Land Use And Land Cover Types Research Articles

Impacts of the Tropical Cyclone Idai in Mozambique: A Multi-Temporal Landsat Satellite Imagery Analysis

The Central Region of Mozambique (Sofala Province) bordering on the active cyclone area of the southwestern Indian Ocean has been particularly affected by climate hazards. The Cyclone Idai, which hit the region in March 2019 with strong winds causing extensive flooding and a massive loss of life, was the strongest recorded tropical cyclone in the Southern Hemisphere. The aim of this study was to use pre- and post-cyclone Idai Landsat satellite images to analyze temporal changes in Land Use and Land Cover (LULC) across the Sofala Province. Specifically, we aimed—(i) to quantify and map the changes in LULC between 2012 and 2019; (ii) to investigate the correlation between the distance to Idai’s trajectory and the degree of vegetation damage, and (iii) to determine the damage caused by Idai on different LULC. We used Landsat 7 and 8 images (with 30 m resolution) taken during the month of April for the 8-year period. The April Average Normalized Difference Vegetation Index (NDVI) over the aforementioned period (2012–2018, pre-cyclone) was compared with the values of April 2019 (post-cyclone). The results showed a decreasing trend of the productivity (NDVI 0.5 to 0.8) and an abrupt decrease after the cyclone. The most devastated land use classes were dense vegetation (decreased by 59%), followed by wetland vegetation (−57%) and shrub land (−56%). The least damaged areas were barren land (−23%), barren vegetation (−27%), and grassland and dambos (−27%). The Northeastern, Central and Southern regions of Sofala were the most devastated areas. The Pearson Correlation Coefficient between the relative vegetation change activity after Idai (NDVI%) and the distance to Idai’s trajectory was 0.95 (R-square 0.91), suggesting a strong positive linear correlation. Our study also indicated that the LULC type (vegetation physiognomy) might have influenced the degree of LULC damage. This study provides new insights for the management and conservation of natural habitats threatened by climate hazards and human factors and might accelerate ongoing recovery processes in the Sofala Province.

Export of dissolved iron from river catchments in northeast Japan

This study investigated the export of dissolved iron (DFe) from river catchments with emphasis on land use and land cover (LULC). The DFe concentrations were measured at 61 and 63 sites during summer and winter, respectively, in the Kitakami Mountain in northeast Japan. LULC was classified as broadleaf forest, conifer, grassland, paddy field, cropland, and urban area using GRASS GIS software. In the downstream areas, the riverine DFe concentrations ranged from 0.84 to 115 μg/L and from 1.5 to 103 μg/L in winter and summer, respectively, whereas the DFe in the upstream areas ranged from 0.16 to 7.6 μg/L and from 1.1 to 20.0 μg/L. Regression analysis was applied to the DFe and LULC data, and the unit load for DFe was estimated for each LULC type, the unit load values being 1.6, 139, and 439 g/km2/day for broadleaf forest, paddy fields, and urban area, respectively, in winter and 61.9, 564, and 727 g/km2/day for grassland, paddy fields, and urban area, respectively, in summer, reflecting the impact of distinct seasonality. A significant positive correlation was found between urban area and riverine DFe concentration in each season, emphasizing the influence of human activity on DFe export to river catchments. Overall, our investigation provides a series of quantitative evidence showing the importance of LULC to understand DFe export from river catchments.

EFFECTS OF LAND USE AND LAND COVER CHANGES ON SOIL EROSION IN SEMI-ARID REGIONS OF TURKEY; A CASE STUDY IN ALMUS LAKE WATERSHED

Land use and land cover (LULC) changes, which are the major causes of soil erosion in watersheds are important processes of land degradation in the world. Erosion models are useful to investigate temporal changes in the past and estimate the future soil losses that might threat the sustainability of crop production. In this study, temporal changes (between 1990 and 2018) of soil loss under different LULC were investigated in a small watershed located in Almus, Turkey. Universal soil loss equation (USLE) was used to model soil loss in the watershed at a fine spatial resolution using geographic information techniques. Rainfall (R), soil erodibility (K), slope length and steepness (LS) and supporting and conservation practices (P) factors of USLE model were kept constant, while cover and management factor (C) was determined by based on LULC types in 1990 and 2018. Spatial and temporal changes in erosion risk were mapped. The maps obtained indicated that LULC change has have both reducing and increasing effects on erosion risk. The average soil loss in 1990 decreased from 0.312 to 0.308 t ha-1 year-1 in 2018. Coverage of low-risk class areas in the basin increased by 20.19 km2 between 1990 and 2018. The increase in mixed forest cover caused a significant decrease in erosion risk. The results demonstrated that LULC is the driving factor for increasing or decreasing in soil erosion at the watershed. Conversion of forests to agricultural lands disturbed the surface cover and increased the erosion risk. The results revealed the significant impacts of LULC changes on soil erosion potential in the Almus Lake Watershed.

