Multi-temporal Landsat Images Research Articles

An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

In Syria, 76% of the forests are located in the Syrian coast region. This region is witnessing a rapid depletion of forest cover during the conflict that broke out in mid-2011. To date, there have been no studies providing accurate, reliable, and comprehensive data on the qualitative and quantitative aspects of forest change dynamics and the underlying drivers behind this change. In this study, changes in the dynamics of forest cover and its density between 2010 and 2020 were detected and analyzed using multi-temporal Landsat images. This study also analyzed the relationship between changes in forest cover and selected physical and socio-demographic variables associated with the drivers of change. The results revealed that the study area witnessed a significant decrease in the total forest area (31,116.0 ha, 24.3%) accompanied by a considerable decrease in density, as the area of dense forests decreased by 11,778.0 ha (9.2%) between 2010 and 2020. The change in forest cover was driven by a variety of different factors related to the conflict. The main drivers were changes in economic and social activities, extensive exploitation of forest resources, frequent forest fires, and weakness of state institutions in managing natural resources and environmental development. Forest loss was also linked to the expansion of cultivated area, increase in population and urban area. Fluctuating climatic conditions are not a major driver of forest cover dynamics in the study area. This decrease in forest area and density reflects sharp shifts in the natural environment during the study period. In the foreseeable future, it is not possible to determine whether the changes in forest cover and its density will be permanent or temporary. Monitoring changes in forest cover and understanding the driving forces behind this change provides quantitative and qualitative information to improve planning and decision-making. The results of this study may draw the attention of decision-makers to take immediate actions and identify areas of initial intervention to protect current the forests of the Syrian coast region from loss and degradation, as well as develop policies for the sustainable management of forest resources in the long term.

Understanding cotton cultivation dynamics in Aksu Oases (NW China) by reconstructing change trajectories using multi-temporal Landsat and Sentinel-2 data

Understanding spatiotemporal dynamics of cotton cultivation patterns is an essential input to cotton farming management, production prediction and policy formulations. Thus, this study demonstrates cotton cultivation dynamics by constructing their change trajectories using multi-temporal Landsat and Sentinel-2 images. Then we reclassified the trajectories into four change trends, namely permanent cotton-field, cotton-field gain, cotton-field loss and unstable changes type. The results showed that increased cotton-cultivated areas were greater than the decreased areas. Cotton fields’ expansion gradually appeared in the outer parts of the permanent cotton fields, especially towards the northern mountain front alluvial-plain. Production decline caused by cotton cultivation loss was 3.09 × 108 kg. The observed cotton cultivation dynamics were associated to farmers’ options driven by crop price fluctuations, agricultural policies and natural conditions. The production loss was associated to cotton field loss and fields that underwent unstable changes. Management strategies should thus focus intercepting the loss and the unstable change trajectories.

A quantitative analysis of shoreline changes along the coast of Bangladesh using remote sensing and GIS techniques

The dynamic shoreline of Bangladesh was analyzed for the last thirty years in this study, in order to identify the positional changes due to erosion and accretion. Remotely acquired, multi-temporal Landsat images, of 30 m resolution and comparable tidal conditions, were collected for the period 1989–2019 at a five-year interval. Shorelines were then manually digitized, and a transect-based analysis was carried out using the Digital Shoreline Analysis System (DSAS) extension in ArcGIS. Rates of shoreline change and net movement along transects were determined for the three different coastal zones of the country. Almost 12,000 transects were cast with 50 m interval, for the whole coast, majority of which showed shoreline retreat. The shoreline variability is found to be the highest in the central zone, with rates of erosion being observed up to ~ 120 m/year and accretion up to ~ 230 m/year. The western zone is found to be retreating steadily over the years, at an average rate of 15–20 m/year, while the eastern zone displayed significant changes only at a few localized sites. A supplementary polygon-based analysis was also carried out in ArcGIS, through which the net area of erosion and accretion was determined for the whole study period. The net area loss is observed to be the highest in central zone (~62 km2), whereas opposite scenario is found in the eastern zone which has gained a net area of around 30 km2.

Improve the Accuracy of Water Storage Estimation—A Case Study from Two Lakes in the Hohxil Region of North Tibetan Plateau

Lake water storage is essential information for lake research. Previous studies usually used bathymetric data to acquire underwater topography by interpolation method, and to therefore estimate water storage. However, due to the large area of Tibetan Plateau (TP) lakes, the method of bathymetry was challenging to cover the whole region of one lake, and the accuracy of the underwater topography, in which no bathymetric data covered, was low, which resulted in a comparatively large error of lake water storage estimation and its change. In this study, we used Shuttle Radar Topography Mission (SRTM) and in situ bathymetric data to establish the underwater topography of Hohxil Lake (HL) and Lexiewudan Lake (LL) in the Hohxil Region of North TP and estimate and analyzed the changes of lake level and water storage. The results showed HL and LL’s water storage was 5.12 km3 and 5.31 km3 in 2019, respectively, and their level increased by 0.5 m/y and 0.57 m/y during 2003−2018, respectively. They were consistent with those (0.5 m/y and 0.5 m/y) from altimetry data, and they were much more accurate than those results (0.077 m/y and 0.156 m/y) from bathymetric data. These findings indicated that this method could improve the accuracy of lake water storage and change estimation. We estimated water storage of two lakes by combining with multitemporal Landsat images, which had doubled since 1976. Our results suggested that the increasing precipitation may dominate the lake expansion by comparing with the change of temperature and precipitation and the increasing glacial meltwater contributed approximately 4.8% and 10.7% to lake expansion of HL and LL during 2000–2019 based on the glacier mass balance data, respectively.

Land use land cover change detection and monitoring of urban growth using remote sensing and GIS techniques: a micro-level study

Accurate information on the rate of land-use pattern changes and urban expansion is essential for the sustainable development and management of natural resources. The rapid urban growth is a major challenge for local government and urban planners in small cities of India due to an insufficient database and inadequate analysis of chaotic urban expansion. The present paper has evaluated the land use/land cover change (LULCC) dynamics of the Barrackpore Subdivision area, India using remote sensing data and utilized the urban growth. Multi-temporal Landsat images with the maximum likelihood classifier (MLC) method were applied to generate the land use land cover maps. The spatiotemporal changes were performed by the “from-to” change matrix identifier to study land transformation. LULCC matrix demonstrated that vegetation cover, agricultural/cropland, wetland, and water bodies have decreased area under 23%, 7%, 6%, 1.7%, while the area under built-up and fallow lands have increased by 32.2% and 6.3% during 1972–2016. The results also revealed marked variation in the growth of built-up areas. The positive correlation between population and built-up land growth indicated that the impact of population pressure has contributed to faster growth of built-up land in the study area. The study calls for policies for proper land use management and sustainable urban development.

Climate Change and Anthropogenic Interferences for the Morphological Changes of the Padma River in Bangladesh

This research aims to identify the morphological changes of the Padma River due to the effects of anthropogenic climate change. The morphological changes were measured by aerial satellite images and their historical comparison, terrestrial survey, sedimentation in the riverbed, water flow, water discharge, siltation, and erosion along the river, etc. The Padma River has been analyzed over the period from 1971 to 2020 using multi-temporal Landsat images and long-term water flow data. The climatic parameters data related to temperature and rainfall were collected from 21 metrological stations distributed throughout Bangladesh over a 50-year period (1965-2015) to evaluate the magnitude of these changes statistically and spatially. The Padma, traditionally considered as a dominantly meandering river, is switching over into a braided river due to its highly susceptible nature of erosion and deposition. Results reveal that the tidal range is high during the dry season and increases from upstream to downstream of the river. Climate change may bring changes upstream by changing rainfall intensity, flood severity, and extreme temperature. More inundation can occur due to sedimentation, and more bank erosion can occur at the same time. An exponential increase of morphological activity with increased river flow, water discharge, bank erosion might substantially increase in the future. The changes in the flow introduced by climate change would impact the morphology of the Padma River of Bangladesh during the monsoon. A major change has been observed in the location of the bank and channel, as well as bars, along with their geometry and morphology over time. It is also observed that the bank line is not stable and migrated continuously. The overall width of the Padma River is varied significantly during the last 50 years. Maps and Landsat images represented that the river channel is shifting abnormally. Both climatic parameters and anthropogenic activity play an important role in fish biology and production. From this study, it is hypothesized that this assessment’s findings might help understand the overall hydrodynamic and morphological nature of the Padma River. It will suggest possible future developmental works that might be implemented on this river.

Evaluation and Monitoring of Water Quantity and Quality of the Buriganga River in Bangladesh using Multi-temporal Landsat Images

Buriganga River, the study area, is one of the most polluted and decreasing expeditiously its area in Bangladesh due to rapid urbanization, effluents of industries and factories surrounding the river, sewage disposal from Dhaka City and some anthropogenic activities. The objective of this study is to evaluate and monitor the water quantity and quality of the river using satellite remote sensing techniques. Unsupervised and indices based classification were used to derive and monitor landuse-landcover (LULC), surface water distribution (SWD), land surface temperature(LST) and total suspended material (TSM) using four sets of Landsat TM/ETM+/OLI/TIRS images of the study area from 1989 to 2015. The indices are Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). LULC classification results showed that the water bodies and vegetation decreased and consequently urban as well bared area increased from 1989 to 2015. Results of indices (NDVI and NDWI) analysis are similar to that of unsupervised LULC outputs, that is, the water bodies decreased with increasing urban structures of the study area. The surface water distribution monitoring results from the suitable change detection GIS model indicate that the water bodies have decreased about 31.07% and accretion rate increased rapidly from 1989 to 2015 along the river bank due to urbanization and accretion activity is more prominent in north, northeast, northwest, south, southeast and eastern part. The study also shows that the rate of TSM is sporadically increasing during the study period i.e., the maximum and minimum value of TSM was 56215.53 and 1956 mg/l in 1989 and 14188714.35 mg/l and 333942 mg/l in 2015 respectively; this indicates that the water is harmful for aquatic life. Both the analyzed satellite image outcome and in situ observations reveal that land surface temperature is also increased in some part of the study area. The study results could be used to make policy for upgrading the water quality and to maintain the extent and water quantity for agriculture, navigation and fisheries sectors of the Buriganga River.
 The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(1), 2019, P 61-69

Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

Quantifying urban expansion using Landsat images and landscape metrics: a case study of the Halton Region, Ontario

The Halton Region, as part of the Greater Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national gross domestic product. It is also one of the most desirable places for living and for thriving businesses. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches, and landscape metrics. Multitemporal Landsat images and the supervised learning algorithms in GIS software were used to explore the dynamic changes and to classify the urban and nonurban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and it mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods including the Land Use in Central Indiana (LUCI) model, the vegetation-impervious surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of the driving forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating that leapfrog pattern of urbanization occurred over the entire period. The purpose of this research is to evaluate urbanization in the Halton Region and give the city managers data to make appropriate decisions in further urban planning.

EFFECTS OF URBAN EXPANSION ON VEGETATION COVER LOSS IN LAGOS STATE, NIGERIA

Urbanization is a significant human activity affecting the environment. The study analyzes land use/land cover changes and examines built-up expansion through multi-temporal Landsat images spanning 1985 to 2015. In order to assess the pattern of urbanization, land consumption ratio (LCR) and land absorption coefficient (LAC) have been estimated. In 1985, vegetation cover occupied 54.70%, water bodies accounted for 22.10%, and built-up/open area accounted for 15.80% of the total study area. However, in 2015, vegetation and water bodies reduced drastically to 42.60% and 17.90% while there was a geometric progression in built-up/open area accounting for 39.50% of the entire study area. Also, between 1985-2015, vegetation cover and water bodies declined by 0.74% and 0.63% while the built-up/open area increased at an average rate of 4.99% per annum. The analysis of land consumption ratio revealed that built-up expansion has led to a high level of consumption of vegetation cover and water bodies at the rate of 0.016, 0.007 and 0.006 in 1985, 2000 and 2015 respectively. Similarly, land absorption coefficient accounted for 0.002 and 0.005 between the periods of 1985-2000 and 2000-2015. The rate of land acquisition indicates that development is high over the 30 years leading to a decline of green areas. If the present trend continues, it will lead to severe degradation of urban micro-biome, especially wetland resources, hence the call for a proper land-use policy for Lagos and other similar megacities in Nigeria.

MAPPING EXTENSION AND MAGNITUDE OF CHANGES INDUCED BY CYCLONE IDAI WITH MULTI-TEMPORAL LANDSAT AND SAR IMAGES

Abstract. In this paper it is described a study case of a rapid assessment of change detections for post-cyclone Idai vegetated damage and flood extension estimation by fusion of multi-temporal Landsat and sentinel-1 SAR images. For automated change detection, after disasters, many algorithms have been proposed. To visualize the changes induced by cyclone we tested and compared two automated change detection techniques namely: Principal Components Analysis (PCA), Normalized Difference Vegetation Index (NDVI) and image segmentation. With the image segmentation of multispectral and SAR images, it was possible to visualize the extension of the wet area. For this specific application, PCA was identified as the optimal change detection indicator than NDVI. This study suggested that image segmentation, principal components analysis, and normalized difference vegetation index can be used for change detection of surface water due to flood and disasters especially in prone countries like Mozambique.

Monitoring Recent Lake Variations Under Climate Change Around the Altai Mountains Using Multimission Satellite Data

Estimating lake dynamics is vital for the accurate evaluation of climate change and water resources monitoring. However, it remains a challenge to estimate the lake mass budget due to extremely scarce in situ data, especially for alpine regions. In this article, multimission remote sensing observations were blended to examine recent lake variations and their responses to climate change around the Altai Mountains during 2001–2009 and 2010–1018. First, the multitemporal Landsat images were used to enable the detailed monitoring of the surface extent of 43 lakes (> 5 km2) around the Altai Mountains from 2001 to 2018. The results presented that the total lake surface extent shrunk from 9835 km2 in 2001 to a minimum of 9652 km2 in 2009, while subsequently rose to 9714 km2 in 2018. By combining the lake area with the water level derived from the ICESat and CryoSat-2 altimetry data, the water storage of seven lakes covering ∼84% of the overall lake area in the region was obtained. The total water storage was detected with a decrease of 4.86 ± 1.17 km3 from 2003 to 2009 and a decrease of 3.65 ± 1.16 km3 from 2010 to 2018, respectively. Although most of the glaciers in this region had a significant mass loss in the past decades, the factor analysis indicated that most of the lakes had maintained a steady or slightly changing tendency because the glacial melting water was counteracted by the negative impact of high evapotranspiration amount. For the lakes with a few glacier melting supplies, e.g., the Uvs lake and Hyargas lake, the significant water budget loss was caused by the increasing evapotranspiration, decreased precipitation, and developed animal husbandry, which mainly dominated the overall decreasing trend of lake water storage in the Altai Mountains.

Shoreline changes and its impact on the mangrove ecosystems of some islands of Indian Sundarbans, North-East coast of India

This study aims to analyse the shoreline oscillations of three estuarine Islands in Sundarban delta and its impact on mangrove forests around the Islands. Six multi-temporal Landsat images spanning 42 years (1975–2017) have been used in the study. Band ratio was computed to discriminate the water line from the land, which was later digitized. Digital shoreline analysis system (DSAS) was employed for estimation and analysis of the shorelines changes by End point rate (EPR) model and Linear regression rate (LRR) model after laying transects offshore of the baseline. Sea level and topography of the islands have also been analyzed. To assess the mangrove health, time series Normalized Differential Vegetation Index (NDVI) analysis has been performed using the Mann Kendall Tau statistics and Sen’s slope. Mangrove degradation maps were produced from the data and combined with evidences collected from field works. The results point to a very dynamic shoreline ensuing in erosion of mangrove forests while some areas do show encouraging trends due to sustained accretion especially in the southern and eastern parts. Overall erosion is higher than accretion in the Islands. Results show that NDVI has been decreasing along patches that are near to erosion hotspots irrespective of climatic trends. Thus it can be concluded that mangrove forests are under severe stress due to shoreline ingression and sea level rise and not climatic alterations. Going forward this work could provide significant information on the nature of shoreline changes and could assist in sustainable development for Sundarban biodiversity niche management.

Prediction of land cover change based on CA-ANN model to assess its local impacts on Bagerhat, southwestern coastal Bangladesh

This study aims to assess land use dynamics of Southwestern coastal Bangladesh using multi-temporal Landsat images. Future trend was done by CA-ANN model. Hybrid image classification method and estimation of land surface temperature along with field survey were accomplished. This study revealed that vegetation decreased significantly (11.75%) and converted mostly into wetlands whereas built-up area slightly increased (1.44%) during 2000 to 2016. According to FGD (focused Group Discussion), a major occupational transformation (from agriculture to shrimp cultivation) and internal migration towards nearby Cities are prominent here. Besides, maximum land surface temperature increased by 12.5˚C during this period as a result of land use change. Simulated land use model of 2024 revealed that 2.49% of settlement would diminish especially from southern region if any natural calamity occurs. Finally, a comprehensive land use management plan has been suggested in this study to establish a balanced and diversified use of coastal land.

