Monitoring urban green space (UGS) changes by using high resolution aerial imagery: a case study of Kuala Lumpur, Malaysia

Urban green space (UGS) in a city is the foundation of natural productivity in an urban structure. It is also known as a natural cooling device that plays a vital role in the city as an urban lung, discharging oxygen to reduce the city heat and as a wall against harmful air pollution. When urbanization happens, UGS, including the gazetted areas, is essentially converted into an artificial surface due to the population’s demand for new development. Therefore, identifying its significance is a must and beneficial to explore. The purpose of this study is to identify the 10 years of UGS change patterns and analyze the UGS loss, particularly in the affected gazetted zone. The study used available aerial imagery data for 2002, 2012, and 2017, and database record of green space. The study had classified UGS by using the Support Vector Machine (SVM) algorithm. The training area was determined by visual interpretation and aided by a land use planning map as reference. The result validity was then determined by kappa coefficient value and producer accuracy. Overall, the study showed that the city had lost its UGS by about 88% and the total gain in built up area was 114%. The loss in UGS size in the city could be compared to a total of 2,843 units of football fields, transformed forever in just 10 years. The uncontrolled development and lack of advanced monitoring mechanism had negatively affected the planning structure of green space in KL. The implementation of advance technology as a new mitigation tool to monitor green space loss in the city could provide a variety of enhanced information that could assist city planners and urban designers to defend decisions in protecting these valuable UGS

Object-Based Supervised Machine Learning Regional-Scale Land-Cover Classification Using High Resolution Remotely Sensed Data

High spatial resolution (HR) (1m – 5m) remotely sensed data in conjunction with supervised machine learning classification are commonly used to construct land-cover classifications. Despite the increasing availability of HR data, most studies investigating HR remotely sensed data and associated classification methods employ relatively small study areas. This work therefore drew on a 2,609 km2, regional-scale study in northeastern West Virginia, USA, to investigates a number of core aspects of HR land-cover supervised classification using machine learning. Issues explored include training sample selection, cross-validation parameter tuning, the choice of machine learning algorithm, training sample set size, and feature selection. A geographic object-based image analysis (GEOBIA) approach was used. The data comprised National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters. Stratified-statistical-based training sampling methods were found to generate higher classification accuracies than deliberative-based sampling. Subset-based sampling, in which training data is collected from a small geographic subset area within the study site, did not notably decrease the classification accuracy. For the five machine learning algorithms investigated, support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), and learning vector quantization (LVQ), increasing the size of the training set typically improved the overall accuracy of the classification. However, RF was consistently more accurate than the other four machine learning algorithms, even when trained from a relatively small training sample set. Recursive feature elimination (RFE), which can be used to reduce the dimensionality of a training set, was found to increase the overall accuracy of both SVM and NEU classification, however the improvement in overall accuracy diminished as sample size increased. RFE resulted in only a small improvement the overall accuracy of RF classification, indicating that RF is generally insensitive to the Hughes Phenomenon. Nevertheless, as feature selection is an optional step in the classification process, and can be discarded if it has a negative effect on classification accuracy, it should be investigated as part of best practice for supervised machine land-cover classification using remotely sensed data

Long-Term Urban Forest Cover Change Detection with Object Based Image Analysis and Random Point Based Assessment

The urban forest provides various ecosystem services. Urban tree canopy cover measurement is the most basic quantification of ecosystem services. There have been few studies focused on long-term high-resolution urban forest change analysis. Further, few if any of these studies have compared object based image analysis (OBIA) and random point based assessment for determination of urban forest cover. The research objective is to define the urban forest canopy area, location, and height within the City of St Peter, MN boundary between 1938 and 2019 using both the OBIA and random point based methods with high spatial-resolution aerial photographic images and Light Detection and Ranging (LiDAR) data. One facet of this project is to examine the impact of natural disasters, such as the 1998 tornado, and tree diseases on the urban canopy cover area. LiDAR data was used to determine the height and canopy cover density of the urban forest canopy. The results were used to compare and contrast the methods, with verification via ground truthing. Results show that both methods gave comparable accurate results. The total canopy cover area remained consistent until 1995, then increased post-tornado. The location of canopy cover areas has changed throughout St Peter over time due to the tornado, the increase in size of the City of St Peter, and land use change within the City of St Peter. The canopy change due to diseases was not detectable

