1,2 % of total area of forest land) had been encroached. To resolve this problem in Vietnam at present, the trend is to promote agricultural infrastructure development using remote sensing and regional geographic information system. In most instances, the regional plan—makers carry out simultaneous interpretation, analysis and evaluation of multiple thematic maps in the decision making process. However, a successful extraction of such spatial information (maps or imagery) is often difficult due to the complexity of the information structure and a large numbers of data contained therein. Therefore a spatial analysis using sophisticated computers has been introduced. The objectives of this study were to define an operational method using remote sensing and GIS technology to overlay various thematic data in one standard reference system and evaluate land resources for agriculture development. It was applied in examples of erosion susceptibility mapping and selecting suitable land for paddy fields at the scale of 1:50000 to assist the planing of a land conservation for Bavi district. THE STUDY AREA Location, The study area was Bavi district, situated about 60 km to the South-West of Hanoi capital. The geographical coordinates are 21°.01’ — 21° .18’ latitude north and l05°.18’ — 105°.29’ longtitude east. This area was selected as it incorporates a complete catena of landforms and their related land cover/use types: from the mountains via the float slopes down into the plain with crops, paddy fields and swamps. The mountain Bavi, with a 1296 a Tanvien peak, is covered with rain forest, and many hills spread into the West, North, and East directions. Data used. The following data were used for the study area: —
-
Satellite data (Spot, Landsat MSS) and aerial photographs. The topographic map was used as a base map at the scale 1:50000, and in addition thematic maps in the same scale of geomorphology, soil, soil depth, and hydrology covered the study area. A number of field data, survey reports, crops yield, crops calendar, soil loss observation... were available for the evaluation. (11)1 17
(11)118
C. PhamVietandM.NguyenPhuong
THFJ4ATIC MAPS PRODUCTION AND GEOGRAPHIC DATABASE The land cover/use map It was produced from digital and visual analysis of Spot data and aerial photographs. Reliable results on the land cover/use were obtained by classifying separately into three main landscapes, already presented in the description of the study area: — — —
the mountainous forest zone 200 a — 1300 a the gently sloping hilly zone 50 m — 200 m the plain intensively cultivated zone 0 a — 50 m
This can be explained by the fact that special signature of a particular land use class varies quite a lot according to its environment (influence of the soil, exposition, climate) and therefore can be well characterized only within a rather homogeneous landscape. A maximum likelihood supervised approach, using ERDAS image processing software, was applied for classification. Particular attention was given to finding a procedure to improve the classification accuracy, by: — —
subdividing each thematic class into several appropriate spectral classes selecting each spectral class from 3 to 4 training areas spreading all over the study area.
These spectral classes were recoded into their land cover/use classes. Four trial classifications were performed before reaching a good classification result. The mapping accuracy obtained for the classes was estimated visually and generally found good ( average 80% ). The main confusions observed were crops with pasture; open forest, recultivated forest with bushes sometimes with some settlement areas. The final result was well obtained in spite of overlaying digital classification details in visual classification contour. Slove maps Both slope angle and length maps were constructed automatically in the geographic information System ARCINFO/ERDAS. The topographic map was digitized for the ARCINFO system for each 100 a
and 60m, 40 m, 20 a and 0 a elevation lines. A slope angle map was produced automatically using ERDAS software, Terrain Analysis, Slope. Calculation of the slope angle for each triangle was made from the length of the steepest slope and the altitude differece between the highest and the lowest point. Slope directions were generalized into 8 expositions: North, West, South, East, NE, NW, WS, SE. Slope length in this exposition were measured through scan slope lenght unit. Other thematic maps Other thematic maps (geomorphology, hydrology, ground water, soil, soil depth) existing from various maps already produced by traditional methods were checked with field data. They were then digitized with the HDG—digitizer (high resolution digitizer) into HOG—file. Each class was defined as an identificator and was created in HOD—file. To be used by the ERDAS system, they were converted in DIG—files. Then, the DIG—files were gridded into a GIS—file with the ERDAS system and registered to the data base. METHOD USED FOR ASSESSING SUSCEPTIBILITY TO SOIL EROSION
Model construction Several studies on problem related to soil erosion in Vietnam were carried out by traditional methods using data from some soil erosion observation stations. However the obtained results were limited in the qualitative estimation of potential soil erosion and were costly. The obtained results can be used only for small typical area. In general, for the hilly and mountainous areas of the study area, a well-known parametric equation, the USLE is currently used to assess soil loss due to water erosion. The USLE is written A = RKLSCP. This equation, explained in detail in Wishmayer and Smith [3] describes soil loss (A) as a function of: —
Rainfall factor (R) was after V.C.Lai calculated [ 4 1: R
=
~
(0.258 P
1 I~ — 0.149)
amount of i-rainfall greatest Intensity in the first 30 minuts of i—rainfall
(11)119
NaturalResouitesEvaluation
-
— — -
Soil erodibility factor (K) A monogram developed by Wishaeier et al (1971) was used to obtain the value of the K—factor related to texture, percentage organic matter, soil structure and permeability of the soil. The following erodibility categories were used as input for the erosion susceptibility model: Slope gradient (S) and length (L) Land cover/use factor (C) Land management factor (P)
However these factors used for the study were considered in tropical monsoon regions like Thailand, South China,.. .and checked out by field observation data and visual method for assessing susceptibility to erosion (4] This study covers the application of remotely sensed data and a geographic information System using the ERDAS software package. By combination of different parameters; climate, slope, soil erodibility, land cover/use, land management; an erosion susceptibility rate from 1.class, 1—05 ton/ha/annual (i.e. no erosion) to 6.class, over 200 t/ha/a (very high erosion) was attributed to each pixel. At an initial stage of the work, an analysis of loss measurement experiments in the surrounding study area, with similar conditions such as Bavi was realized: — Soil loss under different soil tillage systems for rice.., cultivation. —
Soil loss and management practices for coffee,... cultivation.
Computation of soil loss assesse.ent and discusion The simple modeling function defined as a product of each factor was applied. It was computed using the ERDAS software, module ALGEBRA to obtain the final soil loss assessment for each pixel (SOn * 50m). The result was then recoded into erosion classes as shown table 1. Figure 1: Soil erosion map
TA&E 1: Soil Erosion
~iaM 0. 1. 2. 3. 4. 5. 6.
No erosion Very slight Slight Moderate Moderate to strong Strong Very strong
Soil loss IT/tm/al 0 1 6 21 51 151
— — — — —
>
5 20 50 150 200 200
Area [hal 2130.25 1341.50 2398.25 2848.75 4238.75 960.50 2320.00
As a result, the maximum erosion risk seems to occur in the hilly areas, where the forest has been cleared for cultivation of crops and paddy fields. The erosion map in Figure 1 was then checked against some training areas and was found typical for the erosion process which was observed on the ground. These observations were also completed by means of interviews with
(11)120
C. Pham Viet and M. Nguyen Phuong
farmers. A few questions which arose were due to the errors of misinterpretation made on the land cover/use and land management. METhOD USED FOR SELECTION OF SUITABLE LAND Land resources evaluation: The evaluation method is performed by the following procedure (shown in Figure 2). A matrix analysis between thematic maps and training data (fields data) is done to calculate the probability of the necessary thematic maps data on paddy fields with different yields (high, medium and low yields). This probability calculation, and the planners’ knowledge concerning rice yields and agricultural suitability, are then chosen and used as training data to make appropriate evaluation criteria. Thematic data were recoded from the evaluation criteria into 4 ordinal suitable classes (good, normal, bad and unsuitable) appropriating the high, medium, low and no yields for consistency and ease in processing. Figure 2: Data evaluation Probability calculation on rice fields with different yields
