Remote Sensing and GIS Modeling for Selection of a ... - asprs

Remote Sensing and GIs Modeling for Selection of a Benchmark Research Area in the Inland Valley Agroecosystems of West and Central Africa Prasad S. Thenkabail, Christian Nolte, and John G. Lyon

Abstract

Introduction and Background

This paper presents and illustrates a methodology for rational selection of benchmark research areas (or benchmark watersheds) for technology development research activities in the inland valley (IV) agroecosystems of West and Central Africa. This was done through a two-tier characterization approach. The Level 1 characterization involved macro-scale sub-continental-level secondary agroclimatic and soil datasets to produce 18 agroecological and soil zones (AESZ),each of over 10 million hectares, spread across West and Central Africa. The Level II characterization involved the use of Landsat TM or SPOThigh-resolu tion visible (HRV) "windows " within each Level I AESZ, as well as other spatial datasets to determine locations of the representative benchmark research areas. The focus here is a methodology for Level II characterization for benchmark research-area selection using SPOT HRV data, secondary GIS datasets, and detailed ground-truth data with GPs locations. The spatial datalayers were analyzed in a GIS modeling framework. The study was conducted in an area of 0.39 million hectares around Gagnoa, southwestern C6te d'lvoire which is located in A ~ s ~ n u m b16 e r(humid forests with acrisols). A toposequence oriented land-use/land-cover mapping was suggested and implemented. The spatial distribution of the 16 land-use classes was mapped across toposequence: uplands (40.1 percent of total geographic area), valley fringes (40.3 percent), valley bottoms (18 percent), and others (1.6 percent). The broad land-use/land-cover classes as a percentage of total geographic area (393112 hectares) comprised (1) 58.2 percent of areas in pristine humid forests, (2) 23 percent of areas in humid forest-cropland mosaic, and (3) 15.4 percent of areas in significant farmlands in humid forests. Expert knowledge was incorporated through an appropriate weighting criterion for classes in various land-use/land-cover datalayers and other spatial datalayers. GIs modeling was then performed on various spatial datalayers leading to the selection of representative benchmark research areas. It is expected that the research conducted or technologies developed in these benchmark research areas can then be extrapolated or transferred to other areas within the same agroecological and soil zones like AESZ number 16.

The cultivation of lands across the toposequence (uplands, valley bottoms, and valley fringes; see Thenkabail and Nolte (1996))in West and Central Africa is characterized by highly varying and interacting biophysical and socioeconomic factors. The inland valley (valley bottoms plus valley fringes) agroecosystems are greatly influenced by the morphometric characteristics of the valleys (Raunet, 1982; Andriesse et al., 1994),their cropping patterns (Izac et al., 1991),technologies used by farmers specific to their ethnic background (Becker and Diallo, 1992),and water harvesting techniques. Technology development research activities need to be conducted in representative benchmark research areas (or benchmark watersheds) that take into account these hosts of diverse and distinct biophysical and socioeconomic factors. This will enable the transfer of technologies developed in these representative benchmark research areas to similar, larger ecological zones. The lack of an appropriate approach to characterization leading to a rational basis for benchmark research area seIections constitute a major "bottleneck" to technology development research activities in Africa. For example, after several years of research within the framework of the Wetland Utilization Research Project (WURP) (Hekstra et al., 1983) of the International Institute of Tropical Agriculture (IITA), research sites in Bida (Nigeria) and Makeni (Sierra Leone) had to be terminated because of the irrelevance of their findings for cultivation of much of the larger regions within similar agroecological zones. Bida farmers used sophisticated technologies such as water management that are rarely to be found in other regions of the same agroecological zone across West and Central Africa. Further, the soil fertility at Bida was exceptionally low due to its very specific parent material (Nupe sandstone). In this paper, we focus on developing and illustrating a methodology for selecting representative benchmark research areas for inland valley (or lowland) agroecosystem research activities in West and Central Africa. Inland valleys are types of wetlands found along the lower order streams (especially in first- to fourth-order examples). They offer an extensive, fairly unexplored potential for agricultural production (Windmeijer

P. S. Thenkabail, is with the Center for Earth Observation (CEO), Yale University, P.O. Box 208109, New Haven, CT 06511 ([email protected]). C. Nolte is with the International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria. J. G. Lyon is with the Department of Civil Engineering, The Ohio State University, Columbus, OH 43210. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Photogrammetric Engineering & Remote Sensing Vol. 66, No. 6, June 2000, pp. 755-768. 0099-1112/00/6606-755$3.00/0

6 2000 American Society for Photogrammetry and Remote Sensing

and Andriesse, 1993). Their capacity to contribute to African food production, if utilized to a greater extent, should be substantial (Izac et al.,1991).These wetlands are variously called fadamas, dambos, mbugas, and vleis. Inland valley bottoms constitute 18 percent of the total geographic area of the humid forest zone, 9.1 percent in northern Guinea, 7.7 percent to 7.9 percent in southern Guinea savanna, and 10.2 percent in the derived savanna (Thenkabail and Nolte, 1999).Currently, only 8 percent to 20 percent inland valley bottoms and 15 percent to 22 percent inland valley fringes are exploited for farming, leaving vast areas with rich potential yet to be utilized (Thenkabail and Nolte, 1999).Inland valleys have higher crop yields than equivalent upland areas. For example, potential yields of rice in valley bottoms have been estimated at 2.3 t/ha compared to 1.5 ffha on upland fields (Izac et al.,1991).However, actual yields of various wetland crops still remain far below expectation. Also, during the dry season, inland valley bottoms and fringes are almost the only locations in the Guinea savanna zones where crops can be grown, outside of irrigated areas (Izac et al.,1991). Furthermore, the importance of studying the inland valleys needs also to be looked at in a global biogeochemical cycling as highlighted in the research agenda of the International Geosphere-Biosphere Program (IGBP, 1994), which states that the: "Darnbos make up a significant fraction of the landscape in moist savanna regions, but are individually too small to appear on vegetation maps. They are eequently the focus of land-use change, either being drained for dryland crop production, or flooded for paddy rice. There is a strong need to develop appropriate and cost effective methods for carrying out these studies, as well a methods for comparing the biogenic processes in different systems. The priority gases are methane and nitric oxide (NO), with nitrous oxide (N,O) and carbon monoxide (CO) possibly important in some circumstances."

The above factors stress the need to develop technologies that are adapted to farmers' needs and to sustain ecological conditions. Given the fact that the characteristics of inland valleys are known to vary dramatically within and across agroecosystems, it is inadequate to characterize only a few inland valleys as most conventional studies often do. Therefore, the prerequisite to technology development and testing for sustainable utilization of wetlands for food production is to understand these complex systems through systematic inventory, characterization, and mapping at scales that are suitable for developmental planning. Therefore, we propose a two-tier approach for characterization and benchmark research area selection. This strategy involves a combination of biophysical and socioeconomic research issues to be addressed at two spatial scales. During the first tier, Level I (or macro-level; scale of 500,000 or greater) agroecological characterization was conducted. This involved the use of continental- or subcontinental-level macro-scale datasets. During the second tier, Level 11 (regional; scale 2 50,000to 5 500,000)high-resolution (10- to 30-meter)remotely sensed datasets were used in combination with secondary geographic information systems (GIS), the Global Positioning System (GPS),ground truth, and expert-knowledge data. These datasets were incorporated into a GIS modeling framework for agroecosystem characterization, agroecologica and soil zonation, and re~resentativebenchmark research area selections. The two-tie; hierarchical approach was conceived to facilitate the design of appropriate technologies in the benchmark research areas that are able to sustain the highly varying resource base and, at the same time, be acceptable to smallholder farmers in the diverse socioeconomic and ethnic environment of West and Central Africa. This would facilitate extrapolation of research results to larger regions or the regionalization of research results (the "bottom-up" approach) for the

transfer of technology. Izac et al. (1991) include a third tier, Level I11 (or micro-level; scale 5 50,000) very detailed characterization. Stage Level 111is after the selection of benchmark areas and, hence, beyond the scope of this paper. The overall objective of this paper is to develop and illustrate a methodology for selecting benchmark research areas for inland valley agroecosystemsusing the holistic two-tier hierarchical approach. Specific goals within this overall objective were to (1) illustrate a methodology for Level 11toposequenceoriented land-cover and land-use characterization, (2) demonstrate a methodology for incorporating expert knowledge in various spatial datalayers, and (3)perform GIs modeling of various spatial datalavers for benchmark research area selection. ~ &technology e development research activity will be conducted in the representative benchmark research areas identified during ~ e v s1l1characterization for each of the 18 Level I agroecological and soil zones (AESZS) of West and Central Africa. In this paper, the benchmark research areas selected for AESZ number 16 (see 471338 in Plate 1)using SPOT HRv data and other GIS datasets will be discussed in detail.

Methods The study area is located around Gagnoa, southwestern Cbte d'Ivoire, and is covered by SPOT HRV K:47, J:338 of the SPOT grid reference system and encompasses 393,112 ha (Figure la, Plate I).The date of overpass of the satellite was 27 December 1990. The major settlements in the study area are Gagnoa, GuibBroua, Balayo, YakpBoua, and Niaouriyo (Figure la). The study area falls entirely within the agroecological and soil zone (AEZ) 16 of the Level I map (see 471338 in Plate I), which is part of the humid forest zone, with a bimodal rainfall pattern and a length of growing period greater than 270 days. It has Acrisols as the major soil grouping (Plate 1and Table 1).This zone is representative of 18 million ha of land in West and Central Africa (Table 1). Regional population density data for West and Central Africa became available starting in the early 1980s with the UNEPIGRID (1993) database, based on studies by Deichmann and Eklundh (1991) and with the Wetland Utilization Research Project (WURP) (Hekstra et al.,1983)(Figurelb). According to these data, the area around Gagnoa and GuibBroua, with 20 to 49 inhabitants/km2,has a considerably higher population density than the region around Balayo which has 10 to 19 inhabitants/km2and the southern part of the study area with 0 to 9 inhabitants/km2where Niaouriyo, and Yakpboua are the major settlements (Figure lb). Becker and Diallo (1992) reported a population density of 26 to 35 inhabitants/km2for the Gagnoa sous-prbfecturebased on 1988 census. The study area is part of the interior plains region of West Africa characterized by slightly dissected peneplains with inselbergs and mesas over basement complex formations (Figure lc). In the study area, seven different geological formations occur as parent material (BRGM, 1973).In decreasing order of aerial extent (Figure lc), these are granite, migmatite, gneiss, micaschist, sandstone, metamorphic tuff, and syenite. Granites are concentrated in the eastern portion of the study area, migmatites in the western part, micaschist occupy the center-north portions, and gneiss the center-south. Of the sample sites in inland valleys, 31 were located in granites, six in micaschists, eight in migmatites, four in gneisses, one in metamorphosed tuff, and four in syenites. The Level I map characterizes the region with ~ c r i s o l as s the major soil (Plate 1and Table 1). According to the Food and Agriculture Organization (FAO) soil map of the world (FAO/UNESCO, 1977),the northern and the southeastern part of the study area (Figure Id, map unit Af 24-2a) has ferric Acrisols (concurrent with the legend of FA01 UNESCO (1974))as the dominant soil unit, ferric Cambisols and rhodic Ferralsols as associated soil units, and Lithosols as PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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Plate 1. Level I (macro) agroecological and soil zones (AESZ) across West and Central Africa (also see Table 1). SPOTHRV and Landsat TM image locations for Level I I (regional) characterization in Level I zones are shown. Benchmark research areas are selected during Level I I characterization. This paper develops and illustrates the methodology for benchmark research area selection through characterization of the Gagnoa region, C6te d'lviore covered by SPOT HRV image (K:44, J:338) located in AESZ 16.

inclusions. The southwestern part (map unit Ao 59-la) has orthic Acrisols as the dominant soil unit; ferric Acrisols, ferric Arenosols, and rhodic Ferralsols as associations; and humic Gleysols as inclusions. The center-south part (map unit Af 5l/Za) has ferric Acrisols as the dominant soil unit with orthic Acrisols and distric Nitosols as associations. Soils conditions in this part of the area are limited by a petric phase layer of 25cm thickness within 1m of the soil profile, consisting of 40 percent of oxidic concretions, hardened plinthite, or other coarse fragments. The soil texture in most of the study area is coarse (165 percent sand and < 18 percent clay). A rough area calculation, using the composition rules developed by FA0 (1978),leads to the following distribution for soil units in the study area: (I) ferric Acrisols 48 percent (189,000 ha), (2)Rhodic Ferralsols 18 percent (72,000ha), (3) ferric Cambisols 1 7 percent (65,000 ha), (4) Lithosols 8 percent (33,000ha), (5) orthic Acrisols 4 percent (17,000ha), (6) distric Nitosols 2 percent (6,000 ha), (7) ferric Arenosols 2 percent (7,000 ha), and (8)humic hleysols 1 percent (4,000ha). The vegetation in the area is part of the tropical lowland rain forest. Cocoa is the most important cash crop with rice and maize as major food crops. Agricultural systems in the major part of the study area are market-driven in the early intensification phase. Therefore, the major factors driving intensification of the farming systems in much of the study area are good infrastructure (0.15 km roads/km2),accessibility to markets, and existence of a major road networks. Ground-Truth Data

Ground-truth data were collected from 57 inland valley transects involving separate data for valley bottoms and valley fringes within the same 100-m-wide strip transect. These transects data were gathered from 5 7 valley bottoms and 55 valley

fringes. In addition, 17 uplands were sampled. The geographical locations of these samples are shown in Figure la. The location of each ground-truth site was determined using a Global Positioning System (GPS) loo-SRVY model manufactured by Garmin, Inc. Land-use measurements were made in 100-mby 100-mplots -which corresponds to 25 pixels of the SPOT data along a transect covering the valley bottoms, valley fringes, and uplands. GPS location readings were taken at the center of the valley bottoms. Ten land-cover types (Table 2) were recorded at each sample site: water, trees, shrubs, grasses, cultivated farms, barren farms, barren lands, built-up arealsettlements, roads, and others. Different combinations of these land-cover types led to specific land-use classes (Tables 3 and 4). Other characteristics recorded at each inland valley sample site included valley bottom width (in meters), valley fringe width, transversal slope (degrees),stream order (number),and qualitative observations of the soil moisture status as well as the level of water management system prevailing in valley-bottom fields.

Results and Discussion Level I (or Macro) Characterization The first step was to characterize parameters critical to the land use of inland valleys (ws) and their uplands across West and Central Africa. This required a macro level stratification of the secondary agroecological data using a GIs. The objective was to map on a sub-continental (macro) scale broad agroecological and soil zones in the mandate area of the International Institute of Tropical Agriculture (IITA) in West and Central Africa. The Level I map (Plate 1and Table 1)was the result of combining two parameters: (1)IITA's agroecological zones and (2) major soil groupings according to the F A 0 classification. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Level I AEsz0

Agroecological zone According to IITA's Definition

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Northern Guinea savanna Southern Guinea savanna Southern Guinea savanna Southern Guinea savanna Southern Guinea savanna Derived savanna Derived savanna Derived savanna Derived savanna Derived savanna Derived savanna Humid forest Humid forest Humid forest Humid forest Humid forest Midaltitude savannae Midaltitude savannaf

Major FA0 Soil Groupingc

Aread (million ha)

Luvisols Luvisols Acrisols Ferralsols Lithosols Ferralsols Luvisols Nitosols Arenosols Acrisols Lithosols Ferralsols Nitosols Gleysols Arenosols Acrisols Ferralsols Nitosols

151-180 181-210 181-210 181-210 181-210 211-270 211-270 211-270 211-270 211-270 211-270 > 270 > 270 > 270 > 270 > 270

25.2 18.4 12.4 11.9 10.7 47.2 24.9 14.2 14.0 11.7 10.8 150.1 27.2 19.2 18.9 18.0 45.4 12.3

Notes: "AESZ: level I agroecological and soil zones. b ~length ~ of growing ~ : period. CNames refer to the soil classification scheme of FAOIUNESCO (1974). d ~ h area e figures are for West and Central Africa and were determined using the 'AREA' procedure of IDRISI (Eastman, 1992). Ohea distribution of LGP in AEZ 17 is: 151-180 days 11 percent, 181-210 days 9 percent, 211-270 days 59 percent, > 270 days 21 percent. f h e a distribution of LGP in AEZ 18 is: 151-180 days 2 percent, 181-210 days 5 percent, 211-270 days 53 percent, > 270 days 40 percent.

Code 1 2 3 4 5 6 7 8 9 10

Land-Cover Type Description water trees shrubs grasses cultivated farms barren farms barren lands built-up arealsettlement roads others

IITA's agroecological zones map (Jagtap, 1994)is based on an approach of agroecological zoning developed by the Technical Advisory Committee (TAC)of the Consultative Group of International Agricultural Research Centers (CGIAR).In this approach, lowlands below 800 m in elevation are separated from mid-elevation highlands, which occur above 800 m in elevation (IITA, 1992).The lowlands were further subdivided according to the length of growing period (Table 1).The concept of length of growing period fallows the lead of the FA0 of the United Nation's Agroecological Zones Project (FAO, 1978), where water availability and time determine the growing period. The time component constitutes the time span in number of days during a year when precipitation exceeds half the potential evapotranspiration, plus a period required to evapotranspire more than 100 mrn of stored soil moisture. The amount of water storage is mainly determined by soil texture. The soil data came from a digitized version of the FAOIUNESCO soil map of the world (FAOIUNESCO,1977).The soil units of that map were aggregated in this study to constitute 23 major soil groupings. Thereby, at Level I (macro-scale;scale of 1:500,000or above), the inventory area was stratified by two agroecologicalparameters (Table 1,Plate 1): PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

DISTRIBUTION OF ~ N D C O VTYPES E R IN THE 16 LAND TABLE 3. PERCENTAGE USE CLASSES FOR SPOT HRV K:44, J:338 COVERING THE REGIONOF GAGNOA, C ~ TD'IVOIRE E Code of Land-Use Classeso

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Notes: "See land-use class names in Table 5. bClass 3 (savanna vegetation) does not exist in this study area.

Length of Growing Period: The major factor determining a Level I zone was the length of growing period. These zones were northern Guinea savanna, southern Guinea savanna, derived savanna, and humid forest, which were developed by the Agroecological Studies Unit at IITA [Jagtap, 1994). Soil Groupings: Soil grouping is done based on distribution of soil units in each map unit according to composition rules developed by the FAO (1978). The digitized version of the soil map of the world produced by the FA0 in conjunction with the United Nations' Economic, Social and Cultural Organization (FAOIUNESCO, 1974; FAOIUNESCO, 1977) has 106 soil units for West and Central Africa (WCA)that have been merged into 23 major soil groupings such as Acrisols and Luvisols.

OF THE LANDUSE/LANDCOVER CLASSIFICATION SYSTEM USED IN THISSTUDY WITH THE USGS CLASS~FICAT~ON SYSTEM (ANDERSON€7 TABLE4. COMPARISON AL., 1976).