Using Remote Sensing and Biogeographic Modeling to Understand the Oak Savannas of the Sheyenne National Grassland, North Dakota, USA

Oak savannas are valuable and complex ecosystems that provide multiple ecosystem goods and services, including grazing for livestock, watershed regulation, and recreation. These ecosystems of the woodland-prairie ecoregion of the Midwestern United States are, however, in danger of disappearing. The Sheyenne National Grassland, North Dakota, a protected Prairie grassland-savanna, is a representative of such rare habitats, where oak savanna is found at the landscape scale. In this research, I map the distribution patterns of oak savanna in the Sheyenne using a combination of remote sensing and geospatial datasets, including landscape topography, soils, and fire disturbance. Further, I interpret the performance of a suite of advanced Species Distribution Modeling approaches including Maximum Entropy, Random Forest, Generalized Boosted Model, and Classification Tree to analyze the primary environmental and management factors influencing oak distributions at landscape scales. Woody canopy cover was estimated with high classification accuracy (80-95%) for two study areas of the Sheyenne National Grassland. Among the four species distribution modeling approaches tested, the Random Forest (RF) approach provided the best predictive model. RF model parameters indicate that oak trees favor gently sloping locations, on well-drained upland and sandy soils, with north-facing aspect. While no direct data on water relationships were possible in this research, the importance of the topographic and soil variables in the SDM presumably reflect oak preference for locations and soils that are not prone to water saturation, with milder summer temperatures (i.e. northern aspects), providing conditions suitable for seedling establishment and growth. This research increases our understanding of the biogeography of Midwestern tall-grass oak savannas and provides a decision-support tool for oak savanna management

Land-cover change within the peatlands along the Rocky Mountain Front, Montana: 1937-2009

Aerial photographs of nine peatlands along the Rocky Mountain Front, Montana, were analyzed in a GIS. The boundary of wetland extent was hand-digitized and the area within was classified into land-cover types including: total area, open fen, open water, woody vegetation, and non-wetland/agriculture. Changes in wetland extent and land-cover categories were evaluated from the earliest available imagery in 1937 to the last available imagery in 2009. Images prior to 1995 were orthorectified to correct inherent distortions. Results indicate little change in overall peatland area between 1937 and 2009 despite increasing air temperatures in the region. Open water area and the number of ponds increased over the study period, reflecting a rebounding beaver population. Agriculture in Pine Butte Fen, McDonald Swamp, and the Blackleaf Wetland Complex declined over the study period. Land purchases by the Nature Conservancy of Pine Butte Fen and McDonald Swamp have preserved the natural state of those peatlands, and they hold conservation easements for three of the other fens. One peatland is owned by the state and another is located within the Lewis and Clark National Forest. Conversely the sprawling Theboe Lake wetland has been heavily disturbed by ongoing agriculture since prior to 1937, and Bynum wetland has been heavily impacted since the middle of the study period

Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents

Land Use And Land Cover Classification And Change Detection Using Naip Imagery From 2009 To 2014: Table Rock Lake Region, Missouri

Land use and land cover (LULC) of Table Rock Lake (TRL) region has changed over the last half century after the construction of Table Rock Dam in 1959. This study uses one meter spatial resolution imagery to classify and detect the change of LULC of three typical waterside TRL regions. The main objectives are to provide an efficient and reliable classification workflow for regional level NAIP aerial imagery and identify the dynamic patterns for study areas. Seven class types are extracted by optimal classification results from year 2009, 2010, 2012 and 2014 of Table Rock Village, Kimberling City and Indian Point. Pixel-based post-classification comparison generated from-to” confusion matrices showing the detailed change patterns. I conclude that object-based random trees achieve the highest overall accuracy and kappa value, compared with the other six classification approaches, and is efficient to make a LULC classification map. Major change patterns are that vegetation, including trees and grass, increased during the last five years period while residential extension and urbanization process is not obvious to indicate high economic development in the TRL region. By adding auxiliary spatial information and object-based post-classification techniques, an improved classification procedure can be utilized for LULC change detection projects at the region level

Built-up area and land cover extraction using high resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

Abstract: Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa