Characteristics of Landsat TM and Landsat ETM+ sensors.
Open access peer-reviewed article
This Article is part of the Special Issue SUSTAINABLE CITIES IN PRACTICE led by Professor Amjad Almusaed from Jönköping University, Sweden
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Article Type: Research Paper
Date of acceptance: June 2023
Date of publication: October 2023
DoI: 10.5772/geet.17
copyright: ©2023 The Author(s), Licensee IntechOpen, License: CC BY 4.0
Study objective and design A change vector analysis (CVA) was used to determine land cover (LC) changes and identify tree species that are best for urban greening based on carbon sequestration and air pollution. The study assessed LC change in Kitwe, Zambia, from 1990 to 2015. This study identified the most planted urban tree species along Kitwe’s main roads and highways and evaluated typical urban tree species’ pH, RWC, total chlorophyll, ascorbic acid, and biomass. Place and length of study The urban trees in Kitwe, Zambia, make up the study population. The city of Kitwe is a thriving centre for mining and commercial activities and is situated in Zambia’s Copperbelt Province. The investigation took place during 2018 and 2019. Methodology The NDVI and BSI indices were created using spectral indices created from Landsat images of Kitwe taken in 1990 and 2015, respectively. The size and direction of the LC were then determined using CVA, and a district database of land cover changes was constructed using GIS. Urban trees from the built-up area were utilised to create an inventory of common urban tree species based on the land cover classification. The anticipated performance index (API), which measures the suitability of tree species for improving air quality, and the air pollution tolerance index (APTI), which measures the suitability of tree species for urban greening, are two of the three assessment methods that were employed. In addition, above-ground biomass (AGB) was employed to quantify the carbon sequestration contribution of the current urban forest. Results The study discovered that between 1990 and 2015, mining activity and urban growth in Kitwe both contributed to changes in the area’s LC. While the central business district still exhibits a persistent presence as a result of the town’s age, having sprung up before the 1990s with more expansions in the new areas, areas being monitored showed low and medium change intensity, mostly in the northeast of the district. In the current investigation, there was a significant difference in the relative abundance of species (p = 0.05). In the study site, Mangifera indica (RA = 12.3%) and Delonix regia (RA = 15.9%) were the two most prevalent species. According to the study, eleven species were found, and each has accumulated carbon in a unique way throughout time depending on its allometry and age. These distinctions in physiological response (tolerance) to air pollution are noteworthy. Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia were all identified as suitable tree species. Conclusion Over the past 25 years, more than 50% of the land cover has changed, with the majority of that change occurring in regions that are now classified as built-up areas. The majority of Kitwe’s urban forests are found in the populated areas and are made up of a variety of ornamental trees that are frequently cultivated for their aesthetic value, attractiveness, and shade. According to the research, this mixture also includes opportunistic urban trees (invasive species) and fruit-bearing trees intermingled with native species. Overall, this study suggests the following species: For urban trees suited for greening programmes aimed at improving air quality and providing shade and beauty in green areas, residences, and sidewalks that have a low air pollution environment, consider Bauhinia variegata, Toona ciliate, Gmelina arborea, Eucalyptus grandis, and Delonix regia.
land use
change vector analysis
remote sensing
urban forest management
species list
urban planning
smart cities
air pollution
Author information
By 2050, cities will be home to 66% of the world’s population due to the rapid growth of the population [1]. The future of both people and the earth lies in cities. Urban ecosystems are in danger due to population growth and rural-to-urban migration [2]. According to numerous studies [3–7], many tree species are endemic, invasive, or cohabit in urban settings. They make it possible for human cultures to advance socially, economically, and culturally. Most cities are densely populated and frequently have a negative impact on the environment. Increased demand for social services and resources results in resource depletion and a greater carbon footprint.
Pollutants are produced between the urban complex and the natural environment, according to the cyclic nature portrayal and material balance theory [8, 9]. Environmental stress can have an impact on traffic, noise, and air quality [10, 11]. By reducing water recharge and percolation zones, more concrete leads to urban heat islands, heat waves, and a changing global climate [12]. Both mobile and fixed sources of urban air pollution raise global air temperature and carbon dioxide concentration [CO2] [13]. Environmental pressures are the root cause of climate change. Due to extensive use of fossil fuels for manufacturing, transportation, heating, and other industrial activities, cities are the main source of anthropogenic CO2 emissions [14, 15]. 90% of the 4.2 million people who died in 2016 from breathing polluted air were city dwellers, according to UN figures [1]. The metropolitan population of the world, which makes up more than 50%, is highly exposed. Evaluations of pollution and the connections between anthropogenic and forest ecosystems focus on urban areas.
Land degradation reduces long-term ecological function [16]. Examples of degradation include formation of unproducive monocultures by invasive species, compaction, erosion, salinization, and desertification. The degradation of the soil and vegetation has an impact on productivity [16, 17]. Concerns regarding the rising need for food, animal feed, and fuel are raised on a worldwide scale as a result. We have been able to pinpoint the locations and mechanisms of degraded lands all across the world thanks to remote sensing data [16]. These methods should map essential aspects that will help urban centres achieve a sustainable future and monitor dynamics that allow historical trends and scenario prediction [18].
