Remote Sensing and GIS Techniques for Forest Resource Monitoring and Agriculture Potentiality

Sailesh Samanta

Department of Surveying and Land Studies

Papua New Guinea University of Technology Private Mailbag, Lae, Morobe, Papua New Guinea

rsgis.sailesh@gmail.com

Abstract- Remote Sensing (RS) and Geographic Information System (GIS) has demonstrated itself as a very powerful tool in forest and agricultural research and natural resource management. This study proposes an empirical methodology for analyzing and mapping of forest resource and agriculture potentiality using the RS and GIS techniques. The study is carried out over Morobe province in the Papua New Guinea. The forest resource monitoring, mapping and change analysis have been carried out using two sets of satellite data during 2005 to 2010 based on hybrid maximum-Normalized Differential Vegetation Index (NDVI) and minimum-red compositing technique. The paper also examines multi-criteria decision approach to determine rice cultivation suitability based on different variables, like topography, physical and chemical soil properties, climate and land accessibility that are mandatory inputs to land suitability model. These parameters are generated from Shuttle Radar Topography Mission (SRTM) data, soil data base, monthly and annual temperature and rainfall data, respectively. ArcGIS v-10 and Erdas Imagine v-11 model builder are used to construct the index model for agriculture suitability analysis. The entire study area has been classified into five categories of rice suitability. The result indicates that only four percent (4%) land can be demarcated as ‘very high’ and twenty one percent (21%) as ‘high’ suitability categories in the study area and the spatial expanse of all the five categories within the province are mapped and displayed. Keywords- Remote Sensing, Geographic Information System, land suitability, rice, vegetation cover

I. INTRODUCTION

Crop-land suitability analysis is a prerequisite to achieving best possible utilization of the available land resources for sustainable crop production (Perveen et al., 2007). One of the most burning requirements in Papua New Guinea (PNG) is to improve agricultural land management and to impart suitable cropping patterns in order to increase the agricultural production with rational use of land resources. This is paramount towards augmenting agriculture’s contribution of Papua New Guinea’s GDP, which is much below the inherent potential at present. The bulk of the Papua New Guinea’s population rely on subsistence farming of non-cereal-grain crops, such as taro, banana and sweet potato, and/or exploitation of sago palm, which constitute the

staple food for majority of the country. One of the most striking human ecological characteristics in Papua New Guinea is seen in different population densities in association with the environments where they have lived and the major foods which they have grown and eaten. In Papua New Guinea rice is one of the major foods, but the production is very low about two (2%) percent. In particular the rice production of PNG is hardly keeping pace currently with the rapidly rising demands due to its very low production and steeply rising demand from enlightened people who have started preferring (34 kg of rice /year /person) a swap from the traditional non-grain staple food. The situation perpetuates PNG’s dependence of import from south-east Asian countries and Australia. Instead of turning into a net exporter of rice by facilitating rice agriculture through identification of suitable rice growing areas, PNG continues to be heavily dependent on rice imports about ninety eight percent (98%). Hence this study has been envisaged with a view to determining physical land suitability for rice crop using a multi-criteria decision support system and GIS approach. Finally the utilization of the potentially rice-suitable lands vis-à-vis the current vegetation cover / land use on the same tract is explored using Remote Sensing technology. The aim in integrating Multi-criteria evaluation with Geographical Information Systems is to provide more flexible and superior mechanism to the decision makers in order to evaluate the effective factors. This research provides information at local level that could be used by marginal farmers to select cropping patterns in accordance with suitability.

II. STUDY AREA AND DATA USED

This research work attempts to develop index model to determine suitable land for rice cultivation in Morobe province of Papua New Guinea. The study area is bounded within 145° 30' to 148° E longitude and 5° to 8° S latitude. Various physical and chemical parameters are used for rice-land suitability analysis, they are topography (slope and aspect), physical (texture, water holding capacity and soil depth) and chemical (pH, nitrogen, potassium,