Classification of USGS

Classification System Used in This Study

Upland 1 significant farmlands 2 scattered farmlands

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herbaceous rangeland mixed rangeland

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mixed forest land evergreen forest land

mixed range land mixed forest land mixed rangeland

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3 insignificant farmlands

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6 4 4

wetland forest land forest land

2 3

agricultural land, or rangeland, or forest land

3

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33 43 33

forest land, or agricultural land, or rangeland

43 43

mixed forest land mixed forest land

33

mixed rangeland

61

forested land

14

transportation communication and utilities

Valley fringe 7 significant farmlands 8 scattered farmlands

2

9 insignificant farmlands

Valley bottom 10 significant farmlands 11 scattered farmlands 12 insignificant farmlands Others 13 water 14 built-up area/settlements 15 roads 16 barren land or desert land

4 4 2 3 6 6 6

wetland wetland wetland

5

water urban or built-up land urban or built-up land barren land

1

1 7

The above two-agroecological parameters were manipulated (union)using a GIS, which resulted in 18 agroecological and soil zones (Plate I), constituting the Level I map (Plate 1, Table 1). Each zone portrays a specific combination of these two factors and represents an area of over 10 million ha in West and Central Africa (Table 1).Agroecological and soil zones (AESZ) with less than 10 million hectares spread across WCA are not shown in the Level I map (Plate 1and Table 1).

Level II (or Regional) Characterization

The benchmark research areas need to be selected for each of the 18 Level I agroecological and soil zones determined in the previous section. This was done through Level I1 (regional or mesolevel; scale of r 50,000 to 5 500,000) characterization using SPOT high-resolution visible (HRV)satellite images along with number of other datasets in a GIS modeling framework (Figure 2). Regional characterization for selections of benchmark research areas for AESZ number 16 (Plate 1; see SPOT K:J or pathhow marked 47/338) is presented and discussed. A SPOT image in AESZ 16 (Plate 1, Table 1)covering an area of 0.39 million hectares around Gagnoa, southwestern CBte d'lvoire was selected. The SPOT image was georeferencedto the Universal Transverse Mercator (UTM) projection and geographic (latitude and longitude) coordinates using the ground-control points (GCPS) determined with a GPS receiver with an accuracy of about 1pixel (about 20 meter). The Level II study used a number of spatial data layers that were generated from SPOT images, and data from other sources (Figure 2). The SPOT image was digitally analyzed to extract toposequence-oriented land-uselland-cover (see the next section), road networks, and settlements. Data from other sources include population density from UNEP GRID (1993),zonal

boundaries from Level I characterization of this study, discharge and rainfall data from the national institutions, groundtruth data with GPS locations from this study, and expert opinion through a questionnaire to experts. All of these data layers were analyzed in a GIs modeling framework (Figure 2). ToposequenceOriented Lan&Use/LandCover Classes In the Humld Forest Zone The land-uselland-cover (Lac)classes (Table 4, Column 1)

were mapped for each component of the toposequence (valley bottoms, valley hinges, and uplands). Such an approach provided (1) LULC classes of relevance to specific users and (2) increased classification accuracies (Thenkabail, 1999). This classification system was designed to facilitate a comparison with the U. S . Geological Survey (USGS) cl classification system using remotely sensed data (Anderson et al., 1976) (Columns 2 and 3 in Table 4). The classification system uses a hierarchical approach to classify land use at different levels. The 16 land-use classes for the three components of the toposequence mapped in this study can be aggregated to appropriate Level I USGS LULC classes (Table 4). Thenkabail and Nolte (1996)proposed and implemented a unique method for rapid delineation of the different components of the toposequence (valley bottoms, valley hinges, and their uplands) using image enhancement, display, digitizing, and masking techniques. A "piecemeal approach" to classification (Thenkabail, 1999)was adopted to classify the three-band spectral information of SPOT data separately into upland, valley bottom, and valley fringe classes. The CLUSTRunsupervised classification algorithm of Earth Resources Digital Analysis System (ERDAS, 1998)was used. These clusters of the unsupervised classification were then assigned to unique spectral classes or themes through minimum-distance-to-means classification algorithms. Initial unsupervised spectral classes were PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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L.

~ I (b)

1 Significant farmlands, uplands 2 Scattered farmlamds, uplqids 3 Savanna vegetation, uplands 4 Wetland/Marshland, uplands 5 Dense forest, uplands 16 V-dense forest, uplands 7 Significant farmlands, frhges 8 Scattered farmlands, fringes 9 In-ficant farmlands, fringes .I0 SignXieant farmlands, bottoms .I1 Scattered farmlands, bottoms 4 12 Insignificant fandands, bottoms 13 Water .I4 Built-up area or settlements $ 1 5 Roads .I6 Barren land or desert

.

(c) Plate 2. (a) Land-use classes of uplands, valley fringes, and valley bottoms in the entire study area of 393,112 hectares in Gagnoa, C6te d'lviore. (b) Inland valley bottoms and their land-use classes in the entire study area of Gagnoa, C6te d'lviore. Valley bottom covered 1 8 percent (70,760 hectares) of the total geographic area (393,112 hectares). (c) Land-use classes of inland valleys and their uplands in one of the benchmark areas. This benchmark area is in the southwest of Gagnoa. (See legend for color key.)

TABLE

5. LANDUSE

D~STR~BUTION IN THE STUDYAREA.^^^

Full Study Area Color

Area (ha)

study area (percent of total)

Uplands Significant farmlands Scattered farmlands Savanna vegetationC Wetlands/marshland Dense vegetation Very dense vegetation

gray seafoam violet mocha rose red-orange

157,601 22,589 31,992 0 7,024 54,619 41,377

40.1 5.8 8.1 0 1.8 13.9 10.5

8 9

Valley fringes Significant farmlands Scattered farmlands Insignificant farmlandsd

white pine-green red

158,606 26,299 39,376 92,931

40.3 6.7 10.0 23.6

0.31 0.32 0.38

10 11 12

Valley bottom Significant farmlands Scattered farmlands Insignificant farmlandse

Cyan yellow magenta

70,638 11,490 19,058 40,090

18.0 2.9 4.9 10.2

0.29 0.33 0.35

13 14 15 16

Others Water Built-up area/settlements Roads Barren land or desert lands

Blue tan navy sand

6,268 358 2,703 2,194 1,013

1.6 0.1 0.7 0.5 0.3

-0.07 0.11 0.09 0.13

No. 1 2 3 4 5 6 7

-

Land-Use Category

--

Mean NDVI 0.29 0.34

-

0.25 0.34 0.39

--

Notes: T h e study area falls entirely into agroecological zone 16 of the Level I map (Figure 1 and Table 1). bFor the composition of land-cover types and their distribution in each land-use class see Tables 2 and 3. =Class3 occurs only in Guinea savanna,zones. dSpectral characteristic of vegetation in class 9 is similar to that of classes 5,6,and 12;the difference is mainly in the toposequence position. "Mainlyriparian vegetation; spectral characteristics of vegetation similar to classes 5,6,and 9;the differenceis mainly in the toposequence position.