Spatial data enables the use of vegetation cover indices to measure land degradation in semi-arid environments, which is difficult due to vegetation growth and changes [5, 19]. Remote sensing relies on channel sensitivity to radiation within narrow wavelength bands because detectors record EMR in numerous bands. By using visible bands 1, 2, and 3, one may locate roads. Bands 4, 5, and 7 of the reflective infrared spectrum can distinguish between land and water. Thermal imaging employs band 6. Using multispectral bands, Jones and Vaughan [20] provide local and regional mapping of vegetation types and conditions. According to Young
Tracking the effects of human activity on the environment can be done through land degradation and cover changes. Applying the outcomes and results involves researching this influence in order to reduce or control the change. Using the red and infrared spectral bands of remote sensing, vegetation indices can be made to distinguish between places with more vegetation than bare soil. It is difficult to evaluate land degradation using vegetation cover indices in semi-arid environments due to vegetation growth and alterations [5]. Remote sensing relies on channel sensitivity to radiation within a specific wavelength range because detectors record EMR at various wavelengths. Yuan
A system’s adaptive capacity is defined as its potential to respond to recent or impending climatic change [28, 29]. Urban forests are more capable of adapting to pressures from climate change. The phrase describes procedures that modify a system’s reaction to environmental stresses like pollution. According to Butardo and Tenefrancia [30], the institutional, economic, and ecological health of urban areas, as well as their reliance on infrastructure, governance, and natural resources, all affect their capacity for adaptation. Additionally, it asserts that societies with high levels of adaptation are more resilient and able to bounce back from trying situations. As a result, trees can improve urban air quality while reducing air pollution and delivering ecosystem services. Engle [31] claims that the idea is usually overlooked and that one might assess a system’s adaptive capacities by fusing knowledge from vulnerability and resilience frameworks. One must first understand how fragile and robust different tree species are in order to fully grasp the urban forest’s ability to adapt to air pollution in cities. The use of biochemical parameters in trees can achieve this.
According to Escobedo
Ambient air pollution, among other things, has an impact on the physiological and biochemical characteristics of urban trees [32–35]. Therefore, a strong selection criterion that encourages resilient urban trees at these levels should be given priority in green programmes and campaigns. Numerous African cities have urban greening initiatives in place to increase the amount of urban forest cover, but little is known about how to choose resilient urban trees. Zambia is no exception. The air in mining towns is regularly contaminated by mineral exploration. In Zambia, urbanisation has led to problems with both human and economic expansion and environmental degradation with city dwellers ranking better streets, roads, public transport and mobility, crime prevention and security of tenure, and reliable energy at home and work, which were ranked seventh, eighth and ninth biggest problem, respectively [36]. Simukanga [37] claimed that air pollution in the Copperbelt Province was caused by transportation and mining. Thus, in order to find tree species that can adapt to different environmental conditions and tolerate air pollution, studies that use physiological and biochemical markers are critical to effective urban management.
This experimental study tracks Kitwe’s urban forest change using CVA as proposed by Xu
The study population comprises the urban centre of Kitwe, Zambia. Kitwe district is located between latitudes 12° and 13° east and longitudes 27° and 29° south (Figure 1) in the Copperbelt Province of Zambia. The mean altitude is over 1295 m above sea level, with an annual mean temperature of 22.3 °C and a mean yearly precipitation of 1226 mm. The district has three main seasons: the cold-dry season (April–July), which has a mean temperature of 15 °C. The hot dry season (August–October) has a mean temperature between 18.5 °C and 37 °C.
The city of Kitwe is located inside the biome known as tropical and subtropical grasslands, savannas, and shrublands. Specifically, it is within the Central Zambezian Miombo woodland ecoregion, which covers about half of the country. The Miombo woodland, characterised by the prevalence of Brachystegia, Julbernardia, and Isoberlinia tree species, which belong to the legume family. This places the City as aprt of a vast vegetation formation found in central, eastern, and southern Africa. This ecosystem is known for its seasonal dryness and deciduous nature. The city's charecteristic forested area is characterised by the presence of dambos, which are grassy wetlands that serve as the source and borders of rivers.
The study location is the ‘Hub of the Copperbelt’ and is more urbanised due to the city’s growing importance as an urban centre. It is also a mining town with trade activities. Though the district covers an area of 777 km2, the urban part of the district is made up of 24 formal settlements and 19 informal settlements with few or no basic municipal services [38]. According to UN studies (2009), poor waste management, poor water supply and sanitation, especially in low-income areas, poor road networks and drainage systems, the growth and expansion of informal settlements and their attendant problems, inadequate public health services, congestion in the Central Business District, particularly in the city market, air pollution from mining operations, and a declining economy were the main environmental development issues in Kitwe. These urban concerns have deteriorated urban living and environmental circumstances, lowering city dwellers’ quality of life making the city a good study area.
Kitwe is currently the most populated district in Copperbelt Province and the second most populated district in Zambia, with the present population standing at 522,092 with an estimated 3.3% growth rate per annum [39]. The city has seen major expansions over the past 25 years, with a 72.76% increase in population (from 337,000 to 522,092 people), and this has called for the local authority to instigate regularising and upgrading the informal settlements located in designated residential areas sometime in 2014 [1].
In terms of administration, Kitwe City Council is the supreme decision-making body at the district level. The District Council is responsible for all aspects of city planning and development, and as such, it oversees the formulation of local policies and approves district development plans. The council management structure consists of democratically elected councillors that represent their electorate in the twenty-five (25) wards. As such, this makes Kitwe City Council the primary custodian of urban management in the city.
The overall research design followed an exploratory study approach using both qualitative and quantitative methods of data collection and analysis in an integrated manner to meet the intended objectives. An epistemological and deductive approach to building the methods and procedures was used. The design was determined to understand the fundamental issues related to CVA (Figure 2) and the selection process of suitable trees (Figure 3). The initial community analysis was conducted during the rainy season to capture the peak growth of trees in 2018.
The study used Landsat images of Kitwe for two distinct years (1990 and 2015).
The Landsat images obtained had the characteristics outlined in Table 1 and were examined for the appropriate bands required for the project.
Sensor type | Thematic mapper (TM) | Enhanced thematic mapper plus (ETM+) | Pushbroom (both OLI and TIRS) |
---|---|---|---|
Platform | Landsat 4 (launched 16 July 1982) | Landsat 5 obtained (launched 1 March 1984) Landsat 7 (launched 15 April 1999) | Landsat 8 |
Orbit | 16 days/705 km | 16 days/705 km | 16 days/705 km |
Swath width | 185 km | 185 km | |
Bands | B1 (0.45–0.52 B2 (0.52–0.60 B3 (0.63–0.69 B4 (0.76–0.90 B5 (1.55–1.75 B6 (10.4–12.5 B7 (2.08–2.35 | B1 (0.45–0.52 B2 (0.52–0.60 B3 (0.63–0.69 B4 (0.76–0.90 B5 (1.55–1.75 B6 (10.4–12.5 B7 (2.08–2.35 B8 (0.50–0.90 | B1 (0.435–0.451 B2 (0.452–0.512 B3 (0.533–0.590 B4 (0.636–0.673 B5 (0.851–0.879 B6 (1.566–1.651 B10 (10.60–11.19 B11 (11.50–12.51 B7 (2.107–2.294 B8 (0.503–0.676 B9 (1.363–1.384 |
Ground pixel size | 30 m (bands 1–5,7) 120 m (band 6) | 30 m (bands 1–5,7) 60 m (band 6) 15 m/18 m (band 8) | 30 m (bands 1–7,9) 100 m (band 10–11) 15 m (band 8) |
The study evaluated datasets consisting of two images from two time points; 1990 and 2015. These two chosen datasets covered a larger period of coverage (i.e., 25 years) to enable the study to follow the significant urban expansion of the city of Kitwe. The Landsat 5 and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors with six bands and a spatial resolution of 30 m acquired these two multi-spectral datasets. The dataset consists of two images acquired for the city of Kitwe, Zambia, in November 2017 (
Preliminary image processing and data generation followed the building of two mosaics for the years 1990 and 2015. The spectral radiance (
is the spectral radiance
is the calibrated and quantized scaled radiance in units of digital numbers
is the spectral radiance at
is the spectral radiance at
The spectral radiances were then converted to reflectivity for each band based on the metadata of the images using the following Equation (2);
is the spectral radiance
is the inverse square relative Earth-Sun distance
is the reflectance for each band
is the solar Zenith angle in degrees
is the mean exoatmospheric solar irradiance
The study used NDVI as a simple graphical and numerical indicator that can be used to analyse remote sensing measurements and assess whether the target being observed contains live green vegetation or not. NDVI was computed using Equation (3). Satellite-derived NDVI measurements collected the measure of reflection in the infrared (0.73–1.10
is the near-infrared wavelength
is the red wavelength
Using the BSI (Equation (4)), the study was able to assess bare soil and what role it played within Kitwe’s urban ecosystem. The index was an indicator of urban expansion and exposed soil conditions. The index was used to differentiate between agricultural and non-agricultural land. BSI negative values or those near 0 represent zones with vegetation.
is the near-infrared wavelength
is the red wavelength
is the blue wavelength
Bare soil is soil or sand not covered by grass, sod, other live ground covers, wood chips, gravel, artificial turf, or similar coverings.
To detect land cover changes between 1990 and 2015, the change detection method was used. CVA was applied using the NDVI and BSI indices for two time points (
To develop change magnitude (i.e., to indicate the intensity of change as derived based on the Euclidian distance), Equation (5) was used, and it gave map outputs shown in Figure 9. The developed output image was classified into four categories: low (0–15), medium (15–30), high (30–45), and very high (45–60), which represent the sum of changes occurring between the dates. The study tracked changes using equation (5).
In order to determine the direction of the change between 1990 and 2015, Equation (6) was used, which detects a pixel that corresponds with a pixel corresponding to
The values between 90° and 180° represent increased vegetation cover and those between 270° and 360° represent bare soil (degradation).
To quantify the change, the study used an unsupervised classification approach using the Spatial Analyst tool in ArcGIS 10.4 after having calculated the NDVI and BSI indices. The indices were classified by a threshold value of 0.25 and then divided into four classes. Then the study calculated the percentages of each class individually to get the number of pixels per class and converted it to area.
Once the magnitude and direction of the land cover changes in the district were determined, the study focused on determining which vegetation tree species were commonly found within the town. This was done by determining the number of tree species present within the urban part of Kitwe. The initial community analysis was carried out during the rainy season to capture the peak growth of trees. This was carried out between October 2017 and December 2018.
From the total urban tree population, stratified sampling was employed to develop a species list showing the top 24 common species (Figure 10) using the relative abundance method [41]. This list classified the urban trees into tree species groups (strata) to understand their distribution and dominance around the urban centre of Kitwe. Classification of over 1,758 trees located within the built-up areas and within a 15 m road radius. Further classification of the trees was made as commercial, ornamental, fruit trees, and non-ornamental (indigenous) from the ten roads that connect the 42 settlements. From the top 24 species identified, the study was confined to only evaluating the top 9 tree species usable for streets and avenues, with an additional 2 commercial species commonly grown in the plantations on the Copperbelt Province.
Fresh leaf samples were collected from 4 mature individual urban trees identified in Table 2 in accordance with procedures by Sahu & Kumar Sahu [34]. A tree was selected randomly in a cluster to ensure geophysical uniformity between samples. These clusters were demarcated into three urban areas (Nkana East, Parklands, and Riverside) with the highest availability of all target species. This made it easier to have trees within the same age group and with similar background environments.
SN | Species name | Scientific species names | Common name/ English name | Tree type | No of trees sampled (n) | DBH (cm) |
---|---|---|---|---|---|---|
1 | | | Flamboyant, Flame Tree, gold mohar | Evergreen | 4 | 47.20 ± 13.40 |
2 | | | | Deciduous | 12 | 43.64 ± 22.32 |
3 | | | | Deciduous | 12 | 29.18 ± 3.47 |
4 | | | Orchid tree, camel's foot, mountain ebony | Deciduous | 12 | 22.09 ± 5.11 |
5 | | | Yellow cassia, Bombay blackwood, cassod tree, ironwood | Evergreen | 9 | 31.18 ± 18.06 |
6 | | | African tulip tree, fireball, flame of the forest, Flame tree | Evergreen | 11 | 47.72 ± 18.16 |
7 | | | Red frangipani, pagoda tree, red-jasmine | Evergreen | 10 | 17.91 ± 8.07 |
8 | | | Candahar, melina, goomar teak, white teak, | Evergreen | 12 | 43.27 ± 8.06 |
9 | | | Umbrella tree, Australian umbrella tree, ivy tree, octopus tree | Evergreen | 4 | 15.53 ± 13.00 |
10 | | | Flooded gum or rose gum | Evergreen | 4 | 10.09 ± 1.01 |
11 | | | Ocote pine, Nicaraguan pitch pine, oocarpa pine | Evergreen | 4 | 20.78 ± 1.56 |
To determine wood-specific density for carbon sequestration, wood samples were collected following the procedures described by Chave [42]. At least two core samples per sample tree were collected. From the two common methods for determining wood density, the study used the water displacement method, considering available resources.
