Developed Land Cover Research Articles

Reduced reflectance and altered color: The potential cost of external particulate matter accumulation on urban Rock Pigeon (Columba livia) feathers

Feather quality in birds is considered an honest signal of individual health as feather appearance and function depend on an individual’s ability to maintain them. In addition to flight and insulation, feathers are essential for social interactions and sexual selection in the form of visual signals. Airborne particulate matter (PM) can accumulate on feather surfaces and alter feather appearance. We quantified PM accumulation on Rock Pigeon (Columba livia) feathers and analyzed the spectral properties of extracted particulates. Feathers were sampled from two pigeon populations, one rural and one urban, in the Dallas-Fort Worth area, with 47 and 93% developed land cover, respectively, within 2 km of the populations. We determined accumulated PM gravimetrically after rinsing feathers and then measured the visible-near-infrared diffuse reflectance and color properties of extracted particulates. The rate of PM accumulation on rural birds was higher than on urban birds. However, feather particulates collected from urban pigeons had significantly lower total reflectance, reflectance in the visible portion of the spectrum, lightness, and hue angle compared to those of rural pigeons. The hue angle of rural feather particulates displayed a negative relationship with PM accumulation while total reflectance, reflectance in the visible range, and lightness of urban feather particulates were negatively related to PM accumulation. Our findings suggest that wild birds could incur an urban pollution penalty as PM accumulation has the potential to alter feather properties. Further research is needed to better understand the influence of external PM accumulation on the physiological and behavioral health of birds.

Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Earth observation technology has improved the detection of land cover changes. However, current pixel-based change detection methods cannot adequately describe the evolutionary process and spatiotemporal association of geographic entities. Therefore, we developed a method for analyzing the processes and patterns of land cover evolution based on spatiotemporal graphs. First, a spatiotemporal graph was generated from a time series of land cover maps according to the spatial and temporal relationships between land cover objects, as defined by spatial adjacency and temporal transition, respectively. Subsequently, structural characteristics, such as the spatial roles, adjacency type, temporal transitions and evolution trajectories, were derived from the spatiotemporal graph to describe and analyze the evolution of land cover. Finally, this method was applied to analyze land cover evolution in Fujian Province, China, from 2001 to 2019. The proposed method not only completely preserves the spatial adjacency and temporal transition details among land cover objects in a spatiotemporally unified graph framework but also extracts evolution-related spatiotemporal structural characteristics. This study provides a reliable scientific basis for analyzing the consistency of long-term land cover dynamics and has practical value for other geographic applications.

Multimodal and Multitemporal Land Use/Land Cover Semantic Segmentation on Sentinel-1 and Sentinel-2 Imagery: An Application on a MultiSenGE Dataset

In the context of global change, up-to-date land use land cover (LULC) maps is a major challenge to assess pressures on natural areas. These maps also allow us to assess the evolution of land cover and to quantify changes over time (such as urban sprawl), which is essential for having a precise understanding of a given territory. Few studies have combined information from Sentinel-1 and Sentinel-2 imagery, but merging radar and optical imagery has been shown to have several benefits for a range of study cases, such as semantic segmentation or classification. For this study, we used a newly produced dataset, MultiSenGE, which provides a set of multitemporal and multimodal patches over the Grand-Est region in France. To merge these data, we propose a CNN approach based on spatio-temporal and spatio-spectral feature fusion, ConvLSTM+Inception-S1S2. We used a U-Net base model and ConvLSTM extractor for spatio-temporal features and an inception module for the spatio-spectral features extractor. The results show that describing an overrepresented class is preferable to map urban fabrics (UF). Furthermore, the addition of an Inception module on a date allowing the extraction of spatio-spectral features improves the classification results. Spatio-spectro-temporal method (ConvLSTM+Inception-S1S2) achieves higher global weighted F1Score than all other methods tested.

Effects of landscape characteristics, anthropogenic factors, and seasonality on water quality in Portland, Oregon

Urban areas often struggle with deteriorated water quality because of complex interactions between landscape factors and climatic variables. However, few studies have considered the effects of landscape variables on water quality at a sub-500-m scale. We conducted a spatial statistical analysis of six pollutants for 128 water quality stations in four watersheds around Portland, Oregon, using data from 2015 to 2021 for the wet season at two microscales (100m and 250m buffers). E. coli was associated with land cover, soil type, topography, and pipe length, while lead variations were best explained by topographic variables. Developed land cover and impervious surface explained variations in nitrate, while orthophosphate was associated with mean elevation. Models for zinc included land cover and topographic variables in addition to pipe length. Spatial regression models better explain variations in water quality than ordinary least squares models, indicating strong spatial autocorrelation for some variables. Our findings provide valuable insights to city planners and researchers seeking to improve water quality in metropolitan areas by manipulating city landscapes.

A Comparative Study of Various Land Use and Land Cover Change Models to Predict Ecosystem Service Value

Ecosystem services are closely related to human well-being and are vulnerable to high-intensity human land-use activities. Understanding the evolution of land use and land cover (LULC) changes and quantifying ecosystem service value (ESV) are significant for sustainable development. In this study, we used land use and land cover data and other data from 2000 to 2020 to analyze the evolution of land use and land cover and ESV in Tongliao, China. With the goal of exploring the characteristics of different cellular automata (CA)-based models, CA-Markov, Future Land Use Simulation (FLUS), and Patch-generating Land Use Simulation (PLUS) models were used to simulate future land use and land cover, and the results were verified and compared. Considering the impacts of policies for capital farmland (CF) and ecological protection red line (EPRL) in the context of territorial spatial planning, four scenarios (inertial development, S1; CF, S2; EPRL, S3; EPRL and CF, S4) were set. The results showed that from 2000 to 2020, farmland and built-up land increased the most (341.18 km2 and 220.56 km2), while grassland had the largest decrease (380.08 km2). The main mutual transitions were from grassland and farmland. The total ESV showed a decreasing trend (from 52,364.56 million yuan to 51,620.62 million yuan). The simulation results for 2035 under four scenarios were similar, where farmland would decrease the most (96.81 km2). The ESV in 2035 would decrease from 51,620.62 million yuan to 51,541.12 million. In addition, under scenarios for the impact of policy, the land showed a trend of scattered expansion. This study provides a scientific basis for making regional sustainable development policy decisions and implementing ecological environmental protection measures.

Evolution of Land Cover in the Traras MTS. Region Between 1984 and 2020 by Remote Sensing and Gis (Northwest Algeria)

Abstract In Algeria, the mountainous zones represent 11% of agricultural land of the country and are home to 25% of the population. The economic development and the associated urbanization have led to environmental degradation in the Traras Mountains, which are part of the Atlas Mountains. With the aim to evaluate the degradation consequences of this region, we focused on the evolution of land use and land cover over 36 years (1984–2020). Accordingly, we used Landsat and Google Earth Pro images, topographic and ecological inventory maps, as well terrain truth; our analysis tools were mainly based on ArcGIS, Global Mapper, and ENVI software. The diachronic analysis showed that the Traras Mountains lost 8,100 ha of agricultural and forestland between 1984 and 2020. Relating to this, we point out the destruction of 9,200 ha of forested area by the fire. The urbanization also contributed to the loss of agricultural and forested areas (6,300 ha), of which 27% are located in the coastal zone. The built-up situation led to an increase of soil impermeability which causes flooding during heavy rainfall.

Ecological drivers of dog heartworm transmission in California

BackgroundEffectively controlling heartworm disease—a major parasitic disease threatening animal health in the US and globally—requires understanding the local ecology of mosquito vectors involved in transmission. However, the key vector species in a given region are often unknown and challenging to identify. Here we investigate (i) the key vector species associated with transmission of the parasite, Dirofilaria immitis, in California and (ii) the climate and land cover drivers of vector presence.MethodsTo identify key mosquito vectors involved in transmission, we incorporated long-term, finely resolved mosquito surveillance data and dog heartworm case data in a statistical modeling approach (fixed-effects regression) that rigorously controls for other unobserved drivers of heartworm cases. We then used a flexible machine learning approach (gradient boosted machines) to identify the climate and land cover variables associated with the presence of each species.ResultsWe found significant, regionally specific, positive associations between dog heartworm cases and the abundance of four vector species: Aedes aegypti (Central California), Ae. albopictus (Southern California), Ae. sierrensis (Central California), and Culiseta incidens (Northern and Central California). The proportion of developed land cover was one of the most important ecological variables predicting the presence or absence of the putative vector species.ConclusionOur results implicate three previously under-recognized vectors of dog heartworm transmission in California and indicate the land cover types in which each putative vector species is commonly found. Efforts to target these species could prioritize surveillance in these land cover types (e.g. near human dwellings in less urbanized settings for Ae. albopictus and Cs. incidens) but further investigation on the natural infection prevalence and host-biting rates of these species, as well as the other local vectors, is needed.Graphical

Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989–2018

The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPERPNW) to the upper Missouri River basin (PROSPERUM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPERUM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPERUM are generally consistent with the PROSPERPNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPERUM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.

Looking Closer at the Patterns of Land Cover in the City of Porto, Portugal, between 1947 and 2019—A Contribution for the Integration of Ecological Data in Spatial Planning

As more people reside in cities and metropolitan areas, urban vegetation assumes an increasingly important role as one the main providers of ecosystem services in close proximity to human agglomerations. To improve the conditions for citizens and to optimise the sustainability of urban areas, the fields of landscape and urban ecology need to address the urgent priority to integrate ecological data in spatial planning, design, and management programs. With the objective to produce “actionable knowledge” for urban planning in the city of Porto (Portugal), we analyse the evolution of land cover since the mid-20th century at a fine spatial scale. Porto has followed the global trends of urbanisation, marked by a general increase in built-up and impervious surfaces that conquered the previously rural surrounding areas. This caused a severe decline in vegetation cover (especially herbaceous), as well as an increase in fragmentation and isolation of the remaining vegetation patches. These outcomes provide a detailed analysis of the city spatial dynamics, generating valuable information that can be relevant for future interventions regarding urban landscape change at a local scale, the most relevant for planning.

Study on the Evolution Law of Surface Landscape Pattern in Earthquake-Stricken Areas by Remote Sensing: A Case Study of Jiuzhaigou County, Sichuan Province

Earthquakes not only cause the destruction of surface buildings and a large number of casualties, but also have an important impact on regional land-use change. Timely understanding of land cover and its changes before and after earthquakes is of great scientific significance for studying the restoration and reconstruction of the natural ecosystem and the sustainable development of the social economy in disaster areas. At present, there are few studies on land cover changes before and after earthquakes in the earthquake-stricken areas, especially the quantitative assessment of land cover changes before and after earthquakes at the micro scale of landscape spatial distribution. Therefore, this article is based on remote sensing data and uses an earthquake in Jiuzhaigou, Sichuan, as an example. By calculating the land-use degree comprehensive index and its rate of change, the transfer matrix of land-use, quantitative expression methods of the landscape via the landscape pattern index are analyzed from the points of view of the spatial–temporal evolution law of the landscape pattern of land cover before and after the earthquake. The experimental results showed that the area of forest, cropland, shrub. and grassland types in the study area changed greatly, and the area of grassland types in Shuanghe Township changed most obviously, as they increased from 6.57 km2 in 2015 to 7.43 km2, and then decreased to 6.21 km2 in 2020. During the earthquake recovery period, the comprehensive index of land-use degree for most towns in the study area showed an upward trend, and the land cover types were improved. After 2017, the PD and LSI of the study area showed a downward trend, among which the bare land type changed greatly, with the PD value decreasing from 0.38 in 2017 to 0.21 in 2020, and the LSI value decreasing from 58.29 in 2017 to 40.69 in 2020. This indicates that the degree of landscape fragmentation and the spatial heterogeneity of the land surface in the study area are reduced in the later stage of earthquake recovery. After 2017, the AI value of the study area showed an upward trend, and the AI value of the bare land type increased from 63.97 in 2017 to 78.25 in 2020, indicating that the connectivity between landscape patches in the study area was enhanced in the later stage of the earthquake. This also reflects that the ecological environment of the study area gradually showed a good trend in the later stage of the earthquake. The results of this paper not only reveal the spatial–temporal evolution of land cover in Jiuzhaigou County before and after the earthquake, but also help the relevant national decision-making departments to formulate corresponding policies and measures.