savanna woodland), which was obviously absent in the humid forest zone of this study area (thereby,0 percent for class 3 in Table 5). In summary, Classes 5 or 6 were widespread in the humid forest zone but were mostly found only along the narrow strips of higher-order streams in the Guinea savanna zone. Classes 9 and 1 2 differed in canopy cover and vegetation characteristics in the Guinea savanna and humid forest zones. Across agroecological zones, the vegetation of class 12 was more vigorous than in any other class, which makes it easily distinguishable from them. Spectrally, these distinctions were quite dramatic during summer when upland vegetation was relatively drier compared to lowlands, which still had moisture. Class 3 was unique for uplands in the Guinea savanna zone. One additional upland class had been defined: wetland/marshland (class 4). These were inundated, swampy areas that do not belong to IVS and showed a varying degree of cultivation. There were four other classes: water or water bodies such as lakes and major streamflows (class 13),built-up areaslsettlements (class 14), roads (class 15),and barrentdesert lands (class 16). The spatial distribution of the 16 land-use classes (Table 5) in the entire study area is depicted in Plate 2a. The study area was composed of 40.1 percent uplands, 40.3 percent valley fringes, 18 percent valley bottoms, and 1.6 percent others (roads, settlements, etc.) (Table 5). The broad LULC classes as a percent of total geographic area comprised (Table 5 and Plate 2a) (1)58.2 percent of areas in pristine humid forests (combination of classes 5,6,9, and 12), (2) 23 percent of areas with humid forest-cropland mosaic (classes 2,8, and 111,and (3) 15.4 percent areas with significant farmlands in humid forests (classes 1,7,and 10). Valley Bottom Wldths, Stream Densltles, Stream Frequencies, and Settlement Mapping

The SPOT data offer rapid measurement of varying cross sections of streams (Table 6, Plate 2b). The area covered by valley

bottoms (18 percent) was relatively high as a result of large bottom widths with mean values of 89 m (first-ordervalleys), 164 m (second-order), 173m (third-order),and 244 m (fourth-order) (Table 6). The mean valley fringe widths were 300 m (firstorder valleys), 303 m (second-order), 355 m (third-order),and 436 m (fourth-order).The variability of bottom widths within a stream order was very high, notably along the lower-order valleys. This can be expected because ground-truth observations were randomly collected at one transect. If one moves upstream or downstream from the chosen transect, the variability in width of the valleys is usually significant (as may be inferred from the standard errors shown in Table 6). However, remotely sensed data may be used to map this variability (Plate 2b). Also ground measurement requires enormous time and resources. SPOT data were used to determine inland vaIley density (length of inland valleys in km in a watershed to the area of watershed in km2)and frequency (number of inland valleys to the area of watershed in km2;Plate 2b). Hekstra et al. (1983) classified the stream densities (0.97 number/km2) as medium and the stream frequencies (0.75 km/km2)as coarse for this study area using topographic maps. Satellite images can also be used to map major settlements in the study areas. The wide valley bottoms, along with medium stream densities, accounted f& a high percentage of the geographical area being covered by vallev bottoms (18 ~ercent). This confirms the hvuotheses of Izac i t al. (1991)thit the valley bottom area incriises dramatically in wetter zones (because this study area-SPOT 471338- is in AESZ 16;Table 1,Plate 1)compared with drier zones. For example, the valley-bottom area was determined to be 9.0 percent (Thenkabail and Nolte, 1995d) in the wetter Save Region of Benin Republic (see path:192 and row:54 in Plate 1)which is in the derived and southern Guinea savanna zones when compared with relatively drier zones of SikassoIMali and BoboDioulasso/Burkina Faso (see path:197 and row:52 in Plate 1) regions with 8.6 percent (Thenkabail and Nolte, 1995c)which is in the northern guinea savanna. Mokadem (1992) also used PHOTOGRAMMETRIC ENGINEERING 81REMOTE SENSING

TABLE 6. MORPHOLOGICAL CHARACTERISTICS OF INLAND VALLEYSIN THE GAGNOA REGION,COTED'IVOIRE. -

-

-

Ground-Truth Data SPOT Data Transversal Slope Serial Number

Stream Order

avgC s.e.' (degree)

nc

Bottom Widtha

Fringe Width

avg (m)

avg (m)

n

s.e. n

Bottom Width (m)"

Shape ~ a t i o ~

see.

a'Jg

n

avg

n

Notes: Significance levels: (Duncan) 1.A > B (0.10) 2. A > B-C (0.10); A > D(O.O1) 3. A > D (0.10) 4. A > C-D (0.10) OrZvalue for bottom widths measured on the ground and with SPOT data is 0.69 (n = 50, a = 0.01). bshape ratio = bottom width + fringe width. cn = number of observations; avg = average; s.e. = standard error.

remotely sensed data to determine the 15.7 percent valley-bottom area in the relatively wet Moyamba region of Sierra Leone, which falls into the southern part of the derived savanna zone. In contrast, valley bottoms were 5.4 percent in the drier Kabala region of Sierra Leone, which is in the northern part of the derived savanna zone.

barren farms) for valley bottoms, 24.4 percent (21.2 percent plus 3.2 percent) for valley fringes, and 17.1 percent (14.2 percent plus 2.9 percent) for uplands (Table 8). The trend in the cultivation intensities estimated both from SPOT images and from ground-truth data remained the same: valley bottoms have the highest percentage of cultivated areas followed by the valley fringes and the uplands. Because the ground-truth sarnpling was mainly done along the road network, some bias can be expected.

SPOT derived versus ground-truth derlved lan&use/landcover classes

Each land-use class is a mix of a number of land-cover types. Cultivated areas can be estimated by considering land-cover types (Table 2) presented in each land-use class (Table 5) as shown in the matrix (Table 3) (Thenkabail and Nolte, 1995a; Thenkabail and Nolte, 1995b).The intensity of cultivation was significantly higher for valley bottoms (20.6 percent) when compared with valley fringes (16.9 percent) and uplands (15 percent) (Table 7). This was mainly a result of extensive use of valley bottoms for rice cultivation. In comparison, the cultivated farmlands intensities using ground-truth data alone was 26.6 percent (21.1 percent cultivated farms plus 5.5 percent

Benchmark Research Area Selection The benchmark research areas for inland valley agroecosysterns research are selected through a GIs modeling of various spatial datalayers (Figure 2). All data layers in Figure 2 derived from SPOT data have been discussed earlier. Population density and zonal boundary datalayers used in Figure 2 were discussed in earlier sections as were the ground-truth data and ground-control point data layers. The first step was to appropriately weigh various factors

OF VALLEYBOTTOMS, VALLEYFRINGES. AND UPLANDS AND THEIR CULTIVATION STATUS IN THE STUDY AREA. TABLE 7. DISTRIBUTION

Valley-Bottom Area

Entire Study Area

As a percent of total geographic area (percent)

Valley-Fringe Area

Cultivated as a percent of total valley-bottom area (percent)

As a percent of total geographic area (percent)

Cultivated as a percent of total valley-fringe area (percent)

Upland Area As a percent of total geographic area (percent)

Cultivated as a percent of total up land area (percent)

Note: The study area faKentirely into agroecological zone 16 of the Level I map (Figure 1and Table 1). OF LANDUSE PATTERNS WITHIN A 100 BY 100-M PLOT ALONGTHE TOPOSEQUENCE IN THE STUDY AREA. TABLE 8. RELATIVEDISTRIBUTION

Uncultivated and Fallow Land Toposequence

Observations

A. valley bottom B. vallev fringe C. upl&d "

57 55 17

Farmland

trees (percent)

shrubs (percent)

grasses (percent)

subtotal (percent)

cultivated (percent)

18.5 22.7 50.7 (1)

36.3 41.5 18.0 (2)

14.7 5.0 4.4 (3)

69.5 69.2 73.1 (4)

21.1 21.2 14.2 (5)

barren (percent) 5.5 3.2 2.9 (6)

subtotal (percent) 26.6 24.4 17.1 (7)

Barren Landand Others

Total -(percent) 3.9 6.4 9.8 (8)

(percent) 100 100 100

Notes: Significant 1evels:l. A and B (0.05), A and C (0.01), B and C (0.01) 2. A and B (0.01), B and C (0.01) 3. Not significant 4. A and B (0.01), A and C (0.01) 5. A and B (0.01), A and C (0.01) 6. A and B (0.01), A and C (0.10) 7. A and B (0.01), A and C (0.01) 8. A and B (0.10), A and C (0.05) Statistics were calculated using the MEANS procedure and Duncan test of SAS (1988). Significant differences between any two groups (e.g., inland-valley bottoms versus uplands) were reported for each parameter (e.g., trees, shrubs) at 0.01, 0.05, and 0.10 levels. For example, trees were significantly different at 0.01 level between inland-valley bottoms (13.5 percent) and uplands (50.7 percent); the difference has been reported as A and C (0.01) above PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

(or classes or themes) in each datalayer for their potential for inland valley cultivation by seeking and using expert knowledge to accomplish this need to rate each of these spatial datalayers. Opinions were obtained by questioning expert members of the Inland Valley Consortium in West and Central Africa using standard forms. Thirty scientists with experience in agroecosystems of inland valleys and representing diverse expertise were requested to respond to the questionnaire. They represented four international agricultural research centers (II~AlNigeria,WARDA/CBte d'Ivoire, wsc/wAu/The Nethere ) seven national research institulands, and ~ ~ I F r a n cand tions in Benin, Burkina Faso, CBte d'Ivoire, Ghana, Mali, Nigeria, and Sierra Leone. Fifteen scientists responded. The drier the agroecological zone (or the fewer the number of growing days), the greater the need for the utilization of inland valleys and, therefore, the greater the potential for development and utilization. This is because in drier zones valley bottoms are often the only land units that have enough moisture for agriculture, especially during summer when uplands are very dry. Four categories for the length of growing period (days per year) (90 to 5 165, > 165 to I210, > 210 to 5 270, and > 270) were weighted. Typically, 5 km is the maximum distance most farmers are willing commute to cultivate inland valleys. Distance of inland valleys to roads and settlements were rated for four categories (0 to 5 2 km; > 2 km to I 4 km; > 4 km to 5 5 km; and > 5 km.). The greater the population densities and have the greater the pressure for food production, the greater will be the potential for inland valley utilization for agriculture. Four categories of population density (persons per kmz)(0 to 5 30, > 31 to I60, > 61 to 5 90, and > 90) were weighted. Land-use layers were rated separately for valley bottoms, valley fringes, and uplands. Areas of uncultivated lands with dense or very dense vegetation normally have lower chances of inland valley cultivation. This is because dense natural vegetation needs to be removed; this faces environmental opposition. Areas with intense cultivation often adjoin settlements, and/or well connected road networks, and/or markets. Inland valleys that are in the vicinity of intensely cultivated areas have better potential for exploitation as a result of increasing pressure on these resources from growing population and commercialventures. The opinions of experts vary considerably. For example, upland classes 5 or 6 or lowland classes 9 or 1 2 (Table 5) have very dense humid-forest vegetation with little or, in many areas, no farming. While rating these classes, some experts thought that the likelihood of these classes to be developed for cultivation was lowest because it involved clear-cutting (which is labor-intensive and costly), and caused environmentally sensitive problems (destruction of flora and fauna). In contrast, some other experts rated the same classes as the best for developing cultivation, because areas with the highest biomass offerthe best soils, thus arguing that the cost and labor involved in clear-cutting would be worth the effort. Rating upland classes is relevant to cultivation of inland valleys, as there is significant inter-relationships between cultivation in uplands and lowlands. For example, the valley fringes are more likely to be cultivated when they adjoin cultivated uplands. All factors (or classes or themes) in each data layer were weighted according to a scale of 1to 5. Factors that have the highest influencelimpact for Inland valleys were given the highest rating. The expert opinion showed that (1)inland valleys near existing cultivated uplands have the best chance of being exploited for cultivation. This supports the assumption that the greater the degree of upland cultivation, the greater the chances of inland valley development; (2) proximity of settlements to inland valleys has greater influence than closeness to road systems; (3) inland valley utilization for agriculture is likely to be maximum when population density rises above 30 persons/

