Nov 14, 1984 - ABSTRACT: Detection of land use and cover changes using remote sensing images is a challenging task in semi-arid savannah landscapes of ...
Detection of human-induced land cover changes in a savannah landscape in Ghana: I. Change detection and quantification Vescovi, F.D. Center for Development Research (ZEF), Walter-Flex-Str. 3, 53175, Bonn, Germany
Park, S.J. Center for Development Research (ZEF), Walter-Flex-Str. 3, 53175, Bonn, Germany
Vlek, Paul L. G. Center for Development Research (ZEF), Walter-Flex-Str. 3, 53175, Bonn, Germany
Keywords: land use and cover changes, vector analysis, change quantification. ABSTRACT: Detection of land use and cover changes using remote sensing images is a challenging task in semi-arid savannah landscapes of West Africa, due to the complex mixture of natural and cultural landscape elements and the strong seasonal variation of the vegetation. A new statistical procedure was developed to detect human-induced land use changes in savannah landscapes in Northern Ghana using multitemporal NDVIs derived from LANDSAT -TM and -ETM+ images. This procedure consists of four main steps: 1) normalization and realignment of NDVIs to reduce the seasonal variation of vegetation and instrumental error; 2) principal component analysis to extract significant change vectors; 3) vector analysis to determine the type and intensity of human-induced land use changes; 4) visualization of the resulting “change map” and comparisons of different case studies. The output resulting from the application of the PCA and the vector analysis methods provide information on the change types and intensities. 1
INTRODUCTION
The impacts of Land Use and land Cover Changes (LUCC) on the sustainability of the ecosystems are becoming increasingly important issues in global changes research. Human actions seem to lead to the greatest changes in the current state of the earth's surface. Alterations in the surface cover result in changes to the balance of energy, water, and the geochemical fluxes at the regional, local and global level. These changes will inevitably influence the sustainability of natural resources and socio-economic activities. In order to investigate the LUCC processes over a certain time period, satellite imagery methods are suitable to study changes in inhospitable and inaccessible terrain avoiding the high costs for field surveying. They are particularly valuable tools for African environments, in which historical land use change records are either not available or out-dated. In a multitemporal remote sensed imagery of a typical African landscape, two kinds of land changes can be distinguished: 1) short term changes due to the seasonal effect, and 2) long term or permanent changes due to human actions like constructions of new infrastructures, deforestation, and farming activities. The first change is simply due to the effect of the seasonal climate on the vegetation. The second source of variation further includes two different kinds of change: conversion from one land cover category to another (e.g. from forest to grassland) and modification within one category (e.g. from closed forest to open forest). Conversion is reasonably well documented in land cover change studies; modification, however, is less well studied and often ignored at a regional scale. In a typical savannah landscape, settlements are widely scattered and individual agricultural fields are small and frequently mixed with other natural and secondary vegetation. Furthermore, seasonal phenological vegetation changes are too pronounced to confidently separate agricultural lands from natural vegetation using remote sensed images only. In an accompanying paper (Park et al., 2002), a statistical approach was developed to detect humaninduced land cover changes at the study area. This method provides a spatially continuous, quantitative index for the ‘relative’ human-induced changes to investigate the spatial heterogeneity of land use change processes, but it is not able to identify types and/or direction of changes. The present paper proposes a new method applied on the same dataset to detect human-induced land changes to identify and quantify the type of human-induced changes in savannah landscapes.
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STUDY AREA
The study area, about 100 x 100 km in size, is located in the Northern Region in Ghana. It is a typical Guinea savannah-agricultural pattern which, has undergone deep social and environmental transformations in the last years. These are due to population growth, immigration, increase in farming activities and, not to be forgotten, a civil war which broke out in the early nineties. Tamale, the third biggest town in Ghana, is located in the centre of the selected area. The climate of the region is the tropical continental type characterised by a single rainy season (May to October), a prolonged dry season (November to April), high temperatures and very high humidity during the rainy season. The mean annual rainfall ranges from 1000 to 1300 mm. The multitemporal analysis here reported reflects the history of the social transformations that have taken place in the last years and of the activities of the local people leading to land use changes. The area (see in Fig. 1 an example of change) underwent intensive land use changes in only a few years, and the farming activities have increased. One of the causes for this change can be found in the rapid population increment which led to an intensification of the farming activities and an extension of the cultivated surface. However, the population growth in the region, like in many other African environments, can not be considered the sole and major cause of LUCC (Lambin et al., 2001).
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Fig. 1 Increase of bush farms in an area in the neighbourhood of Fufulsu, North Region, Ghana. In the picture on the left, the yellow circles display farm activities in 1984. The yellow circles in the picture on the right indicate the most evident changes recorded 15 years later. The river flowing in the centre is the White Volta. In the 80s, it was not possible for the local people to settle close to the river because of the risk of onchocerciasis (river blindness), which is nowadays substantially under control thanks to the Onchocerciasis Control Programme in West Africa (OCP) (http://www.who.int/ocp/apoc/apoc001.htm). Thereafter there is significant agricultural encroach to riparian zones.
3
METHODS
3.1 Data set The multitemporal data set considered in this study consisted of two LANDSAT-TM acquisitions from 14 November 1984 and January 1991, and two LANDSAT-ETM+ acquisitions from 7 November 1999 and 14 March 2000. The two images from November display the area at the end of the rainy season, when the vegetation is still well developed and NDVIs are high. The two images from January and March were acquired in the dry season in which the vegetation was wilted. The study site lies across two LANDSAT scenes, whose path/row is 194/53 and 194/54. Two scenes were mosaiked for the four years, and a subset, about 100 x 100 km in size, was cut from the four mosaics. 3.2 Geometric corrections A field campaign was carried out for ground truthing and a number of ground control points was collected using a GPS. The georectification was performed according to the Universal Transverse Mercator projection (UTM 30) on one of the mosaics of the image of the year 2000. This was considered the master image and all other slave images were image-to-image coregistered to the master image. Particular attention was paid to the coregistration in order to minimise the geometric errors. Since the following multitemporal analysis is performed on a pixel-by-pixel basis, any misregistration would provide erroneous results. The average coregistration error was below 1.5 pixel for the whole mosaic.
3.3 Atmospheric corrections The comparison between scenes acquired in different time periods has necessarily to deal with the weather conditions of the acquisition day. Atmospheric correction protocols are currently suitable for some limited atmospheric disturbances like haze or smoke which were removed from all four subsets to the maximum extent possible, whereas thick clouds and their shadows had to be held as tolerable unknown areas. The simple regression method was applied on the subsets of the four years. This method is well known in literature (Chavez, 1988) and is applied when direct climatic measurements for the acquisition day are not available. An area of low reflectance in the image is chosen (water, deep shadows or thick forest); next, the pixel values of the near infrared band (4th band in LANDSAT) are plotted against the values of the bands to be corrected (3rd band for calculation of NDVI) and a linear regression is calculated. The offset in the 3rd band-axes represents an estimation of the atmospheric path radiance and these minimum values are subtracted from the respective image band. 3.4 NDVI calculation, normalisation and realignment The Normalised Difference Vegetation Index (NDVI=(infrared-red)/(infrared+red)) was calculated for the four scenes. Figure 2a shows the distribution of NDVIs. As expected, the season effect plays the major role in the difference of the NDVI values among scenes. The two rainy season data sets from 1984 and 1999 have positive NDVIs, whereas the dry seasons 1991 and 2000 feature negative values. In addition, there might be systematic differences in NDVIs, possibly caused by sensor conditions, variation in sun illumination angle, and sensor observation angle. In order to remove all those radiometric differences and make the distributions of the data comparable, the NDVI values for the four images were recalculated after a normalisation and alignment process. The minimum and maximum values of individual NDVIs were rescaled between –1 and 1, and the mean value was centred at 0. This process substantially normalises the variations between different scenes, but not within scenes. Similar methods have previously been applied to normalise other spectral signals by Hall et al. (1991) and Chavez (1989). The normalisation and realignment were performed by the following equation:
Iyp = 2/?? max-min?* DNyp - Oy where: Iyp is the new vegetation index for the year y in the pixel p. ?? max-min? is the absolute difference between the maximum and minimum value of the original NDVI data, which must be normalised between –1 and +1. DNyp is the digital number of the original NDVI value for the year y in the pixel p. Oy is the calculated offset to bring the average of the data distribution for the year y to 0. The calculated equations for individual NDVIs were as follows: for 1984: Iyp = 3,3898 DNyp –0,6463 for 1991: Iyp = 7,6923 DNyp +0,8699 for 1999: Iyp = 2,7778 DNyp –0,7743 for 2000: Iyp = 9,0909 DNyp +2,3184 In Fig. 2b the normalised NDVIs after the application of these equations to the original NDVIs is plotted. We expect the normalisation and alignment of the NDVIs for the four years will reduce the majority of the vegetation variation that is caused by seasonal effects and systemic instrumental errors. Therefore, it should be possible to compare scenes to detect human-induced changes from the corrected images.
a: Original data
b: Normalized and aligned data Fig. 2 a) Original frequency distributions of the NDVIs for the four images considered in this research. b) NDVIs distributions after normalization and alignment. Seasonal and systemic factor differences are artificially levelled out.
3.5 Principal Component Analysis (PCA) The detection of land cover changes with LANDSAT data using PCA was first reported by Byrne et al. (1980). The calculated multitemporal NDVI dataset can be considered a multidimensional dataset in which most of the data, particularly the “no change” areas, are correlated and therefore lying on the diagonal of the scatterplot created in the multidimensional space. The pixels lying off the diagonal should show the NDVI change that occurred over time between the acquisitions. Due to the decorrelation nature of the PCA process, the second-order PCs are expected to enhance those regions of localised change. Even though this process was applied to the normalized and aligned data, the use of PCA could be performed without carrying out the calibrations required for the usual image pre-processing (Jiaju, 1988). One of the limitations of the PCA procedure is that it cannot be determined beforehand which of the PCs best display the change and identify the direction in which the change occurred. However, a good knowledge of the history of the land management in the study area can help to identify on the output PCA image the main changes and separate the various change types depending on the specific colour displayed in that RGB composition. An RGB composition particularly suitable to display the changes should be composed of the first-order PC and two of the last-order PCs in order to enhance the maximum contrast between “no change” and “change” class, respectively. 3.6 Vector analysis The vector analysis applied to the change studies was originally developed for application on the NOAAAVHRR sensor and the MODIS sensor (Lambin et al., 1994; Lambin at al., 1997; Borak et al., 2000). Since such instruments, although featuring a coarse spatial resolution, offer the advantage of frequent coverage of the study area, this facilitates the monitoring surface processes over time. In our study, an attempt was made
to apply the same technical principle to the above-mentioned LANDSAT dataset, which has a higher spatial resolution but consists only of four acquisitions. In the multitemporal image processing context, a vector can be defined as the time-trajectory of a certain pixel value (e.g. the reflectance or a biophysical indicator like NDVI or tasselled-cap) in the multitemporal dataset (Lunetta et al., 1998; Lambin et al., 1994). The magnitude of the vector is proportional to the accumulated values along the time period considered. In the case of the NDVI multitemporal vector, this parameter can be referred as the integrated vegetation index for that land cover in the time marks of the dataset, which, in turn, can be related to the total dry matter accumulation and productivity (Tucker at al., 1983; Tucker et al., 1986) and the actual evapotranspiration for that cover type (Chilar et al., 1991). The direction of the vector depends on the relative NDVI magnitude for each time and is measured by the angles of the same vector on each axis. This parameter can be interpreted as a synthetic quantifier of the total variation of the NDVIs over the considered time period. Since it is assumed that the NDVI variation sources (atmosphere, season effect, sensor sensitivity, illumination, etc.) had already been corrected or levelled out by the previous data preprocessing, the angle of the vector in respect of that axes could be accounted as an index of the occurred human-induced change in that year. Fig. 3 presents a simulated example of human-induced land use change, using vector analysis. In this case, the data are assumed normalised and aligned, and hence independent of the seasonal and instrument variations. Since the cover types are here supposed in a “neutral” time, the four images have lost their seasonal identity and are here reported as numeric points in time (time points t1, …t4).