Soil Loss Analysis of an Eastern Kentucky Watershed Utilizing the Universal Soil Loss Equation

Soil erosion is the displacement of soil’s upper layer(s) triggered by a variation in topography, land use and soil types, and anthropogenic activities. This study selected the Marrowbone Creek-Russel Fork watershed in eastern Kentucky to estimate the mean annual soil loss over eight years (from 2013 to 2020) utilizing the Universal Soil Loss Equation (USLE). We included monthly precipitation, soil survey, digital elevation model (DEM), and land cover data to estimate the parameters of the USLE. The mean annual soil loss for the study area ranged from 1.77 to 2.91 Mg ha−1 yr−1 with an eight-year mean of 2.31 Mg ha−1 yr−1. In addition, we observed that developed land cover classes were less erosion-resistant than undeveloped land cover classes over the observation period. The results of this case study in our small watershed that has been historically impacted by upstream coal-mining activities are comparable to the results from similar studies in other geographic regions. However, we suggest other researchers conduct similar studies using robust data to determine the applicability of the USLE model and validate the results in developing measures to address soil loss issues.

Tackling climate change impacts on biodiversity towards integrative conservation in Atlantic landscapes

Throughout the last decades, the increasing pressure of anthropogenic (climate and land-use) changes has been a major impact on biodiversity in several regions and habitat types. These drivers have disturbed the timing of reproduction in animals and plants as well as the migration of animals resulting in changes in population size and species distribution. Our study analyses the impact of climate change in the spatial distribution of selected species, between historical period (1950–2018) and future period (2041–2070), in four case studies at a watershed level over the Atlantic region, highlighting the importance of integrating landscape trends to anticipate key biodiversity pattern responses. The results were compared to predicted future climate projections (2041–2070), based on two IPCC scenarios (RCP4.5 and RCP8.5), using a 5-model ensemble developed under the EURO-CORDEX project. Further, complementary downscaling methodologies were applied, allowing the increase of the spatial resolution from ~12 km to ~1 km in all climate variables. Land cover maps were developed using the Forecasting Landscape Scenarios Model. We assessed the impact of projected climate change and land cover development on specific vulnerable species distribution for each case study. The results showed an overall temperature increase for all case studies and both representative concentration pathways scenarios and a shift in potential habitat area of species addressed to areas upstream of the catchments. These predictions have a strong importance in defining conservation strategies of these vulnerable species, and may overall bring guidelines for the management of Atlantic landscapes in response to climate change, namely as pertinent ecological indicators under realistic future changing regional scenarios. • A projected rise in temperature is observed for all case studies. • Climate change and land cover evolution has great impact in future species habitat. • Integrative land cover and climate change provide refined results of habitat area. • Species potential habitat areas tend to decrease.

Simulating thaw-induced land cover change in discontinuous permafrost landscapes

Permafrost thaw is causing a rapid evolution of the lowland discontinuous permafrost regions of the Taiga Plains in Northern Canada and elsewhere in the Northern Hemisphere. Notably, this thaw is changing the spatial distribution of the dominant hydrologic land cover types (permafrost plateaus, fens, and isolated bogs) in parts of the Northwest Territories (NWT), Canada. Here, we develop a multinomial time series land cover model (TSLCM) to simulate historical land cover transitions, model spatial patterns of transition, and predict the long term evolution of land cover in the areas surrounding the Scotty Creek Research Station (SCRS), NWT, and similar discontinuous permafrost landscapes. The machine learning-based TSLCM is informed by a set of observed spatio-temporal variables. The independent variables represent driving factors of change, and include the estimated summertime land surface temperature anomaly (LST), the distance and a custom cost distance to land cover interfaces, time increment between initial and final states, and time-accumulated temperature; the dependent variable is classified land cover maps from 1970 to 2008. First, we applied both random forest (RF) and Multinomial Log-Linear Regression (MLR) methods to train a synthetic data model; the model which improves the performance of the TSLCM in extrapolating time series change by adding new data instances to the initial data set. We boosted the initial data by combining the predicted land cover change maps from synthetic data model and the real data set. Then, we evaluated a MLR, RF, and an extreme gradient boosting (XGBoost) model in their ability to simulate land cover change.The final results of this study show that the Ensemble Learning (EL) based approaches are capable of effectively representing historical land cover change and can produce physically consistent and plausible future land cover scenarios. Deterministic predictions from the TSLCM indicate that the permafrost plateaus’ coverage will continue to decrease, with corresponding decreases in isolated bogs’ coverage and their secondary runoff contributing areas.

An International Library for Land Cover Legends: The Land Cover Legend Registry

Information on land cover is vital to numerous United Nations (UN) missions, including achieving the Sustainable Development Goals (SDGs). Because land cover data are developed by a variety of organizations for a range of objectives, they are based on different classification schemes and have discrepancies. In addition, the sustainability for land cover is hampered by limited access to information and documentation. Accordingly, international standards for land cover are developed to improve interoperability between different land cover datasets. However, the use and development of land cover datasets are limited by various factors including availability of properly documented land cover legends in support of different applications including change assessment, comparison, and international reporting. The purpose of this article is to highlight the importance of land cover in achieving several goals and to introduce the first international platform for land cover legend, named Land Cover Legend Registry (LCLR). This registry is a contribution to the international land cover community and the UN in effort to promote and support data harmonization processes and interoperability from local to global level, and vice versa. Users can not only use the registry for preparing consistent datasets, but also contribute to it by providing the latest data to ensure the long-term availability of both updated and existing datasets around the world. Moreover, building on the experience developing land cover legends with different nations, a brief explanation on the preparation of legends is also provided. Additionally, it is more important than ever to develop land cover registers to support the use, expansion, integration, and use uptake of land cover data, particularly for innovative remote sensing, machine learning, and information and communication technologies and techniques that build on existing and national contexts.

Machine Learning Approaches for Developing Land Cover Mapping.

In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.

A river ran through it: Floodplains as America's newest relict landform.

Artificial levees are a major human modification of river corridors, but we still do not have a clear understanding of how artificial levees affect floodplain extent at regional and larger scales. We estimated changes in river-floodplain connectivity due to artificial levees in the contiguous United States (CONUS) using a combination of artificial levee databases, delineations of floodplain areas, and deletion of artificial levees from topography. Our results indicate that artificial levees do not only decrease floodplain extent but also alter locations of floodplain connectivity. Anthropogenically connected and disconnected locations are similar in land cover and are predominantly, in decreasing order of extent, cultivated, wetland, forested, and developed land cover types, with more than 30% of the entire floodplain area in the CONUS cultivated or developed. This study indicates that artificial levees cause complex changes in river-floodplain connectivity and can increase flooded areas in some rivers.

Spatial evolution of land use and land cover for the Tatu-Bola Wildlife Refuge - Pernambuco, using orbital images

This article aims the analyze NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), and NDBI (Normalized Difference Built-up Index) radiometric indices for TM and OLI sensor scenes aboard the LANDSAT satellite. The study area is the Tatu-Bola Wildlife Refuge, created in March 2015 through State Decree number 41,546, and its surroundings. The study aimed to monitor the spatial and temporal evolution of land use and land cover in a period before and after the implementation of the Armadillo-Bola Wildlife Refuge, using indices of vegetation (NDVI), moisture (NDWI), and exposed soil (NDBI) and its relationship with the surroundings. TM and OLI images (orbit and point 217-066) were used on board the LANDSAT satellite 5 (23-05-1987, 15-05-1996, and 01-02-2005) and 8 (28-01-2015 and 17- 03-2021), radiometrically and geometrically corrected and obtained by accessing EarthExplorer, with the calculation of radiance and reflectance being performed, and the indices applied. In order to better identify the area with vegetation and exposed soil, the RGB composition was performed, with NDBI, NDVI, and NDWI indices for Landsat images and fusion of multispectral and panchromatic bands in WPM sensor images from the CBERS-4A satellite from 23- 08-2020 (orbit and point 198-124) and 18-09-2020 (199-124). Digital numbers of cloud and shadow pixels were excluded through supervised classification. Between 1987 and 2021, it was possible to observe a decrease in vegetation cover, especially around the RVS Armadillo-balla, and an increase in area, with NDVI values lower than 0.40. The Refuge area in Petrolina has the largest amount of deforested area. The use of RGB composition of the indices and the merged images from the CBERS-4A satellite, with a spatial resolution of 2 m, present a significant improvement in identifying areas of exposed soil and dry vegetation.