km2;and (4)not utilized portions within inland valley systems currently exploited have a better chance of being developed. The above-discussed expert knowledge weights were incorporated into different spatial datalayers. A GIS modeling effort was made (Usery and Phyllis, 1988)using all data layers shown in Figure 2 and the GISMO routine of ERDAS. The resulting output spatial data layer showed areas with different sums. These constituted representative benchmark areas (Plate 3) for the agroecological and socioeconomic zone number 16. The potential benchmark research sites were characterized as Plate 3 and Table 7: (1)near-flat valley bottoms, (2) large valley bottom widths (about 100 m for first- to third-order valleys and about 400 m for the fourth-order valleys), (3)well connected road networks (typically within 5 km), (4)proximity to settlements (typically, within 5 km), ( 5 )rainy-season shallow inundation of flood water in valley bottoms, (6)mild to very mild transversal slopes (mean of about 1.5 degrees for first- to third-order valleys and a mean of about 0.5 degrees for the fourth-order valleys), and (7) large fringe widths (about 200 m for first- to third-order valleys and about 920 m for fourth-order valleys). A final selection of a single best site (or more than one best sites: (Plate 2c) could be done through ground assessments, including visits to one or more best sites. This would involve evaluation of additional factors such as social, ethnic, environmental, and economic issues, as well as the interests of National Agricultural Research Systems (NARS)collaborators in the potential areas or benchmark watersheds of interest. A team of researchers with various expertises, including agronomists, breeders, soil scientists, hydrologists, and remote sensing/~ISspecialists, could then visit the potential benchmark site for a rapid appraisal. The parameters assessed are moisture status of the valleys, issues of natural resource protection (flora and fauna inventory), accessibility to markets, socioeconomic and health issues, and cost and labor of clear-cutting. In addition, the interests of NARS in these sites and interviews with farmers are important. The outcome would be the selection of a final key benchmark site or a key benchmark watershed (Plate 2c) for technology development [Level 111)research activities.

Plate 3. Location of the potential benchmark areas in Gagnoa, CBte d'lviore determined through integration of SPOT HRV, secondary GIs, GPS, ground-truth, and expert-knowledge data in GIS framework. 1

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

agroecological and soil zones (AESZ)that they represent such as for AESZ 16.

Summary and Conclusions This study developed and illustrated a methodology for benchmark research area selections for the inland valley agroecosystems of West and Central Africa. The method involved GIs modeling using SPOT data and other spatial data layers from secondary GISdatasets, GPS, and ground-truth. The paper highlights incorporating expert knowledge in various spatial data layers to appropriately weigh factors that influence benchmark research area selection. The entire study area falls within a single Level I (macro) agroecologicd and soil zone 16 (AESZ 16 in Plate 1and Table 1).The AESZ 16 is in the humid forest agroecological zone with Acrisols as the major soil grouping and has an area of 18 million hectares across West and Central Africa (Plate 1and Table 1). The Level II (or regional) characterization was performed in a sub-area or "window" (see 471338 in Plate 1)of Level I AESZ 16 for selecting benchmark research areas. The methodology for benchmark research area selections was demonstrated in an area of 393,112 hectares, the size of a full SPOT image, in the humid tropic Gagnoa region of southwestern CGte d'Ivoire. A toposequence-oriented (separately for valley bottoms, valley fringes, and uplands) land-uselland-cover classification was developed using SPOT data. This approach to classification was useful (1)when "patchy resources" such as inland valleys need to be characterized and mapped, (2) to increase the number of distinct classes being mapped, and (3) to increase accuracv of the classes being mapped. The classification was deiigned to be compati61e with the USGS Anderson system. The 16 land-use classes - six for uplands, three each for valley fringes and valley bottoms, and four others - were mapped for the entire study area (Plate 2a and Table 5). The total area covered by humid forest vegetation with insignificant farmlands (land-use classes 5,6,9,and 12) was 58.2 percent, whereas the area with humid forest-cropland mosaic (land-use classes 2,8, and 11)was 23.0 percent of the total geographic area. The intensity of cultivation was significantly higher for valley bottoms (20.6 percent) when compared with valley fringes (16.9 percent] and uplands (15 percent). The characteristics of inland valleys in the region clearly indicated a vast potential for agricultural development. Over 80 percent of the valley bottoms and valley fringes are currently unexplored, and they offer wide bottom widths, adequate water resource, and are relatively easy to clear compared to uplands due to the presence of significantly fewer trees. The study area comprises 18 percent valley bottoms, 40.3 percent valley fringes, 40.1 percent uplands, and 1.6 percent others (e.g., roads, settlements) (Table 7). It was established that the area of inland valleys increased significantly in the wetter humid forest ecoregions when compared with other agroecological zones in Africa such as the southern Guinea savanna, the northern Guinea savanna, and the derived savanna. The inland valley stream density in the study area was determined to be 0.97 km/km2 (medium as per Hekstra et al. (1983)) and the stream frequencies were determined to be 0.75 streams per km2 (coarse as per Hekstra et al. (1983)). Expert knowledge was used to weigh various spatial datalayers (Figure 2) for their importance and potential for inland valley cultivation. GIS modeling of various spatial datalayers lead to selection of areas that are best suited for conducting technology development research activities (Plate 3) in inland valley agroecosystems. A final selection of a single best benchmark area or watershed (Plate 2c; showing a zoom of the benchmark area) can be done after a rapid appraisal of a few highest ranked benchmark areas (Plate 3) through field visits. The representative benchmark research areas will then be used for technology development research activities. The research conducted in such benchmarks is expected to facilitate extrapolation of research findings or technology transfer to larger PHOTOGRAMMETRIC ENGINEERING &

REMOTE SENSlNG

Acknowledgments Dr. Annie-Marie Izac, Director for Research at the International Center for Research in Agroforestry (ICRAF), was instrumental in initiating the Inland Valley Project for West and Central Africa. The Directorate General for International Cooperation (DGIS),The Netherlands, provided financial support for satellite imagery acquisition. The authors are grateful for the support of the Inland Valley Consortium (IVC) which constitutes the National Institutes of Benin, Burkina Faso, CBte d'Ivoire, Ghana, Mali, Nigeria, Sierra Leone, and five International Research Institutions; the West African Rice Development Association (WARDA); the International Institute for Tropical Agriculture (ETA); the Winand Staring Center (SC-DLO);the Wageningen Agriculture (IITA);the DL0 Winand Staring Center (SC-DLO);the Wageningen Agricultural University (wAu); and the Centre de Coopbration Internationale en Recherche Agronomique pour le DBvelopement (w). We would also like to thank Ms. Umelo for excellent editorial help, Mr. Babalola for secretarial assistance, and Mr. Ajuka and Mr. Wahab for assistance during ground-truthing. The three anonymous reviewers helped improve this paper. Discussions with Mr. Merrill Conitz are very much appreciated. Color images were sponsored by the Center for Earth Observation (CEO)at Yale University. Authors thank Prof. Ronald Smith, Director of CEO for the support.