The study used indicative biochemical data to evaluate tree tolerance as well as show urban tree species’ ability to adapt to varying environments. Biochemical data were collected from each fresh leaf sample from the forty-four sample trees. Sampling was observed over three sampling periods, accounting for 132 leaf samples obtained in total. Fresh leaf samples collected were taken to the Copperbelt University School of Natural Resources (SNR) laboratory for analysis. Biochemical data consisted of four parameters: ascorbic acid, relative water content, chlorophyll, and pH. Various laboratory apparatuses used included beakers, test tubes, clippers, and graduated cylinders, among others.
The study adopted a checklist by Kashyap
Grading character | Pattern of assessment | Grade allotment | Grading character |
---|---|---|---|
Tolerance | APTI | 2.0–6.0 6.1–10.0 10.1–14.0 14.1–18.0 18.1–22.0 | + + + + ++ + + + + + + + ++ |
Biological and Socio-economic | Plant habit | Small Medium Large | − + + + |
Canopy structure | Sparse/irregular/globular Spreading crown/open/semi-dense Spreading dense | − + + + | |
Type of plant | Deciduous Evergreen | − + | |
Laminar structure | Size | Small Medium Large | − + + + |
Texture | Smooth Coriaceous | − + | |
Hardiness | Delineate Hardy | − + | |
Economic value | Less than 3 3 or 4 uses Five or more uses | − + + + |
The information collected was analysed through the identification of common patterns, equations, comparisons of primary findings, and the statistical package SPSS. Interpretation of results and attempts to rationalise or understand the meaning of these figures and/or numbers were also considered as follows:
ERDAS Imagine 2014 was utilised for satellite image preparation, alteration, and treatment. All image processing and adjustments were completed here. For all mapping and visualisation software for index calculation, categorization, and visualisation, ArcGIS 10.4 was utilised. Excel was also used for data analysis and calculations.
Classification of the introduced and most common tree species was implemented using relative abundance (RA) [41], which ranked the most commonly found urban trees around Kitwe’s urban built area. RA is the number of individuals of each tree species and was summed up for all the species counted, divided by the total number of individuals in which the species occurred (see Equation (7)).
is the Relative Abundance
is the total number of individual trees per species
is the total tree population
To determine the tree’s tolerance and infer the tree’s adaptive capacity, biochemical data were collected from the laboratory using a laboratory protocol developed and recorded in the lab book. A summary of the data analysis techniques below.
Ascorbic acid content (mg/g) was measured according to the methods described by Pandey & Tripathi [10]. In brief, for each 1 g sample prepared into a test tube, 4 ml of oxalic acid-EDTA extracting solution, 1 ml of orthophosphoric acid, and 1 ml of 5% tetraoxosulphate (vi) acid were added to the mixture. The mixture was stirred for a minute, after which 2 ml of ammonium molybdate and then 3 ml of water were added. The solution was then allowed to stand for 15 min, after which the spectrophotometric method as described by Bajaj and Kaur [45] was done using the absorbance at 760 nm. The concentration of ascorbic acid in the samples was then extrapolated from a standard ascorbic acid curve and recorded in the laboratory book.
According to the method prescribed by Liu and Ding [46], RWC was collected using the drying method. Each fresh leaf sample was weighed using an analytic scale, with the result recorded in the laboratory logbook to get the fresh weight (FW), after which the sample was placed in an airtight vial. The vial was then fully hydrated by filling it with water to full turgidity for 2–3 h at room temperature. The sample was then removed, allowed to dry off moisture using tissue paper, and immediately weighed to get the turgid weight (TW) result. After this, the sample was placed in a drying oven to dry leaf samples at 70 °C for 24 h and weighed again to get the dry weight (DW). To calculate RWC, we used Equation (8) below.
Fresh weight
Dry weight
Turgid weight.
The U.S. EPA and Liang
To calculate the leaf extract pH value, a digital pH metre was used for each leaf sample. This was done by placing about 0.5 g of leaf sample, which was crushed and homogenised in 50 ml of de-ionised water, then centrifuged, and the supernatant was collected for pH measurement.
From the results collected from the ascorbic acid (A), RWC (R), total chlorophyll (T), and pH (P) analyses, Equation (12) was used to assess the APTI. The mathematical expression combines the four biochemical parameters into a single rate and is based on studies by Singh and Rao [49] and Pandey and Tripathi [10].
is the Air Pollution Tolerance Index
ascorbic acid content in mg g−1 of fresh weight.
total chlorophyll in mg g−1 of fresh weight.
pH of leaf extract and
relative content of water in percentage.
To interpret APTI, Table 4 was used to provide categorization [2] of the index values between sensitive and highly tolerant.
APTI value | Category |
---|---|
4.0–5.0 | Sensitive |
5.0–6.0 | Intermediate |
6.0–7.0 | Tolerant |
>7.0 | Highly tolerant |
Based on the results from the API checklist (Table 3), each response corresponded to a given grading character (+ or −) for each parameter. Then, using the total score per species, a percentage score was evaluated using Equation (13).
is grades obtained by tree species
is the maximum possible grade for any tree species
Each species’ percentage score was then used to interpret the results using the classification criteria in Table 5 below, which factors in the percentage score to assign the tree category.
Grade | Score (%) | Assessment category |
---|---|---|
0 | Up to 30 | Not recommended |
1 | 31–40 | Extremely poor |
2 | 41–50 | Poor |
3 | 51–60 | Moderate |
4 | 61–70 | Good |
5 | 71–80 | Exceptionally good |
6 | 81–90 | Excellent |
7 | 91–100 | Best |
The water displacement method was used to calculate WD. The method made volume measurement easy and reliable, even for irregularly shaped samples. Combining species-specific literature estimations and field measurements using the water displacement approach yielded the average WD. Immersing the 4.9-mm-diameter wood core samples from the sample trees in water and computing the ratio of the increase in water volume to the dry wood weight calculates water displacement.