User criteria
I
I
E1evation~ ISlopel
I
I
___
_______
____
Land use
Geo— morphology
I
I
____
existing condition
Soil
___
Soil depth
I
_____
potential productivity
_____
Distance to river
_____
Water availability
I Rice’ suitability
I
1I
Comparing Yield—suitability
For the evaluation, seven kinds of thematic maps given in Figure 2 were used. First stage evaluation can be done before obtaining a final results (second stage evaluation) to simplify the complicated procedure. One of the intermediate evaluations is the existing condition that indicates land workability/cover. Another is potential productivity, which is associated with soil and geomorphology condition. The next is water availability, which shows the water supply for rice fields. By the integration of these intermediate evaluation data, the land suitability for rice fields can be evaluated. Every area unit was evaluated into one of the four classes for rice suitability: good, normal, bad and unsuitable. Rice suitability map It is clear from the rice suitability map (see Figure 3) that rice can be cultivated well in the alluvial area. The lowland area Is evaluated to be moderately good through lacking irrigability. Hilly lands are suitable for rice but are difficult to work due to the land slope. The highlands and moutainous areas are unsuitable to rice fields. As a result, 979,8 ha (6,2% of the total areas) are very suitable to rice fields, 2169,8 ha (13,8%) are moderately suitable, 1268,8 ha (8,1%) are bad suitable and 15734,5 ha (71,9%) are unsuitable. Table 2 shows the evaluation result of rice suitable areas in comparison to the rice yields map. Certainly rice yields depend not only on natural conditions, but also on other social and economic conditions. Rice fields should be selected to be grown on the very suitable fields. Therefore, there are sufficient areas. Among 979,8 ha found good suitable in the evaluation map there area only 543,6 ha (about 39,6% of total 1371,6 ha rice fields) used for rice cultivation. Out of which 296,3 ha were used for rice cultivation with high yields, 218,8 ha with medium yields and 28,5 ha with low yields. The other 60,4% (828,0 ha) of total rice fields are found in moderately and poorly suited areas of the result map.
Natural Resoumes Evaluation
(11)121
From the comparison map between rice suitable areas and existing rice fields with different yields, it can be seen that rice fields can be relocated into highly suitable areas. Fimure 3: Rice suitability map
TA&.E 2:
Evaluation result: Rice suitability in area comparing with rice yield (in ha)
Suitability Good
Normal
Bad
Unsuitable
Total
Yield High
296,3
43,0
0,3
88,2
427,8
Medium
218,8
287,2
14,8
99,2
620,0
28,5
26,5
22,0
146,8
223,8
No
436,2
1813,1
1231,7
10981,9
14462,9
Total
979,8
2169,8
1268,8
11316,1
15734,5
Low
OGNCLUSION At present, disorderly exploitation of forest and agricultural resources and soil erosion is a major concern in Vietnam. These models and the methodology used in this study are useful for the determination of soil erosion and the selection of suitable land for paddy fields. The basic methodology (remote sensing data and GIS in combination with observation data and existing map data) developed in this study are highly adaptable to the conditions of developing countries and, therefore, could be rather easily transferred to other areas in Vietnam. it is recommended that for erosion protection and the rational use of forest and agricultural resources, appropriate methods should be introduced to local farmers in the areas which are heavily affected by erosion. REFERENCES 1. ERDAS Field guide, version 7.4, Jan. 1990, Raster GIS Modeling Example, ERDAS, mc, Suite, Atlanta. 2. Manu Omakupt, 1989, Soil Erosion Mapping Using R.S. Data and GIS, Proc. of 10th Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia 3. Smith and Wishmeler, 1972, Rainfall erosion, Agronomy 109—148 W 4. V.C. Lai, 1991, Soil erosion mapping in Vinh phu, Proc 3th science and technology, Hanoi, 5. C. Pham Viet, H. Wirth 1991, Fernerkundungs- und GIS-Technologien zur Uberwachung der Landnutzung eines tropischen Gebietes in Vietnam, Proc 6th remote sensing and monitoring, Pot adam, Germany.