References Anderson, J.R.,E.E. Hardy, J.T. Roach, and R.E. Witrner, 1976. A LandUse and Land-Cover Classification System for Use with Remote Sensor Data, Geological Survey Professional Paper 964, U.S. Government Printing Office, Washington, D.C., 28 p. Andriesse, W., L.O. Fresco, N,van Duivenboden,and P.N. Windmeijer, 1994. Multi-scale characterization of inland valley agro-ecosystems in West Africa, Netherlands Journal of Agricultural Science, 42:159-179. Becker, L., and R. Diallo. 1992. Characterization and Classification of Rice Agroecosystems in Cdte d'zvoire, WARDAIADRAO, Bouakb, CBte d'Ivoire, 56 p. BRGM (Bureau de recherches g6ologiques et minihres) 1973. Carte gdologique du sud-ouest de la C6te d'lvoire 1:500,000, Institut National Gographique, CBte d'Ivoire. Deichmann, U., and L. Eklundh. 1991. Global Digital Datasets for Land Degradation Studies, GRID Case Studies No. 4, GEMSIGRIDIUNITAR Africa Database, UNEPIGRID Nairobi, Kenya. Eastman, J.R., 1992. IDRISI-User's Guide Version 4.0, Clark University, Worcester, Massachusetts, 178 p. ERDAS, 1998. Field Guide, ERDAS Inc., Atlanta, Georgia, 392 p. FA0 (Food and Agricultural Organization of United Nations), 1978. Report on the Agro-Ecological Zones Project, Volume I, Methodology and Results for Africa, World Soil Resources Report 4811, Food and Agricultural Organization of United Nations, Rome, 158 p. FAO/UNESCO, 1974. Soil Map of the World, 1:5,000,000, Volume I, Legend, UNESCO, Paris, France. FAOIUNESCO. 1977. Soil Map of the world, 1:5,000,000, Volume VI, Africa, UNESCO, Paris, France. Hekstra, P., W. Andriesse, G. Bus, and C.A. de Vries. 1983. Wetland Utilization Research Project, West Africa. Phase I, The Inventory: Vol. I: Main Report, Vol. II: The Physical Aspects, Vol. III: The Agronomic, Economic and Sociological Aspects, Vol. IV: The Maps, WURP-Report, ILRIISTIBOKA, Wageningen, The Netherlands, 160 p. IGBP (International Geosphere-BiosphereProgram], 1994. Africa and Global Change, A Report from a Meeting at Niamey Niger, 23-27 November 1992, Stockholm, IGBP Report Series No. 29 (both English and French under the same cover), 55 p, each language. IITA (International Institute of Tropical Agriculture), 1992. Medium Term Plan 1994-1998, IITA, Jbadan, Nigeria. 136 p. lane

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IITA (International Institute of Tropical Agriculture), 1994. Zooming in on backyard resources (by Prasad S. Thenkabail and Christian Nolte). Satellite imagery pinpoints the potential of inland valleys. pp. 24-27 and pp. 31. IITA annual Report 1994, ISSN 0331-4340, IITA, Ibadan, Nigeria, 64 p. Izac, A,-M.N., M.J. Swift, and W. Andriesse, 1991. A Strategyforlnland Valley Agroecosystem Research in West and Central Africa, RCMP Research Monograph No.5, Resource and Crop Management Program, IITA, Ibadan, Nigeria, 24 p. Jagtap, S.S., 1994. Resource Analysis and Information System, RIS, User's Guide, Agroecological Studies Unit, IITA, Ibadan, Nigeria. Mokadem, A.I., 1992. Apports de la T616dBection d 1'6tude des basfonds. Cas d'6tude au Sierra Leone, Facult6 des Sciences Agronomiques, DBp. du GBnie Rural, U.E.R. Hydraulique Agricole, Laboratoire de T616d6tection et d'Agrohydrologie, Gembloux, Belgique, 42 p. Raunet, M., 1982. Les Bas-Fonds en Afrique et d Madagascar. Formation, Caracthres MorphopBdologiques, Hydrologie, Aptitudes Agricoles, IRAT, Service de pbdologie, Montpellier, France. SAS (Statistical Analysis System), 1997. SAS/STAT User's Guide, Release 6.03 Edition, SAS Inst. Inc., Cary, North Carolina, 1028 p. Thenkabail, P.S., 1999. Characterisation of the alternative to slash-andburn benchmark research area representing the Congolese rainforests of Africa using near-real-time SPOT HRV data, The International Journal of Remote Sensing, 20(5):839-877. Thenkabail, P.S., and C. Nolte, 1995a. Mapping and Characterizing Inland Valley Agroecosystems of West and Central Africa: A Methodology Integrating Remote Sensing, Global Positioning System, and Ground-7kuth Data in a Geographic Information Systems Framework, RCMD Monograph No.16, IITA, Ibadan, Nigeria, 62 p. -, 1995b. Regional Characterization of Inland Valley Agroecosystems in Gagnoa, Cote d'lvoire through Integration of Remote Sensing, Global Positioning Systems, and Ground-7hth Data in a

Geographic Information Systems Framework, Inland Valley Characterization Report 2, RCMD, IITA, Ibadan, Nigeria, 38 p. , 1995c. Regional Characterization of Inland Valley Agroecosystems in Sikasso, Mali and Bobo-Dioulasso, Burkina Faso through Integration of Remote Sensing, Global Positioning Systems, and Ground-7kuth Data in a Geographic Information Systems Framework, Inland Valley Characterization Report 3, RCMD, IITA, Ibadan, Nigeria, 46 p. , 1995d. Regional Characterization of Iinland Valley Agroecosystems in Save, Bante, Bassila, and Pararou Regions in South Centml Republic of Benin through lntegmtion of Remote Sensing, Global Positioning Systems, and Ground-7hth Data in a Geographic Information Systems Framework, Inland Valley Characterization Report 1, RCMD, IITA, Ibadan, Nigeria, 46 p. , 1996. Capabilities of Landsat-5 Thematic Mapper (TM) data in regional mapping and characterization of inland valley agroecosystems in West Africa, The International Journal of Remote Sensing, 17(8):1505-1538. , 1999. Regional characterisation of inland valley agroecosystems in West and Central AMca using high-resolution remotely sensed data, GIS Applications for Water Resources and Watershed Management (John G. Lyon, editor), AM Arbor Press, Michigan, (in press). UNEP/GRID, 1993. Global Resource Information Database, United Nations Environment Programme, Nairobi, Kenya. Usery, LE., and A. Phyllis, 1988. Knowledge-based GIs technique applied to geological engineering, Photogrammetric Engineering b Remote Sensing, 54(11):1623-1628. Windmeijer, P.N., and W. Andriesse (editors), 1993. Inland Volleys in West Africa. An Agro-Ecological Characterization of Rice-Growing Environments, International Institute for Land Reclamation and Improvement [ILRI) Publication 52, ILRI, Wageningen, The Netherlands, 160 p.

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