Kettering
Because the species list developed identified species with no defined local allometric equations to develop estimates, published generalised equations were used instead. The species-specific growth data gathered was then plugged into the biomass estimation allometric equation developed by Chave
Tree biomass (estimated in kg matter per tree)
Height of the tree
Diameter at breast height
Wood density(g/cm3)
To calculate the CO2 according to the method described by Aguaron and McPherson [53], a multiplication factor of 0.50 was applied to the estimated AGB in kg matter per tree, and the result was further multiplied by 3.67 as shown below (Equations (15) and (16)).
The above equations measure AGB per tree up to the time of the study. Therefore, measurement of tree uptake of CO2 combined with core samples was done by using incremental borers taken at dendrometer measurement positions [54] to get accurate carbon sequestration of the trees through the years of the urban tree as well as WD.
The study used biochemical data to measure urban tree species’ adaptability and test the hypothesis (H0).
All analyses employed SPSS 20.0 (SPSS Inc., Chicago, IL, USA) with a significance threshold of P 0.05. Data was cleaned to remove significant outliers and inconsistencies. Basic descriptive tables and graphs were checked for normality before undertaking an analysis of variance (ANOVA), which is “robust” against normality breaches. The ANOVA examined sample differences. It tested the hypothesis (H0) that all urban trees are air pollution-resistant, adaptable, and provide enough biomass for urban greening.
After merging all three assessment tools, comparison conversations and rank analyses were conducted to select the best species for the greening programme’s objectives. The study analysed species feature-by-feature to determine their similarity. Scatter plots showed how best to blend these species.
A two-tiered rating scale determines the best species for each yardstick and for all three yards. The 11 species were rated from best (1) to worst (10).
The results of the CVA technique demonstrate several types of changes in terms of biomass growth and loss and LC changes over time. Kitwe is characterised by complex landscape changes induced by several causes, and Phiri
According to the study findings, the area is primarily defined by four major classifications (see Figures 4 and 5). Between 1990 and 2015, the built-up area’s LC rose dramatically while bare soil and vegetation decreased. The change vector images (Figure 5) produced from the two study periods permitted verification that the deforested area in Kitwe was 28,140 ha between 1990 and 2015. The NDVI index (see Figure 6) for this impacted area revealed a 3.45% rise in low vegetation for grasslands and agricultural fields, from 22,918 ha (1990) to 23,709.87 ha (2015). While the indigenous forest cover decreased by 24.93%, from 14,458.8 ha in 1990 to 10,853.01 ha in 2015, the NDVI index also decreased. This coincided closely with the Copperbelt Province’s annual average deforestation rate of 0.84%, according to the Forestry Department (2016).
Water resources decreased by 31%, from 13,619.53 ha in 1990 to 9,393 ha in 2015, which could be linked to changes in rainfall patterns and increased water abstraction [56]. This loss could also be a direct result of expanded, developed areas and the destruction of crucial recharge sites in this district as a result of mining and agriculture [57]. Figure 4 shows a decrease in bare soil, which could be a signal to increase concrete, or Figure 7 shows a continuum ranging from high vegetation conditions to exposed soil conditions in 1990 and 2015.
Figure 8 depicts the magnitude of the changes, which were relatively low within 92.94% of the district (see Table 6), demonstrating that while changes were occurring, the majority of them occurred within existing and much older communities rather than in new ones. According to the study, 6.36% of the district underwent medium-level alterations, particularly in the Northeast in regions such as Kafue and Kamfinsa, as well as parts of the CBD. The growth in built-up area is primarily owing to the city’s expansion, and it is likely to continue towards the northeast as more land parcels are approved for construction.
Kitwe magnitude (1990–2015) | ||
---|---|---|
Classification | Area (m2) | Percentage (%) |
Low (0–15) | 741,913,200 | 92.94 |
Medium (15–30) | 50,796,900 | 6.36 |
High (30–45) | 5,543,100 | 0.69 |
Very high (45–60) | 44,100 | 0.01 |
| |
The results suggest that 60% of Kitwe’s LC is degraded (see Table 7), with the remaining 40% showing no change (see Figure 9) and covering the main CBD of Kitwe as well as mine regions. Persistence may be a trend related to the lengthy existence of urban areas and the urban activities that take place in these regions.
Kitwe land degradation (1990–2015) | ||
---|---|---|
Classification | Area (m2) | Percentage cover (%) |
Degradation | 478,926,000 | 59.99 |
Persistence | 319,371,300 | 40.01 |
| |
Other than normal urban activities, most mining-related land use increased significantly in the 1970s and early 1980s during the copper industry’s boom years [58] and continue to influence land use in Kitwe. High alterations have been observed (Figure 9) in the Mindolo and Twatasha areas as a result of heavy mining activities, with much of the region being used as garbage disposal sites or tailings dams. When compared to the opportunity cost of using the land for other purposes such as agriculture and recreation, this is a loss of productive land. With the majority of the land transformed into a waste storage area, dangers from the loss of isolated, unique microhabitats within an otherwise undisturbed habitat have been discovered [59] to cause the local extinction of certain plant and animal species.
As expected, the majority of Kitwe’s built-up regions are composed of evergreen species rather than deciduous plants, and their continuous presence in the town is extremely likely to be the result of intentional introduction. Both introduced and indigenous species predominate. A post-hoc analysis using the Bonferroni correction found that there were no significant differences between the species groups in any of the urban tree species categories. This supports the theory that, because this is part of Kitwe’s urban tree inventory, it could have come from secondary urban forest programmes that happened at varied times following the city’s expansion. The top-ranking species identified (see Figure 10) as the most abundant species within the urban built-up region were
Despite the city having lost most of its indigenous green spaces, which have been converted into residential or commercial sectors over time, more indigenous species are regularly scattered about the city in small spaces.
In comparison to other towns like Mufulira, Kalulushi, Ndola, and Lusaka, these species are located along comparable sidewalks, roads, and avenues surrounding the city’s main metropolitan areas, forming homogenous habitats or green spaces. This analysis indicated that the prevalent selection procedures were derived from Commonwealth town planning systems, which were modelled after British practise and legislation. The study refers to research by Home [60], who confirms that the roots of these introduced species can be traced back to pre-colonial Northern Rhodesian times (now known as Zambia), when these areas were still being planned for as townships. Home [60] goes on to explain Lusaka’s reputation as a garden city, referring to “Dutton’s initiative” that constructed a chain of nurseries and planted various trees in Lusaka and sections of the Copperbelt, including the ones in this article.
After determining the most abundant species, the study analysed the plants’ ability to cope with environmental conditions by measuring plant sensitivity to their surroundings and carbon sequestration. The tree’s sensitivity was characterised in connection with its exposure to its environment and air pollution, which was very likely to occur in built-up areas. The focus of adaptive capacity was the ability of species groupings to respond to specific sorts of hazards—in this example, drought, plant health, and biochemical air pollution. These responses assist the urban trees in regenerating and/or adapting in order to reduce susceptibility and increase reaction time. According to Flórez Bossio
The laboratory test findings revealed biochemical differences (
SN | Species name | DBH (cm) | Height (m) | No of trees (N) | No of leaf samples (n) | pH | Ascorbic acid | RWC (%) | TChl | APTI | Categorization |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | | 47.20 ± 13.4 | 10.99 ± 2.95 | 4 | 12 | 5.821 ± 0.281 | 0.0928 ± 0.085 | 59.368 ± 18.068 | 0.692 ± 0.704 | 5.99 ± 1.76 | Intermediate |
2 | | 43.64 ± 22.32 | 17.29 ± 2.39 | 12 | 12 | 6.415 ± 0.263 | 0.0801 ± 0.027 | 70.459 ± 25.693 | 1.225 ± 0.419 | 7.11 ± 2.58 | Highly tolerant |
3 | | 29.18 ± 3.47 | 12.33 ± 0.96 | 12 | 12 | 4.997 ± 0.413 | 0.0327 ± 0.017 | 56.025 ± 7.924 | 1.132 ± 0.691 | 5.62 ± 0.79 | Intermediate |
4 | | 22.09 ± 5.11 | 10.55 ± 2.07 | 12 | 12 | 6.815 ± 0.577 | 0.0537 ± 0.034 | 78.800 ± 5.800 | 2.387 ± 4.334 | 7.93 ± 0.60 | Highly tolerant |
5 | | 31.18 ± 18.06 | 10.56 ± 1.71 | 9 | 12 | 6.148 ± 0.456 | 0.1131 ± 0.095 | 50.734 ± 23.044 | 1.763 ± 3.257 | 5.16 ± 2.27 | Intermediate |
6 | | 47.72 ± 18.16 | 10.31 ± 5.23 | 11 | 12 | 5.955 ± 0.209 | 5.9553 ± 0.209 | 56.432 ± 24.608 | 0.586 ± 0.618 | 5.68 ± 2.45 | Intermediate |
7 | | 17.91 ± 8.07 | 9.18 ± 2.94 | 10 | 12 | 5.884 ± 0.208 | 0.0109 ± 0.006 | 74.673 ± 29.467 | 0.727 ± 0.598 | 7.47 ± 2.95 | Highly tolerant |
8 | | 43.27 ± 8.06 | 13.92 ± 1.49 | 12 | 12 | 6.303 ± 0.390 | 0.1260 ± 0.086 | 61.549 ± 32.19 | 0.242 ± 0.239 | 6.24 ± 3.18 | Tolerant |
9 | | 15.53 ± 13.00 | 8.11 ± 5.42 | 4 | 12 | 5.926 ± 0.499 | 0.0199 ± 0.016 | 80.996 ± 16.018 | 0.507 ± 0.570 | 8.11 ± 1.61 | Highly tolerant |
10 | | 10.09 ± 1.01 | 6.96 ± 0.8 | 4 | 12 | 4.669 ± 0.179 | 0.1523 ± 0.090 | 70.633 ± 13.602 | 0.638 ± 0.791 | 7.15 ± 1.37 | Highly tolerant |
11 | | 20.78 ± 1.56 | 15.35 ± 2.18 | 4 | 12 | 5.267 ± 0.213 | 0.0858 ± 0.110 | 59.617 ± 31.006 | 0.826 ± 0.603 | 6.01 ± 3.05 | Tolerant |
| | | | | | |
The overall pH ranged between 4.67 and 6.80, with
Table 8 shows that the RWC was regularly distributed, with a mean of 65.39% (SD = 1.17). RWC values for urban tree species in Kitwe were in the higher range, i.e., between 50% and 90%. This was greatest in
There was a statistically significant difference in the means of TChl between the various urban tree species groups. When compared to other species,
Vulnerability, as it relates to the socioeconomic elements of the tree species, covered numerous aspects such as the aesthetic value of trees as well as the minimal demand for care and maintenance, which is the major criterion for use, particularly in urban management [63]. Take, for example,
The observations in Kitwe reveal that natural indigenous plants are subject to challenges from invasive species that are known to enter forest gaps, plantations, roadsides, and riparian zones (banks of watercourses) (ibid.). These have a large impact on Kitwe’s urban forest inventory because they are abundant in the built-up areas. CAB International classifies
Kitwe city land use dynamics posed an additional threat to the city’s urban forest’s ability to survive change. According to Phiri
Following the biochemical analysis, the APTI was used to determine which trees are appropriate species for urban greening programmes focused on mitigating or adapting to air pollution. According to the APTI data (Table 8),
According to the box plots for comparative species groups (see Figure 11), only four species groups (JM, BV, SA, and PO) have a high level of agreement with each other, while the rest of the data are dispersed across groups. These could be the result of various environmental factors, some of which could only be revealed by gaining access to the biological parameters. The results of the biochemical analysis (Table 8) of plants revealed species variation, which may be attributed to environmental variables such as shifting soil profiles throughout the city, which results in varying soil quality [67], and the age of the tree [68]. Such factors have been shown to have a direct effect on the two key parameters, ascorbic acid and TChl [69], and have been identified as the most significant contributors to species differences when compared to pH and RWC. This means that all common plants in a specific metropolitan location can be classified according to their sensitivity or tolerance to air pollution.
When the results are compared to the plants’ socioeconomic characteristics (Table 9), the highest API is found in
SN | Species name | APTI | Plant habit | Canopy structure | Type of plant | Size | Texture | Hardness | Economic value | Total plus (+) | API grade | % Scoring | Assessment category |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | | + | + | + + | + | + | − | + | + | 8 | 3 | 52.94 | Moderate |
2 | | + + | + + | + ++ | + | + + | − | + | − | 11 | 3 | 58.82 | Moderate |
3 | | + | + | − | − | + | − | + | + + | 6 | 1 | 35.29 | Extremely poor |
4 | | + + | + + | + ++ | − | + | + | + | + + | 12 | 4 | 64.71 | Good |
5 | | + + | + + | + + | + | + | − | + | + + | 11 | 3 | 58.82 | Moderate |
6 | | + | + | + + | + | + + | − | + | + | 9 | 3 | 52.94 | Moderate |
7 | | + | + | + | + | − | − | + | + + | 7 | 2 | 41.17 | Poor |
8 | | + + | + ++ | + + | − | + + | − | + | + + | 12 | 4 | 64.71 | Good |
9 | | + | − | − | + | − | + | + | + | 5 | 0 | 29.41 | Not recommended |
10 | | + | + ++ | + | + | + + | − | + | + + | 11 | 4 | 64.71 | Good |
11 | | + + | + ++ | + | + | + + | + | + | + + | 13 | 5 | 70.59 | Exceptionally good |
Kitwe’s urban forest had the highest WD (0.662 g cm−3) among
SN | Species name | Carbon uptake | |||||
---|---|---|---|---|---|---|---|
Wood specific gravity (g cm−3) | Average AGB (kg matter per tree) * | Number of trees in Kitwe urban | Total AGB in Kitwe urban (kg) | Carbon uptake in Kitwe urban (kg) | CO2 uptake in Kitwe urban (kg) | ||
1 | | 0.579 | 867.89 ± 650.58 | 282 | 244,744.72 | 122,372.36 | 449,106.56 |
2 | | 0.376 | 1,197.63 ± 1132.22 | 171 | 204,793.92 | 102,396.96 | 375,796.84 |
3 | | 0.490 | 289.69 ± 167.1 | 163 | 47,220.09 | 23,610.04 | 86,648.86 |
4 | | 0.653 | 130.41 ± 83.45 | 152 | 19,821.98 | 9,910.99 | 36,373.34 |
5 | | 0.650 | 533.35 ± 632.07 | 108 | 57,602.08 | 28,801.04 | 105,699.83 |
6 | | 0.220 | 395.55 ± 386.92 | 74 | 29,270.45 | 14,635.22 | 53,711.27 |
7 | | 0.500 | 113.20 ± 136.76 | 50 | 5,660.02 | 2,830.01 | 10,386.14 |
8 | | 0.340 | 501.22 ± 212.62 | 48 | 24,058.79 | 12,029.40 | 44,147.88 |
9 | | 0.413 | 119.34 ± 216.66 | 21 | 2,506.20 | 1,253.10 | 4,598.87 |
10 | | 0.662 | 18.52 ± 4.63 | 53 | 981.38 | 490.69 | 1,800.83 |
11 | | 0.440 | 240.31 ± 32.18 | 99 | 23,790.60 | 11,895.30 | 43,655.75 |
The fact that variance is modest (see Figure 12) may be attributed to the species’ ability to sequester carbon as well as the species’ abundance in urban areas. The ability of urban forests to sequester carbon is determined by various aspects, including (1) the age of the tree, (2) its size, (3) environmental considerations, and (4) policy and methods of generating deliberate enabling elements that enable such greening programmes. These considerations stem from the observation that there are considerable changes in carbon sequestration results between tree species (see Table 10) due to the difference between young and older urban trees. Anwar [68] verified this, arguing that older trees have lived longer and have had more time to store carbon than younger urban forests. According to the findings of this study, the two commercial tree species are young urban trees with extremely low biomass (Table 10). This has a substantial impact on each tree’s biochemical characteristics. For example, TChl varies over the tree’s life cycle. Table 8 displays how TChl changes depending on species, leaf age, pollution level, and other biotic and abiotic variables [71]. Certain pollutants increase TChl, while others decrease it [72].
Disaggregating all of the findings from APTI, API, and AGB reveals (see Table 11 and Figures 13–15) that there are disparities between the species groups, with the data being closely spread, showing a high level of similarity, and the rest of the results being scattered between groups. These could be the result of exposure to various environmental factors and the influence of certain functional groups.
In all comparisons, the overall associations were statistically significant (Figures 13–15). Three species,
When AGB and APTI (Figure 14) are compared, it is clear that while most species had low AGB, the species varied in terms of their tolerance to air pollution, implying a negative association. A statistically significant linear association (
After integrating each assessment evaluation, each species was evaluated from best (1) to worst (10) (see Table 11). The most suited trees were
SN | Species name | AGB rank | APTI rank | API rank | Score | Overall rank |
---|---|---|---|---|---|---|
1 | | 8 | 2 | 2 | 12 | 1 |
2 | | 2 | 5 | 5 | 12 | 1 |
3 | | 6 | 6 | 3 | 15 | 2 |
4 | | 7 | 7 | 1 | 15 | 2 |
5 | | 1 | 8 | 7 | 16 | 3 |
6 | | 11 | 4 | 4 | 19 | 4 |
7 | | 3 | 11 | 6 | 20 | 5 |
8 | | 9 | 3 | 9 | 21 | 6 |
9 | | 5 | 9 | 8 | 22 | 7 |
10 | | 10 | 1 | 11 | 22 | 7 |
11 | | 4 | 10 | 10 | 24 | 8 |
The findings for APTI, API, and carbon sequestration are consistent with those of Jim [67] and Bowler
To address the vulnerability dynamics of urban forests, urban planners should establish socioeconomic scenarios and follow and trace development trends and routes while designing adaptation solutions. Understanding urban trees’ sensitivity to air pollution, socioeconomic performance, and capacity for carbon sequestration will help in achieving these goals the best. Having high APTI and API values, as well as high carbon sequestration, should be promoted for new development areas employing properly developed urban greening programmes and giving specific adaptive capacity ranges [61]. These tree species could be incorporated into the design of an urban greening programme to help with long-term air pollution planning.
Kitwe’s main metropolitan areas have made considerable contributions to reducing air pollution and boosting carbon absorption, thereby increasing the city’s carbon stocks (see Table 10). All of the urban trees evaluated in Kitwe perform admirably in their surroundings, and there are substantial variances in tree species APTI, adaptive capacity metrics, and cumulative biomass.
Depending on the objective goals of the greening programme, one can compare one tree species to another to find the most effective combination that meets developmental needs that prioritise the climate variability of urban tree species as well as air pollution tolerance. The combination in Figure 15 allows urban planners and managers to assess trees suited for greening programmes aimed at improving air quality, providing shade, and improving aesthetics in low-pollution green spaces, houses, and sidewalks.
The application of APTI, API, and carbon sequestration in green infrastructure design gives planners a notion of which tree species might ameliorate air pollution and provide effective ecosystem services for urban greening programmes. The lack of air pollution data to establish a pollution gradient to compare our data between highly contaminated and less polluted areas is one of the study’s limitations. Furthermore, there was insufficient data on urban tree care services near the trees sampled to provide a comprehensive methodological approach to forest management. According to Nayak
The goal of this study was to assess how the land cover of the city of Kitwe had changed and to pinpoint any impacts on the urban forest that predominates there. The study’s findings suggest that remote sensing can help with SDG 11 objectives 6 and 7, which are concerned with reducing the negative environmental effects of urbanisation and enhancing the layout of green places. In addition to providing visualisation in the form of maps of Kitwe, remote sensing and GIS also assist in providing a clear indicator of change and tracking and monitoring the direction and scale of city expansion. The study was successful in developing a thorough picture of how the vegetation changed between 1990 and 2015. According to the study, LC changes have occurred in Kitwe partly as a result of mining activity and urban growth. While the central business district still exhibits a persistent presence as a result of the town’s age, having sprung up before the 1990s with more expansions in the newly developed areas, the areas being monitored showed low and medium change intensity, mostly in the northeast of the district. According to the data gathered, Kitwe City’s built-up area is home to a variety of native and exotic tree species. Plant sensitivity to their environment and their capacity to store carbon in built-up areas were used in the study to evaluate the plants’ capacity to deal with environmental conditions.
Kitwe has the top 24 common tree species, 11 of which were evaluated out of the 1758 trees found in the built-up region along the main roads and highways. The majority of the urban forests in Kitwe are made up of a variety of ornamental trees, which are frequently grown for their aesthetic value, attractiveness, and shade. According to the research, this mixture also includes opportunistic urban trees (invasive species) and fruit-bearing trees intermingled with native species. The study found the most common species and the direction of the city’s land changes, and it concluded that the newer areas for land development would need greening programmes that could incorporate an efficient plan that satisfies both the city’s and the plant’s capacity for adaptation at the design/planning stage. Therefore, the decision should always be based on the objective, geographic location, and needs of local development in areas northeast of the district.
Informing the public about the importance of vegetation, as some of these trees may be donated and planted in public places such as schools as part of city-wide programmes to maintain and conserve urban forests. Although all eleven species may live in low-pollution environments, it is best to keep them away from highly polluted areas such as industrial zones, roads, and highways. The study found that policy and regulation should be strengthened to be more reliable in terms of providing standard regulatory tools that impact green infrastructure (GI) expansion within cities and towns. This will aid in the development of robust, efficient, and successful urban regions.
The amount of carbon that could hypothetically be absorbed would provide annual carbon sequestration. Such data is useful for measuring urban forest production and ensuring the long-term viability of air pollution mitigation techniques, even when they are part of GI. It is also advised that local governments promote suitable species to counteract the biodiversity loss brought on by various urban development initiatives by deliberately creating an enabling environment. It is strongly advised that the API grading process include a broader range of appropriate stakeholders. Expert panels and focused interviews should be used to help understand the socioeconomic issues. It is recommended to use APTI and API together since API improves the selection criteria for acceptable plants. Furthermore, local governments should attempt to safeguard some of the endangered species by developing habitats for them and/or building neighbourhoods that not only provide food, shelter, and shade but also improve air quality, thereby avoiding species extinction.
It is recommended that the existing urban management system be replaced and that additional studies be performed to construct a selection framework. (1) The framework would expand on this study to offer a theoretical comprehension of the methods used to choose suitable plants. (2) Evaluate the regulatory structure and determine whether or not the measuring system is current. (3) Make sure the metrics align with regional and national standards. (4) Make it easy to compile a database of findings. It is recommended that future studies investigate the effects of climate change on urban tree species as well as the connections between APTI and carbon sequestration.
Without the support of the German Research Foundation, this study would not have been possible. I would like to thank everyone in the Science Research Group, but especially Phenny Mwaanga (PhD), Donald Chungu (PhD) and Phillimon Ngandgwe (PhD), for their insightful remarks and ideas including review of the paper. The School of Natural Resources Laboratory Technician, Flex Chileshe, Mr. Yolonimo Banda, and Michael Bwembya are also thanked for their contributions to the research.
The authors declare no conflict of interest.
David Agamemnon Banda was responsible for the study’s conception, analysis, documented sample methodology, and initial drafting. This entails overseeing both the study’s analyses and the corresponding literature searches.
Above-ground biomass
Analysis of variance
Anticipated performance index
Air pollution tolerance index
Below-ground biomass
Copperbelt University
Carbon dioxide
Atmospheric CO2 concentration
Diameter at breast height
Dry weight
Fresh weight
Global positioning system
Kitwe City Council
Most recent mature leaf
Natural-based solutions
Relative abundance
Relative water content
Total chlorophyll
Turgid weight
Source data (raw scientific data accompanying the research) for this article is available on Figshare: https://doi.org/10.5772/geet.deposit.c.6830907.v1
Supplementary Data
Downloaded from the U.S. Geological Survey (USGS) website using EarthExplorer (
Written by
Article Type: Research Paper
Date of acceptance: June 2023
Date of publication: October 2023
DOI: 10.5772/geet.17
Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0
© The Author(s) 2023. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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