1. Introduction
Cities are considered important areas for economic opportunities and an engine for a country’s development [
1,
2]. Nevertheless, accelerated urbanization can lead to, not only deterioration of the quality of life, but also to environmental degradation especially in developing countries with a few coping strategies [
3,
4,
5]. Cropland conversion, land use competition and wetlands alteration are among the aftermaths linked to rapid urbanization [
6,
7]. In the urbanization hotspots of Sub-Saharan Africa, the Indian sub-continent and Latin America, a population boom, a slums’ development and accelerated land cover conversion in urban fringe zones are key characteristics [
8]. The emergence of high-rise buildings, the redevelopment of inner cities and the expansion of built-up areas in urban fringe zones are continuously taking place, particularly where new enacted planning regulations are being implemented. The rate of urbanization in the abovementioned regions is not at the same pace as the availability of basic infrastructures, and urban expansion has been observed in un-serviced zones, therefore following the “tenure-occupancy-servicing” trajectory. The authorities in charge of urban planning often lack detailed information on spatial patterns of land use development [
9]. The collected statistics are geographically aggregated to large heterogeneous administrative entities, which have been judged as meaningless for informed decision making in urban land management related matters [
10]. Therefore, up-to-date data and effective methods are needed for tangible and detailed urban land cover information extraction for tracing the continuous dynamic change in complex urban environments. Reliable data and information on both trajectories of land cover patterns, and the extension of deprived zones, such as slums, informal settlements and environmentally sensitive zones, are paramount for responding to the pressing urban land administration and management questions. This information is further needed for assessing the environmental impact of urbanization and for forecasting the supply and demand of urban ecosystem services.
One of the methods for near real-time data acquisition and for repetitively tracing the dynamic change of our living planet is the use of remote sensing data. Indeed, satellite remote sensing has been widely used for land cover/land use mapping and change analyses e.g., [
11,
12]. Various studies have found remote sensing as a promising method for analysing urban landscape dynamics and its resulting environmental impacts e.g., [
13,
14,
15,
16,
17], thus contributing to a more sustainable urban development. The objective of this study is to evaluate the effectiveness of combining spectral information with elevation data, a geometric ruleset, object-based and knowledge-based approaches as well as urban density and greenness indices for detailed urban land cover classifications in complex dynamic environments, such as Kigali in Rwanda, which is a fast-growing city of Sub-Saharan Africa. Specifically, this study explored the extent to which the above-mentioned method can help in detecting informal settlements and delineating lowland agriculture located in valleys that have similar spectral reflectance as the adjacent cropland stretched on the neighbouring upland/hillside areas. Twelve land cover classes are proposed in the classification scheme. Specifically, built-up areas were considered in the proposed classification scheme as a super-class composed of three sub-classes (including high- and low-density built-up areas, and informal settlements), given that detecting informal settlements in the urban complex environment requires the consideration of built-up areas as an informal settlements’ nesting class. Likewise, extracting lowland agriculture necessitates the consideration of the various green areas as a super-class and depicting lowland using hierarchical and threshold rule-based strategies.
With the emergence of high-resolution satellite sensors, such as IKONOS, QuickBird, WorldView, Pléiades, these multispectral images have become available worldwide. These systems provide data for detailed land cover/land use analysis at a very fine scale [
18]. High-resolution images were analysed mainly using object-based image analysis (OBIA), which offers the possibility of integrating context-based expertise with spectral information content [
19]. Nevertheless, the images with high spatial resolution are sometimes suffering from either spectral variability within the same land cover class or from the presence of shadows from high-rise buildings and tree crowns [
20,
21,
22]. OBIA requires spectral grouping through a segmentation process. As there are no optimum parameters for generating objects through segmentation, these parameters need to be empirically tested and determined. The testing process can result in under/over-segmentation errors, thus affecting the quality of final land cover classification. A further challenge in urban land cover mapping is the complexity of urban landscapes characterized by a mixture of different land cover classes with overlapping spectral properties. Therefore, integrated methods for urban information extraction from high-resolution satellite data need to be developed to improve the quality of the land cover classification.
Previous studies investigated various methods for improving image classification accuracy including: (i) the use of a knowledge-based and rule-based approach [
19,
23,
24]; (ii) integrating original spectral bands with texture feature measures [
10,
25,
26,
27]; (iii) combining original image bands with transformed bands using principle components analysis (PCA) and/or intensity-hue-saturation (HIS) [
28,
29]; (iv) classifying generated segments using hybrid approaches [
30], and (v) the fusion of multispectral images with synthetic aperture radar (SAR) images [
19,
24]. Other studies proposed either the application of edge detection algorithms [
31,
32] for accurately depicting linear features such as road networks, coupling landscape metrics and transfer learning as a framework for urban dynamics monitoring [
33] or performing multi-scale hierarchical classification in high spectral dimensional feature space [
34,
35]. Machine learning methods have been further applied, specifically for assessing the potential of high-resolution data in producing accurate urban land cover information. Novack et al. [
36] e.g., tested the performance of WV-2 over QuickBird-2 imagery in detecting urban structures in complex urban environments of São Paulo, Brazil. By comparing different supervised learning algorithms in high dimensional feature space, they found that additional spectral bands of WorldView-2 were improving the accuracy of spectrally similar urban objects, especially those lacking geometrical and contextual patterns. Deep learning approaches, such as convolution neural networks (CNNs) [
37] were also found promising in segregating urban objects with high spectral variability. High-resolution sensors with multiple collection sequences over same illuminated scene are also believed to provide data that can improve the classification in complex environments, such as urban areas [
38,
39]. For instance, classified WV-2 data from multiple and sequential collections through a variety of observation angles over Atlanta, Georgia resulted in 13% kappa improvement compared to a single overflight target acquisition [
40].
The use of high-resolution imagery and object-based approaches is well established in various urban applications, such as slum detection [
10,
25,
32,
41,
42], urban poverty analysis [
43], road network and buildings detection [
44,
45,
46,
47,
48,
49,
50], mapping and monitoring urban ecosystem services [
51] to name few. Pu et al. [
52] e.g., tested the performance of object-based and pixel-based methods when applied on high-resolution IKONOS data for a detailed urban land cover classification in Tampa Bay, Florida, USA. Their findings revealed that the objected-based approach outperformed the pixel-based method. Furberg and Ban [
53] assessed the spatial-temporal urban land cover change in Stockholm, Sweden between 1986 and 2006 using an object-based and rule-based strategy with multi-spectral SPOT images and successfully performed change detection by considering seven land cover classes, including high and low-density built-up areas, mixed forest and open land, industrial areas and water. Their study purpose was to investigate the spatial temporal dynamics of the landscape composition and configuration in the study area using landscape metrics. Using QuickBird multispectral data, Ban et al. [
19] developed an object-based and knowledge-based approach for urban land cover classification in Toronto, Canada. They found that, for identifying 16 land-cover classes, the object-based and rule-based approach was effective and an overall classification accuracy of 87.9% (kappa: 0.868) was achieved. In the global south, detailed urban land cover studies were carried out based on high-resolution data and object-based approaches as well. Turlapaty et al. [
50] detected buildings and estimated their height in Rio de Janeiro, Brazil using a hybrid approach, where a template matching algorithm was coupled with a support vector machine (SVM) classifier. They demonstrated that multi-angular high-resolution images, such as WorldView-2 and height information, were worthwhile for detecting urban built-up structures and the 3D visualization of urban forms. Kuffer et al. [
54] combined spectral information extracted from QuickBird and IKONOS imagery with spatial metrics to quantify the morphological differences in planned and unplanned urban areas focusing on New Delhi, India and Dar es Salaam, Tanzania. Based on the homogeneous urban patches derived from segmented high-resolution images, spatial metrics and multi-criteria evaluation, the accuracy of their developed urban settlement index was confirmed with more than 70%. Kohli et al. [
25] developed an objected-oriented and rule-based method for slum detection using QuickBird data over Pune, India. Their method consisted of integrating expert knowledge with a hierarchical classification resulting in slum detection. Kuffer et al. [
10] tested the utility of the gray level co-occurrence matrix (GLCM) variance to classify slums and planned areas using very high-resolution data over Mumbai and Ahmedabad, India and Kigali, Rwanda respectively. Kit and Lüdeke [
32] illustrated the usefulness of combining the Canny and line-segments-detection algorithms for slum detection in the Indian megacity of Hyderabad. Despite the limited spatial coverage, mapping and analysing informal and planned settlements in urban environments using high-resolution data and object-based approaches has been used in different areas of Sub-Saharan Africa, such as Voi Township, Kenya [
55], Kibera ward in Nairobi, Kenya [
56], Kisumu in Western Kenya [
57], Cape Town, South Africa [
58] to name few. The satellite-based detection of deprived urban areas such as slums and informal settlements, and environmentally sensitive areas, such as the wetlands, is still a challenging topic to explore especially in rapidly urbanizing hotspots of global south nations. Meanwhile, the production of geospatial data and information about urban spatial patterns and growth in those nations are urgently needed for monitoring the implementation of Goal 11 of the Sustainable Development Goals (SGDs) which advocates making cities inclusive, safe, resilient and sustainable [
59].