Understanding the dynamics of landscape of greater Sundarban area using multi-layer perceptron Markov chain and landscape statistics approach

Sundarban mangrove is the largest continuous mangrove forest in the world and is the habitat of an incredible amount of biodiversity. Biodiversity loss and degradation of this highly ecologically productive mangrove forest due to climate change will have a tremendous impact on the livelihood of the people living in the deltaic region. The present study was aimed to find out the dynamics of change in different land cover for the greater Sundarban region. The multi-temporal Landsat images were used to understand the dynamics in three decades from 1998 to 2018. The analysis revealed that Sundarban mangrove forest changed very insignificantly. The change between 1998 and 2008 was approximately 1.2%, i.e. around 26.2 km2, and the change between the year 2008 and 2018 was approximately 1.27%, which consists of 28 km2. However, the dynamics of open mangrove forest and dense mangrove forest were found very high. The human habitat and aquaculture were found as the most rapidly expanding land-use type in this landscape. Area under human habitat has increased approximately 80%, i.e. nearly 654 km2 between 1998 and 2018; area under aquaculture has increased approximately 116% (i.e. around 231 km2) between 1998 and 2008. The MLP-Markov Chain based modelling of land-use has shown an overwhelming increase in human habitat in near future. However, being situated in the close vicinity of dense human population of the city Kolkata, the mangrove habitat remained intact, which is largely due to great amount of conservation efforts that have been exercised by the Govt. and the local forest department. The landscape statistical analysis for understanding the ecological dynamics of every land cover types showed that in 2018, the human habitat became second dominant category in this study area. The aquaculture practice for high economic value has pushed down the native agricultural practice in the region.

Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.

OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES

The development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping.

Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images

Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.

On the absurdity of rapid urbanization: Spatio-temporal analysis of land-use changes in Morogoro, Tanzania

This study questions the frequent overemphasis on population growth aspects of African urbanization with little consideration of the spatial extent by analyzing the influence of population growth on the spatial expansion of the Morogoro urban municipality (MUM) in Tanzania between 2000 and 2016. Shannon's Entropy, a random forest supervised classifier, and spatial analysis were adopted to analyze Multi-temporal Landsat images obtained through the Google Earth Engine platform to quantify the spatial and temporal distribution and pattern of land-use change. Findings from this research show that Shannon's entropy values for MUM increased from 0.522 in 2000, to 0.761 in 2007, and to 0.901 in 2016 with the urban land cover recording a considerable and consistent increase. Similarly, the municipality's annual rate of change in population decreased from 4.17% in 1967 to 3.81% in 2016, and is estimated to rise to 4.54% by 2030 with a corresponding population of 25,262 in 1967 and 622,000 in 2016. From the results, the rate of population growth is not commensurate with the rate of spatial expansion, as the spatial extent is more than twice the population growth. An important contribution from this research relates to the limited attention to the faster rate of urban expansion compared to population growth in African cities; a situation that is inconsistent with sustainable and resilient urban futures. It is recommended that municipal authorities should consider initiatives (e.g., environmental planning models) to reverse the current trend of urban growth in order to improve the health, density, sustainability and resilience of the urban environment.

REMOTE SENSING EVALUATION OF ECOLOGICAL ENVIRONMENT OF ANQING CITY BASED ON REMOTE SENSING ECOLOGICAL INDEX

Abstract. Figuring out the regional ecological environment quality and ecological change is critical for ecological environment monitoring and management and urban construction planning. Based on the remote sensing ecological index (RESI), we evaluate the ecological quality and ecological change from 1999 to 2019 of Anqing city. Multi-temporal Landsat images are used to extract the four indicators of humidity, vegetation, heat and dryness, respectively. Then the RSEI is calculated by principal component analysis. The results show that the ecological quality of Anqing city declined from 1999 to 2019 and then grew slowly from 2009 to 2019. The eco-environmental quality of Anqing city dropped slightly from 1999 to 2019, and the regions with worse quality grades exceeded those becoming better. Particularly, we find that from 1999 to 2009, the area where the ecological quality became better made up 18.31% of the urban area, while the worse ecological area accounted for 29.68% of the urban area; from 2009 to 2019, the area of improved ecological environment reached 24.35%, while the area of degraded quality constituted 41.36%. Land-use changes dominated eco-environmental quality. The areas of poor eco-environmental quality expanded in residential regions and eco-environmental quality of mountainous area improved since returning cultivated land in steep hills into forest. The RSEI results are expected to provide a quantitative foundation for planning sustainable development and the rational use of resources in Anqing city.