Prospective Mapping of Land Cover and Land Use in The Classified Forest of The Upper Alibori Based on Satellite Imagery

The dynamics of land cover and land use in the classified forest of the upper Alibori (FCAS) in relation to the disturbance of agro-pastoral activities is a major issue in the rational management of forest resources. The objective of this research is to simulate the evolutionary trend of land cover and land use in the FCAS by 2069 based on satellite images. Landsat images from 2009, 2014 and 2019 obtained from the earthexplorer-usgs archive were used. The methods used are diachronic mapping and spatial forecasting based on senarii. The MOLUSCE module available under QGIS remote sensing 2.18.2 is used to simulate the future evolution of land cover and land use in the FCAS. The land cover and use in the year 2069 is simulated using cellular automata based on the scenarios. The results show that natural land cover units have decreased while anthropogenic formations have increased between 2009 and 2014 and between 2014 and 2019. Under the "absence multi-criteria zoning (MZM)" scenario over a 50-year interval, land cover and use will be dominated by crop-fallow mosaics (88%). On the other hand, the scenario "implementation of a multicriteria zoning (MZE)", was issued with the aim of reversing the regressive trend of vegetation types by making a rational and sustainable management of resources.

Understanding Urban Growth through Heat Islands Using Remotely Sensed Data: Yaounde Case Study, Cameroon

Studies in urban dynamics have focused on population growth, urban sprawl and activities expansion to determine, understand and study the phenomenon of urbanization in the world. The methods used in these studies have explore remotely sense data from Global Positioning Systems (GPS), satellite imagery, aerial and ground photography for interpretation, analysis and explanation of urban land use and land cover evolution. This data is often combined with ground observations and other methods in social sciences to understand urban growth. In this article, data is tapped from temperature differences analysis in multi-date satellite images and combined with population statistics, human activities and infrastructure build up to explain the phenomenon of urbanization. The results of the findings will enrich our knowledge in Urban Geography on approaches and methods used in understanding urbanization and its problems. Drawing on case study material from Yaounde in Cameroon, this article examines how remote sensing techniques can help in understanding urban heat island in Yaounde and its negative outcomes on urban population, activity and the environment. As the study shows, urban growth has a direct relation with temperature increase and an inverse relation with vegetation change. Also, Increase in bare surfaces due to deforestation for development and settlement increases surface temperature and vice versa. Vegetation regulates surface temperature by absorbing energy from solar radiation and remission in the form of latent heat through the process of photosynthesis. Analysis of surface temperature increase through remote sensing techniques, urban land use evolution, determinants and implications of Yaounde heat islands constitute the focus of this paper. The implications of rise in surface temperature and relations with urban growth are important for decision making. This knowledge is essential for urban geography research, new methodological approaches to urbanization and policy.

State-and-Evolution Detection Models: A Framework for Continuously Monitoring Landscape Pattern Change

Detecting the evolution of large-area landscape patterns using long-term remote-sensing images is helpful in supporting research on the relationship between landscape patterns and ecological processes, as well as the development of ecological process simulations and spatiotemporal interaction models. However, detection methods have generally been developed as separate applications, each with a separate type of landscape pattern change; remote-sensing images are acquired at epochal timesteps. Consequently, in practical applications, many omission changes for some types of pattern changes and inaccurate evolution time are presented in the detected map. In this article, state-and-evolution detection models (SEDMs) are promoted to obtain complete information about the evolution of landscape patterns based on yearly land cover data. In the proposed framework, we first define the major categories of landscape pattern changes to comprehensively reveal the characteristics of landscape pattern changes associated with real change cases. Next, a morphological rule-based pattern recognition approach is proposed for quantitative discrimination among these categories. This approach is then applied in annual land cover data to continuously detect landscape pattern evolution processes and evolution time. Finally, the detected evolution time in different evolution processes is applied to measure the timestep between two disparate types. The performances of the SEDMs are presented by Landsat-derived land cover evolution in Shanxi, China. The detected results are indirectly verified by the land cover conversion matrix and connect index, indicating strong robustness and generalization ability of the SEDMs.