This research aims to evaluate the combination of the object-based SVM classification of high-resolution satellite imagery, the derived rules of geometric features, and the urban density and greenness density indices for improving land cover classifications in complex urban environments. The proposed framework can be a method to rapidly produce land cover maps when land cover class separability is still problematic. The application of the above-mentioned method can also speed up the production of detailed urban land cover information, which is highly needed in rapidly urbanizing cities of the global south, particularly in Sub-Saharan Africa. Despite previous research efforts, very few studies aiming at producing high quality urban land cover maps, including slum areas extraction in Sub-Saharan Africa, have been produced. In particular, scant studies based on near real-time information extraction using high-resolution satellite data exist on Kigali, Rwanda. To the best of the author’s knowledge, there are only generalized studies on slum detection by Kuffer et al. [
10] and the building footprints extraction based on Pléiades multispectral bands and elevation information by [
60]. Therefore, the evaluation on the use of very high-resolution images for detailed urban land cover mapping in Kigali, Rwanda is considered as an added value to the existing methods for urban information production in Africa. This paper is structured as follows.
Section 1 introduces the background and rationale for the research and the objectives.
Section 2 presents the study area and data used in this research. The methodology is described in
Section 3, followed by the presentation of the results and discussion respectively in
Section 4 and
Section 5.
Section 6 provides the main study outlook and draws conclusions.
2. Study Area and Data Description
Kigali is Rwanda’s capital and largest city with an estimated 730 km
2 metropolitan area and a population of more than 1 million [
61]. The area shown in the WorldView-2 image below covers the city’s central and eastern parts (see
Figure 1). As a fast-growing and rapidly changing city, there is an increasing demand for land in peri-urban areas for housing and emerging secondary and tertiary activities. Parcel subdivisions, expropriation and peri-urban land-use conversion are continuously taking place. The demolition of existing buildings, the construction of high-rise buildings in the inner city, the densification and renewal of road networks, and the expansion of built-up areas are among the most prominent urban developments. Therefore, tracing the ongoing urbanization (re)development is paramount for supporting land management and sustainable urban planning. Since the post-genocide (Genocide against Tutsi happened in Rwanda in 1994 and at least one million people lost their lives) era, the population growth was high paced in Kigali. In two consecutive censuses period, Kigali’s city population quadrupled from an estimated 200,000 in 2002 to 1.135 million in 2012 [
61], and the impervious surface has been sprawling ever since.
A cloud free high-resolution WorldView-2 (WV-2) image acquired on 17 May 2016 was used in this study. The WV-2 satellite is a high spatial resolution space-borne sensor launched in 2009 with eight multispectral bands ranging from blue to the near infrared parts of the electromagnetic spectrum and one panchromatic band (450–800 nm) [
62]. In addition to blue (450–510 nm), green (510–580 nm), red (630–690 nm), and near-infrared-1 (770–895 nm) bands, WV-2 has the additional coastal blue (400–450 nm), yellow (585–625 nm), red-edge (705–745 nm), near-infrared-2 (860–1040 nm) bands. The imagery was first orthorectified using the satellite orbital model and a digital terrain model (DTM) at 10 m resolution, then projected in the Universal Transverse Mercator-36 South Zone with World Geodetic System 1984. The DTM was produced by the Department of Lands and Mapping of Rwanda Natural Resource Authority (RNRA) using stereo-restitution based on 25 cm spatial resolution ortho-rectified aerial photos. Before its use, the DTM was reprojected from a customized local projection system, Transverse Mercator 2005 to the same projection as the WV-2 imagery. The DTM was mainly used to derive the elevation and slope information.