2012 Sixth International Conference on Sensing Technology (ICST)

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phosphorus) soil properties, climate (temperature and rainfall) and land accessibility. Optical bands with standard false color combination (Near-Infrared, Red and Blue bands) of Landsat-7, TM satellite images are used to find out the canopy cover classes in the study area. One of the most widely used digital elevation model (DEM) data sources is provided by the shuttle radar topography mission (SRTM) (Coltelli et al. 1996), but as with most other DEM sources, the SRTM data requires significant levels of pre-processing to ensure that there are no spurious artifacts in the data that would cause problems in later analysis such as patches of no data (Dowding et al. 2004). In the case of the SRTM data, these patches of no data are filled, preferably with secondary sources of DEM data, like toposheets. Both data sets are used to generate wall-to-wall DEM data set for the entire study area. Different statistical data are obtained from census of Papua New Guinea to carry out this research work. Climate Research Unit (CRU) of East Anglia University, UK has developed a database of monthly gridded (0.5° x 0.5°) climate observations globally using various techniques (Mitchell and Jones, 2005). Temperature and rainfall variables have been taken for this study. All other details of the variables are given in the table 1.

TABLE 1. DIFFERENT DATA USED FOR RICE LAND

SUITABILITY ANALYSIS

Variables Year Source Soil data 2009 Census of Papua New Guinea Toposheet 1960 University of Texas Libraries,

Austin SRTM data 2003 ftp://e0srp01u.ecs.nasa.gov Landsat-7, TM satellite image

2005 and 2010

University of Maryland Institute

Temperature and rainfall

1901-2002

http://www.cru.uea.ac.uk

Market access data base

2010 PNGUNITECH and NARI (National Agriculture Research Institute)

III. METHODOLOGY

Multi-criteria decision-making approach is a process where geographical data is combined and transformed into a decision through GIS. It involves input data process, the decision maker’s preference and manipulation of both information using specified decision rules. In this multi-criteria decision-making approach, the input data is geographical data. Topography, soil, climate and land accessibility are used for the land suitability analysis for rice cultivation.

A. Topographic database

First of all DEM is generated from topographical maps. Erdas Imagine v-11 is used to rectify topographical maps of the study area using the Universal Transverse Mercator (UTM) projection and WGS 84 datum with a RMS error of 0.021. Using the 3-D surface analysis process the digital elevation model is generated from contours and spot heights, which have been digitized from topographical maps. Prepared data sets in this process are compared with the SRTM data set to find out the accuracy of both data sets and to generate wall-to-wall DEM data set for the entire study area. Processing is made on a void by void basis for the entire study area. In cases when a higher resolution auxiliary DEM is available, point coverage is produced of the elevation values at the centre of each cell of the auxiliary DEM within void areas. Where high resolution auxiliary DEM is not available, the 30 second SRTM30 DEM is used as an auxiliary for large voids (Hutchinson (1988; 1989). The final DEM data is used to produce slope and aspect for the study area. The Slope tool is used to calculate the maximum rate of change between each cell and its neighbors. Every cell in the output raster slope data has a slope value. The lower slope value indicates a flatter terrain and higher the slope value as steeper terrain. An aspect tool is used to identify the steepest down slope direction from each cell to its neighbors. It can be thought of as slope direction or the compass direction a hill faces. Aspect is measured clockwise in degrees from 0, due north, to 360, again due north, coming full circle. The value of each cell in the aspect dataset indicates the direction of the cell's slope faces. Flat areas having no down slope direction are given a value of “-1”. ArcGIS v-10 is used to generate slope and aspect map of the study area using the DEM data. Flat and smooth surface are better for rice cultivation as it facilitates even and equal distribution of water.

B. Soil database

Physical and chemical data base of soil are prepared from soil region maps. In the United States, twelve soil texture classifications are defined by the United State Department of Agriculture (USDA), they are sand, loamy sand, sandy loam, silt loam, loam, sandy clay loam, silty clay loam, clay loam, clay, sandy clay, silty clay and Silt. Clay, silt clay, silt clay loam, textures of soil are best for paddy/rice crop cultivation. The effective depth of soil is defined as the thickness of soil above a layer restricting root growth (e.g. consolidated rock or cemented materials). Most annual crops have a rooting depth of about 50cm, while for tree crops the rooting system can reach beyond 150cm. However, most agriculture crops produce good yields in soils with an effective soil depth of about 100cm and this value has been used as an upper limit.

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The available water holding capacity of soil is the amount of water held in the soil between tensions corresponding to field capacity and permanent wilting point. The pH of the soil is defined as the negative logarithm of the hydrogen-ion concentration of the soil solution. A little acid soil having a pH value of 6 to 7 are better for paddy cultivation. However, it has been found to be grown in a wide range of pH varying from 4 to 8. The three major soil elements N, P, and K are generally correlated with plant growth and subject to rapid changes following forest clearing and cropping. For each case, we converted them into 3 groups according to their characteristics for rice cultivation. C. Climate data Temperature and rainfall are two climatic factors which has a favorable and favorable influence on the development, growth and yield of rice. Rice being a tropical and sub-tropical crop is normally grown at a fairly high temperature-rainfall region, ranging from 20° to 40°C and 1250mm to 2000mm of annual rainfall. Different raster data sets are generated by the spatial interpolation process using these climate variables of Climate Research Unit.

D. Market access data base

We consider market accessibility data set as one indirect variable for determination of suitable land for cultivation in the multi-criteria decision making model. Existing data base of market accessibility (Samanta et al., 2011) for Morobe province is used for this study. Village point, informal market, formal market, provincial capital market, telecommunication, airstrip transportation, major road transport and major wharf data set are used for this analysis. Proximity analysis is performed using village points to generate market access zone using all market access parameters. Overlay analysis of each market access zone with village point layer is processed to find out whether the villages are accessible to any kind of market or not. Then village points within each market access area are coded as “1” and village point out of market access area “2”. Statistical analysis (SUM) is performed of all coded village according to different mode of market access. Final village point layer is generated with total market access code and classified into three (3) market accessible ranks, like 7 to 9 as good market access (1), 10 to 11 as moderate market access (2) and 12 to 14 as poor market access (3).

E. Suitability rating

According to the degree of favorable environment for rice crop, simple statistical weighting/ratings were used for all the variables leading to multi-criteria decision support approach. We contrived three rating systems, like “1” as suitable, “2” as moderately suitable and “3” as unsuitable for all variables (Figure 1).

F. Land suitability analysis

Erdas-11 and ArcGIS-10 software were used to prepare topography layers (slope and aspect of the land), soil physical and chemical layers (texture, water holding capacity, depth, pH, nitrogen, potassium, and phosphorus), incumbent major climate feature layers (temperature and rainfall) and land access rating layers. We devised the relevant index model in the model maker using the multi-criteria decision-making approach. All those twelve variables in figure 1 were used as inputs in the index model. In the first step we produced the ‘topographic suitability’ using slope and aspect layer for the area, followed by ‘physical soil suitability’ using texture, water holding capacity and depth layer; ‘chemical soil suitability’ using soil pH, nitrogen, potassium and phosphorus layer; ‘climate suitability’ using temperature and rainfall layer; and finally ‘land access suitability’ composites. In the next step all suitable categories were stored in the temporary output memory file. Finally we used them as temporary memory input for rice crop land suitability analysis.

G. Forest cover mapping and analysis

Optical bands with standard false colour combination (SFCC) of LANDSAT 7 TM satellite image were used to find out forest density classes in the study area. Vegetation canopy cover data sets are generated (Figure 2) from satellite images using a hybrid maximum-Normalized Differential Vegetation Index (NDVI) and minimum-red compositing technique. The forest cover data has been reclassified into 5 classes, like less than 10 % canopy cover as open and water surface area, 10 to 30 % canopy cover as open or blank vegetation cover, 30 to 50 % as low dense vegetation cover, 50 to 70 % canopy cover as dense vegetation cover and more than 70 % as very dense vegetation cover. The present vegetation cover map and the suitability map for rice crop are overlaid to identify differences between the present vegetation cover and the potential suitable land for rice.

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Figure 1. Land suitability zones for rice crop cultivation, based on multi-criteria decision-making approach using slope and aspect of land, texture, water holding capacity, depth, pH, available nitrogen, potassium, phosphorus of soil, temperature, rainfall and land access of Morobe province, Papua New

Guinea by Erdas-11 and ArcGIS-10.

IV. RESULT AND DISCUSSION

All twelve variables, namely slope and aspect of the land, soil texture, soil water holding capacity, soil depth, pH, nitrogen, potassium, phosphorus, temperature, rainfall and land access, which are all used in the index model according to the suitability rating criteria. ArcGIS platform is used to prepare these rating maps after reclassifying all attributes for each variable. Statistics for each rating class are calculated for all twelve variables. The “rating” and “sum” functions are used in the index model to produce the final output map of suitable rice crop land for Morobe province (Figure 1). The dense forest resource (density >50%) has been changed from 2005 (76.28% of land area) to 2010 (73.57% of land area) due to deforestation for different economic development and logging activities. A 2.71% of dense forest (density >50%) has been reduced during 5 years (table 2).

TABLE 2. FOREST COVER CHANGES DURING 2005 TO 2010

Canopy cover (%) type

Area as 2005

Area as 2010

Area with More than 50 % canopy

Less than 10 3.94 % 4.72 % 2005

2010

10 to 30 9.94 % 10.38 %

30 to 50 9.85 % 11.33 %

50 to 70 21.65 % 23.09 % 76.28 %

73.57 %

More than 70 54.62 % 50.48 %

Change of dense forest during 2005 to 2010

2.71 %

We obtained useful information concerning the spatial distribution of different suitability levels in the study area in 1:250000 scale. This phase allowed us to fine-tune our results, because the resultant layer provided the information about how the rice can be cultivated across the various land suitability zones. The overlay analysis is

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performed between the rice suitability map and vegetation cover map of the study area for better understanding of their relationship. The index model predicts the inverse relationship between the percent of vegetation cover and rice land suitability. All the

medium, medium-low and low suitable lands are laid under dense vegetation cover (more than 50%), where as high and medium-high suitable lands are cited in the open vegetation cover (less than 50%) in the study area.

Figure 2. Vegetation cover as on 2010 derived using Landsat 7 TM satellite image.

V. CONCLUSIONS

Rice crop suitability analysis is carried out for entire Morobe province, Papua New Guinea. Spatial multi-criteria decision-making approach is used with twelve geographical data sets, as input in the model, namely slope and aspect, soil texture, water holding capacity, depth, pH, nitrogen, potassium, phosphorus, temperature, rainfall and land access. Markham valley region in the middle, north east part of the study area and major parts of Lae district comes under high to medium high rice suitable zone, where vegetation cover is very negligible. We received better result after cross checking our modeled output with the existing maps of Geobook data set of the study area. In the future study we can

attempt small scale mapping (national level) as well as large scale (district level) mapping on similar theme for Papua New Guinea with addition and further refined parameters.

ACKNOWLEDGEMENTS

Author expresses sincere gratitude to Papua New Guinea University of Technology & Department of Surveying and Land studies for providing GIS laboratory facility to carry out the research work. The authors are also grateful to the academic staff of GIS section and National Agriculture Research Institute for their valuable comments and suggestions.

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REFERENCES

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[2] CRU (Climate Research Unit), average temperature and rainfall data over 100 years, TS 2.1, http://www.cru.uea.ac.uk.

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[6] Mitchell, T. D. and Jones, P. D., “Aan improved method of construction a database of monthly climate observation and associated high-resolution grid", International Journal of Climatoogy, 25, pp. 693-712, 2005.

[7] Perveen, F., Ryota, N., Imtiaz, U., Hossain, K. M. D., “Crop land suitability analysis using a multicriteria evaluation and GIS approach, 5th International Symposium on Digital Earth”, The University of California, Berkeley, USA, pp. 1-8, 2007.

[8] Samanta, S., Pal, D. K., Antonio, W., and Pal, B., “Spatial Modeling and Interpolation to Establish Market Accessibility using ArcGIS v-10.0”, 4th Science and Technology Conference 2011, (27th June to 1st July), Vudal Campus, East New Britain, Papua New Guinea, pp. 8.

[9] SRTM (Shuttle Radar Topography Mission), elevation data on a near-global scale, ftp://e0srp01u.ecs.nasa.gov.

[10] USDA (United State Department of Agriculture), Soil texture classification, http://soils.usda.gov.

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