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Fig. 3 a) Example of derived vectors for two simulated cases here supposed independent of the season effect: human-induced change (e.g. deforestation) and any savannah vegetation cover. b) Calculated angles of the two multitemporal vectors on the second time point. For graphical purposes this plot only represents the first three temporal dimensions.
During the image processing for the vector analysis two parameters had to be calculated: 1. A multitemporal vector P (p,y) for pixel p for the year y was defined (Lambin et al., 1994): I (y1) I (y2) I (y3) I (y4)
P (p, y) =
where I are the NDVI values, for pixel p in the four years. In practice the resulting vector P image was calculated as the Euclidean distance between the pixel in the multidimensional space and the axes origin. P(p, y) =
v
y4
Sy (Ipy+1)2 1
where Ipy are the NDVI values, for pixel p in the year y. The length of the vector | P | measures the accumulated NDVIs over the considered years. 2. The angles of P on each axes of the multitemporal dataset is calculated as the ratio between the NDVIs for each year and P, or, in other words, the sines of the angles of P in the multidimensional space: sin P(p, y) = Ipy / P(p, y) To check the correctness of the angle calculation a control test was performed: y4
Sy sin2 P(p, y) = 1 1
The result must be 1 according to the Pythagorean theorem and the trigonometric definition of sine and cosine: cos2 (t) + sin2 (t) = 1 4
RESULTS
The main output of this approach is a method that not only identifies the LUCCs but also quantifies the environmental and ecological impact of anthropogenic activities which modify the use and/or the cover of the land. The combination of artificial bands like PCs and the calculated vector angle synthesizes different biophysical vegetation descriptors derived from remote sensed data over the considered time period. In fact, the NDVI variations over time are assumed to be more or less correlated with the variations in a number of vegetation parameters (LAI, biomass, status of the vegetation, etc.). Therefore, the output bands resulting from the application of the PCA and the vector angle analysis methods provide, at a glance, the synthesis of a number of information on the change types and intensities throughout the whole study area. A different approach was followed by Park et al. (2002) on the same area and with the same temporal dataset. The multiscale hierarchical adaptive model developed in that study to characterise dominant land cover change mechanisms for various spatial scales resulted in an overall map which was substantially consistent with the here presented results. In both maps, the most evident spots showing extreme changes were substantially identical, confirming reciprocally the goodness of the two approaches. The simple RGB composition of artificial bands like PCs, multitemporal vectors or angles resulted in very unnatural colours that are hard to interpret. Several attempts were made to display them in the most effective way. One of the most suitable methods to enhance both type and impact of the changes was the 3D-plotting of the calculated vector angle over an RGB composition. The three bands composing the RGB composition consisted of one of the last PCs, the multitemporal vector and the first PC, respectively. This composition enhances the contrast between change and no-change, which gives information on the type of LUCC in a specific colour. (e.g., from savannah to agriculture, from forest to clearing, etc.), whereas the calculated vector angle, which was referred to the NDVI of the year for which the change had to be enhanced, displays the impact or intensity of the change in the vertical dimension. Some examples of land use changes are shown in the Figs. 4 and following. The no-change areas (permanent water, towns) were given by very low angle values, which resulted in a low-level surface or a depression. The colour of such areas is usually dark. In contrast, the dramatically changed areas (deforestation, new settlements, irrigation systems and intensification of farming) were detected by a rise in the plotted surface, and the colour of that elevation corresponds to the type of change. In Fig. 4a and 4b, the Daboya dam is represented in two different acquisitions 15 years apart. The difference in size of the water surface is only due to the exceptionally dry rainy season in 1984. Despite this, the savannah vegetation looks relatively greenish in November and wilted in March. The very evident green spot in the March acquisition is the new irrigation system; its effects are remarkably evident in the dry season. In Fig. 4c, the resulting change analysis is displayed in the 3D plot in which the image is an RGB composed of R: PC4, G: multitemporal vector and B: PC1, while the vertical dimension is given by the sine vector angle (calculated as the ratio between the NDVI from 2000 and the multitemporal vector). The colours represent the change types: green indicates intense farming, and magenta means no change. The surface height indicates the change impact or change intensity here expressed as sine of angle. For instance, between the drastic change like the irrigation system in Fig. 4b and the absolutely no change, as in the case of the water surface, the calculated sine difference is 0.80-0.27=0.53. Note that the region around the dam, despite the extreme seasonal change, remains relatively flat, meaning that the method discriminates between seasonal change and human-induced change.