2.1. Conceptualizing Land Cover Classification Scheme
Based on the information needs to support sustainable land management and urban planning, twelve land cover classes were proposed in the classification scheme including the high-density built-up area (HDB) and low-density built-up area (LDB), informal settlements (IS), paved road (PR) and unpaved roads (UPR), urban green space (UGS), upland and lowland agricultures, forest, bare land, wetland and water as illustrated in
Table 1. The designation of proposed land cover classes was preceded by conceptualizing the hierarchical land cover scheme that facilitated the implementation of the multi-stage land cover classification workflow. The built-up area was conceptually considered as a super-class, which can be later split into sub-classes (i.e., high- and low-density built-up areas).
The informal settlements were captured in the schema as a sub-class nested in high-density built-up areas. Besides, agriculture was proposed as a super-class, i.e., at an aggregate level, and was subsequently subdivided into two sub-classes including the upland and lowland agricultures. Other remaining land cover classes such as paved and unpaved roads, forests, urban green spaces, bare land, wetland, and water were considered at the higher level of the proposed hierarchy and consequently refined using spectral and geometric based rules.
2.2. Training and Validation Data Collection
One of the key aspects of the supervised methodology was the collection of the training samples. In this study, the training samples were collected at the segment level (Level 1, see step 1 in
Figure 2) and the proportion of the area occupied by each land cover class (see
Table 2) was considered to determine the number of the objects representing each class. The training data was collected at the segment level as all pixels in each segment belong to one land cover class. The number of training objects for each class ranged from 12 to 181 and the total was 438 (see
Table 2). Regarding the validation data, individual points were randomly selected across the study area for each class. To guarantee the independence of the validation data from the training data, the validation points that were inside the training objects were excluded. To ensure the quality of the validation samples, each of the 2925 validation points were manually labeled by cross-checking the high-resolution QuickBird images on Google Earth with WV-2 imagery. The number of validation points for each land cover class is reported in
Table 2. As the lowland and highland agriculture class was extracted using a rule-based strategy, the training objects were selected for the combined agriculture class. However, the validation samples for the highland and lowland agriculture classes equivalent to 208 and 91 respectively, were separately collected.
5. Discussion
In this research, a high-resolution urban land cover map was produced with 12 classes using a multi-level and customized classification strategy based on WV-2 data. As inferred in previous studies e.g., [
69,
70] and our own experience during the proposed processing chain, the extraction of a detailed urban land cover map based on high-resolution data, such as WV-2, in complex urban environments using conventional object-based classification is prone to inaccuracies due to spectral variability in the same land cover class and spectral similarities among several land cover classes. Despite its worthwhile importance, the use of object-based and rule-based strategies for classification refinement is not sufficient to detect informal settlements.
Figure 10 shows the spectral signatures of the 12 land cover classes using the 2925 validation points across the eight multispectral bands of WV-2 imagery. It was revealed that that some land cover classes were spectrally overlapping in all bands. This is the case for the lowland and upland agriculture. Overlapping between classes was also observed in bare land and unpaved roads. Similarly, the built-up area classes consisted of HDB, LDB and IS which were spectrally confusing with one another in all bands with a slight separation in the near infrared bands. Therefore, merely training an advanced classifier, such as SVM, is not enough to delineate land cover classes with good accuracy in complex urban environments.