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Fig. 4 a) and b) Construction of an irrigation system in Daboya. During the dry season the effect of the irrigation on the landscape is very evident. Fig. 4 c) Measurement of the difference between the no-change (water) and the maximum change (irrigation system).
Fig. 5 a) and b) – Deforestation and farming activities in Wuripe village. Fig. 5 c) The impact quantification of this changeis represented in the 3D plot. The height of the “hill” in the centre quantifies the change impact.
Another example of heavy impact is shown in Fig. 5. The displayed area shows Wuripe village, in which over 2000 ha forest have been cleared and farmed. In 1984 (Fig. 5 a)) the spot consisted of a closed woodland with only a few huts. As a result of immigration, Wuripe village grew up extremely fast in the following 10 – 12 years. After clearing the forest the farmers started cultivating corn, millet, yam in association with cassava, and cow-pea in small fields (bright spot in the image Fig. 5 b)). The change analysis in Fig 5 c) enhances the change type and quantifies the impact. The 3D plot is an RGB composed of R: PC3, G: multitemporal vector and B: PC1. The vertical dimension is given by the sine vector angle calculated as the ratio between the NDVI from 1984 and the multitemporal vector. The red colour indicates deforestation, while the yellowish stripes display the thick woodland, which is extremely closed along the streams. Being calculated on the NDVI from 1984, the deforestation change results in a red “hill” in the centre of the plot, which can be quantified in sine units. In Fig. 6, an example of no change as confirmation of the goodness of the method is presented. An urban area is considered: Tamale town acquired in the same season 15 years apart. In this case, there is hardly any change and in fact the two images 6 a) and 6 b) are very similar. Some few variations are only detectable in the green park in the centre of the town. The chosen RGB composition is here R: PC2, G: multitemporal vector, B: PC1, where the moderate vegetation changes are displayed in green and the no-change urban area in purple. As expected, the 3D plot of the change analysis resulted in a low level surface in which the maximum hardly reaches 0.4 units of sine of vector angle. The resulting surface in the built-up area has a vector angle over NDVI data from 1999 close to 0 since no change occurred in the town over the analysed time sequence. The savannah-like vegetation displays a homogeneous surface above it, showing only a slight natural change during the 15-year period. Note that the maximum value here is similar to the minimum value of the woodland change displayed in Fig. 5 c).
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Figg. 6 a) and b) Example of no change: Tamale town. Fig. 6 c) The resulting surface is shallow and the urban area in the middle displays a sine of vector angle close to 0.
5 CONCLUSIONS The application of the vector analysis on the multitemporal remote sensed data is relatively new in the scientific literature of remote sensing, as only few satellites have been operating over a long period of time. The technique is providing several interesting breakthroughs in the complex issue of land use change. The coupling of PCA and vector analysis applied on a multitemporal NDVI dataset of the four LANDSAT images has proven to be effective in detecting, recognizing and quantifying the main changes during the 15-year time period in the study area. However, the interpretation of the results, particularly of the PCA outputs, is scene-dependent and cannot be easily generalized. This approach, as well as the multiscale hierarchical adaptive model approach proposed for the same dataset (Park et al. 2002), is an attempt to distinguish between the natural changes (e.g. the vegetation growth or the climate effects) and the human-induced changes (e.g. deforestation, urbanization, constructions of new dams, or farming activities). The last ones are increasingly modifying surface covers in West Africa. The identification and localization of the main human-induced driving forces leading to land use changes in Africa open perspectives to the social and ecological sciences for which remote sensing could in future be a substantial key element to link the local knowledge on the land use and cover change to the issue of the global climate change.
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