The effectiveness of using high resolution data for mapping an urban landscape at fine resolution needs to rely on integrated methods to take into account the spectral information content, geometric properties of the urban structure, the rule-based approach and spatial variability of urban and greenness density. The application of the above-mentioned method can lead to the production of a highly accurate urban land cover map, but its implementation needs to follow a step-by-step methodology, such as the hierarchical classification workflow as illustrated in this study. The proposed approach involves a number of multistage classification and rule-based classification strategies. At the high level of the hierarchy, the one-pass object-based SVM classification takes into account the super-classes that are disaggregated into sub-classes and refined at a low level to capture the within class spectral and spatial diversity (see
Figure 4). For instance, the built-up areas were first considered as a super-class that was divided into three sub-classes (i.e., HDB, LDB and IS) using a rule-based approach, the urban and greenness density indices, and the GLCM texture measures. The delineation of lowland agriculture stretched in the valleys in the study area was judged important because their management plan is different from one of the neighbouring upland agriculture. Indeed, while planning the use of land located in valleys, attention to their sensitivity to degradation and their respective ecological functions need to be taken into account. Therefore, it was found worthy to split the agriculture class into two sub-classes, i.e., lowland and upland agriculture. Spectrally, the lowland and upland agriculture classes are identical. Their separation was possible after the extraction of lowland based on topographic and slope data combined with post-processing operations and using a thematic object feature overlap. Previous studies combined high resolution multispectral data with digital surface models (DSM) to establish the distinction between the upland and lowland, such as mangrove forests or wetlands e.g., [
71,
72]. In the present study, the thematic layer valleys were found useful in separating the lowland agriculture class from the neighbouring upland agricultural zones.
The study pointed out that the synergy between a robust classifier, such as SVM, and the integration of a geometric rule-set and the proposed density indices (UDI and GDI), is a reliable method to improve the urban land cover classification in complex urban environments. The findings in the present study concur with previous studies e.g., [
25,
73,
74,
75], where the rule-based approach using geometric features, texture measurements and the spectral band threshold were found useful for land cover classification enhancement. Some of the features to ingest in the feature space include the bands’ mean and standard deviation, and in particular the geometric features related to the object’s extent and shape, such as compactness, asymmetry and rectangular fit, area, width and length. The GLCM-texture features as proposed by [
76] are contributing to the production of optimal segments and to class separability, while training an advanced classifier [
75]. The improved classification results align with the claims in the previous studies where several authors e.g., [
10,
22,
69] have been emphasizing the importance of texture features and geometric related features in improving the land cover classification in complex land cover environments. Particularly, the shape and extent of the object’s features (especially area, length, width and rectangular fit) were found valuable for refining misclassified buildings in planned areas with LDB. The length and width were identified suitable for delineating the unpaved road and bare land which usually have similar spectral properties.
Furthermore, the multistage object-based classification was found to be a worthwhile framework for the informal settlements extraction from high-resolution imagery. Normally, informal settlements are developed in high-density built-up areas in unsuitable construction sites [
66,
67]. It was illustrated that the proposed urban density and greenness indices contributed to define the spatial patterns of urban morphology, such as slums and informal settlements. The results demonstrate that the proposed method is able to enhance both the land cover classification accuracy and computation performances while extracting informal settlements considering the HDB as a super-class in which informal settlements are nested as a sub-class. The relatively high producer and user’s accuracies (77% and 90.2% respectively) in the informal settlements detection are promising and our results are in accordance with recent findings in slum detection research based on high resolution multispectral data by [
25]. In that study, slums were detected with 60% agreement after training only three land cover classes, namely slums, non-slums and others. The methodology for slums and the informal settlements detection can be enhanced by incorporating additional features to the spectral information, such as the bands’ statistics, image texture features and geometric objects, which are added in the feature space. Meanwhile, the conceptualization of informal settlements can take advantage of the information derived from high-resolution imagery using the integrated method, but the full coverage of informal settlements ontology can go beyond image classification. Indeed, the image classification and visual interpretation are considering the physical entities, whilst the definition of an informal settlement is embracing other aspects beyond physical morphology, such as legal aspects of land tenure and deprived living conditions and sub-standards [
77,
78].
As rule-based classifications remain consistent to a certain degree when applied to other areas [
75,
79], the proposed classification framework can be easily transferred and successfully tested in different study areas. The practical challenges in applying the proposed framework are mainly related to finding the optimum values for thresholding and to customize methods for extracting particular land cover classes, which are not standardized across different testing areas. Previous studies illustrated that the accuracy of the land cover classification is highly dependent on the type of landscape composition [
80] and on the training and testing set characteristics [
81,
82]. Therefore, the cut-off values used in the present study for thresholding should be adapted, not only to the local context, but also to the variables taken into account during the classification processing chain.