Time series analysis of land use and land cover changes related to urban heat island intensity: Case of Bangkok Metropolitan Area in Thailand

This study focuses on time series analysis of land use and land cover (LULC) characteristics and its relationships with urban heat island (UHI) intensity in Bangkok Metropolitan area. The study is aimed at examining impact of LULC changes on the greenness and land surface temperature (LST) as well as the relationship of LST, LULC and Normalized difference vegetation index (NDVI). We consider the heat intensity, hot spot zones, and land use characteristics related hotspot areas. LULC classification based on satellite images taken by Landsat data in summer dates of 1991, 1997, 2005 and 2016, were classified into four LULC types: built up area; vegetation area; barren land; and surface water. UHI was studied by deriving LST based satellite images method using the thermal band of Landsat satellite in the same periods as of LULC classification. The results show that the built up area was approximately 30% in 1991, then, it was sharply increasing to approximately 55% of the total area in 2016. The changing trends of LST tend to be increasing while the value of NDVI with dense vegetation meaning tend to be declining over the study period. The significant negative correlation between LST and NDVI implied that the lower biomass can higher LST. UHI intensity was also continually rising from 11.91 °C in 1991 to 16.21 °C in 2016 leading to nearly 5 °C increase in Bangkok. High density of buildings, no vegetation areas and large area of concrete pavement are the most significant related to the hot spot areas.

Evaluation of the Biodiversity Conservation Function in Liaohe Delta Wetland, Northeastern China

The scientific evaluation of the wetland biodiversity conservation function is the basis of balanced wetland protection and development. Our research sought to provide references for the protection of wetland ecological environments as well as the related planning and management policies. The study established a fitting model for evaluating the biodiversity conservation function in the Liaohe Delta, northeastern China. The new model, the Wetland Biodiversity Conservation Indicator (WBCI), was with four input factors, including the vegetation coverage (VC), habitat suitability index (HI), land use and land cover (LULC) index (LI), and threat factor index (TI) of the LULC type. The values assigned to HI and TI were based on Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) habitat quality models. The weights of all the factors in WBCI were valued with the Principal Component Analysis (PCA). We evaluated the wetland biodiversity conservation function of Panjin, Liaohe Delta, China, by using the WBCI model based on Gaofen-1 (GF-1) satellite data in 2018, and the result was verified with InVEST and other models. It showed that the output map was similar to that of InVEST, with the higher-quality habitat including the wetland, tidal flat, water body, and forest, as well as the lower-quality land use types including the paddy field, crop field, construction land, and land used by traffic. The wetland biodiversity conservation function was better in areas less affected by human disturbance, with very abundant species and good-quality habitat. It was poor in areas impacted by more frequent human activities such as the land cultivation, housing, and traffic, which led to the landscape fragmentation. The WBCI model provided a more accurate reflection of the bird distribution than the InVEST model. The WBCI model was able to reflect the difference in quality of each habitat grade, in contrast to the net primary productivity (NPP) method and species distribution models (SDMs). The new model was, therefore, simpler and suitable in reflecting the quality of wetland biodiversity function in the Liaohe Delta.

Spatial Patterns of LULC and Driving Forces in the Transnational Area of Tumen River: A Comparative Analysis of the Sub-regions of China, the DPRK, and Russia

Understanding the spatial patterns of land-use and land-cover (LULC) and their driving forces in transnational areas is important for the sustainable development of these regions. However, the spatial patterns of LULC and their driving forces across multiple scales are poorly understood in transnational areas. In this study, we analyzed the spatial patterns of LULC and driving forces in the transnational area of Tumen River (TATR) in 2016 across two scales: the entire region and the sub-regions of China, the Democratic People’s Republic of Korea (DPRK), and Russia. Results showed that the LULC was dominated by broadleaf forest and dry farmland in the TATR in 2016, which accounted for 66.86% and 13.60% of the entire region, respectively. Meanwhile, the LULC in the three sub-regions exhibited noticeable differences. In the Chinese and the DPRK’s sub-regions, the area of broadleaf forest was greater than those for the other LULC types, while the Russian sub-region was dominated by broadleaf forest and grassland. The spatial patterns of LULC were mainly influenced by topography, climate, soil properties, and human activities. In addition, the driving forces of the spatial patterns of LULC in the TATR had an obvious scaling effect. Therefore, we suggest that effective policies and regulations with cooperation among China, the DPRK, and Russia are needed to plan the spatial patterns of LULC and improve the sustainable development of the TATR.

Land use/land cover assessment as related to soil and irrigation water salinity over an oasis in arid environment

AbstractThe land use and land cover (LULC) changes and the implications of soil and irrigation water salinity have adverse effects on crop production and the ecosystems of arid and semiarid regions. In this study, an attempt has been made to analyze and monitor the LULC changes using multitemporal Landsat data for years 1986, 1998, 2007, and 2016 in Al-Ahsa Oasis, Saudi Arabia. In addition, efforts were made to measure the spatial distribution of soil and irrigation water salinity along the oasis. The supervised maximum likelihood classification method was applied to classify the individual images independently. Moreover, soil samples were collected at surface soil depth from the selected LULC types, namely, date palm, croplands, and bare land. Also, groundwater samples were collected from bore wells located in agricultural farms. The spatial distribution of the soil salinity (Ece) and irrigation water salinity (ECiw) was classified based on the Food and Agriculture Organization guidelines. The results showed that significant changes in LULC patterns have occurred during 1986–2016 in the study area. The ECewas found higher in date palm compared with cropland and bare land. However, the spatial distribution of the ECiwover the oasis indicated that 94% of irrigation water ranged between moderate and severe salinity risk. The study concludes that salinity management practices need to be developed in the study area aiming to sustain crop yields, improve soil properties, and minimize the environmental impacts of LULC changes on the ecosystem of Al-Ahsa Oasis.

Integrating Maxent model and landscape ecology theory for studying spatiotemporal dynamics of habitat: Suggestions for conservation of endangered Red-crowned crane

Abstract Numerous studies have been conducted on current distribution and future changes of habitats by researchers from a range of disciplines such as ecology, biology, geography, environmental science, and agricultural science, among others. However, there is a lack of detailed studies on historical spatiotemporal dynamics of habitat, which can provide useful information for the conservation and management of endangered species habitats. Thus, we proposed an integrated framework to assess historical changes of habitat across space and time, as well as to analyze the driving mechanism based on Maxent model and landscape theory. Here, we collected Red-crowned crane records and environmental variables including climate, elevation, wind farm disturbance, land use and land cover (LULC) type and disturbance, and road disturbance at regional scale from 1984 to 2017. Our results suggested that there was a dramatic decline in habitat area, habitat suitability and habitat connectivity. LULC disturbance was the major driving factor leading to the decline, followed by LULC type and wind farm disturbance. Importantly, our results quantified LULC disturbance threshold, LULC type suitability, and range of wind farm impact, which served to propose specific recommendations for habitat restoration. Our approach highlighted the importance of integrating species distribution models and landscape ecology theory for conservation, hence was widely applicable to endangered species in the context of increasing human activities.

Dynamics of Land Use and Land Cover Changes in An Arid Piedmont Plain in the Middle Reaches of the Kaxgar River Basin, Xinjiang, China

The Kaxgar River Basin, a key section of the Tarim River Basin, is a typical ecologically fragile region that has undergone rapid changes to its spatial patterns over the preceding few decades. In particular, the expansion of salinized land has posed a severe threat to ecological restoration and economic development. This study monitored the rates and patterns of land use and land cover (LULC) changes in the plain area of Aketao County in the middle reaches of the Kaxgar River Basin. Five Landsat images (captured in 1990, 1998, 2002, 2013, and 2018) were divided into seven LULC types: built-up land, cultivated land, woodland and grassland, light-moderate salinized land, heavy salinized land, water areas, and bare land. Subsequently, their dynamic processes were analyzed. The results revealed that in 1990, the dominant LULCs were cultivated land, woodland and grassland, and bare land. Throughout the study period (from 1990 to 2018), the coverage of built-up land, cultivated land, bare land, water areas, and light-moderate salinized land increased; by contrast, that of the other LULC types decreased. The most marked LULC changes were the expansion of light-moderate salinized land (by 6.2% of the study area) and the shrinkage of woodland and grassland (by 9.4% of the study area). Almost all the analyzed LULC types underwent conversion to other types; such conversion occurred most frequently between 1998 and 2018. The conversions of woodland and grassland into cultivated land and light-moderate salinized land were the most notable phenomena. Another highly evident change was the conversion of heavy salinized land into bare land. These results revealed that the expansion of salinized land and the shrinkage of woodland and grassland in the study area were the most severe environmental changes. Therefore, ecological protection and salinization control are urgently required to enable local economic development while not exceeding the environmental carrying capacity and ensuring the safety of the “green corridor” in the lower reaches of the Kaxgar River Basin.

Impacts of spatial heterogeneity patterns on long-term trends of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature time series

Land surface temperature (LST) is a crucial parameter for global climate change studies. LST changes are also directly associated with the large-scale changes in land cover. Previous studies carried out a comparative analysis of satellite-derived LST response between periods before and after homogenous land cover changes. We present an alternative approach that quantifies long-term LST variability in response to various land use/land cover change (LULCC) patterns over Phuket Island, Thailand, from 2003 to 2017. First, four Moderate Resolution Imaging Spectroradiometer (MODIS) overpass times of LST time series were adjusted for seasonal effects using a cubic spline function to preserve the number of original data and enable estimates of LST dynamics and trends using the generalized least squared models. Second, LULCC patterns were classified according to land cover type conversion and spatial pattern transformations between the years 2000 and 2016. Spatial homogeneity and heterogeneity were quantified by the coverage percentage for each land use and land cover (LULC) type within a given location. Finally, the influence of LULCC patterns on the long-term spatiotemporal behavior of LST was assessed using the generalized estimating equation model. Results showed that different land cover transitions influence the dynamics of daytime LST but not the nighttime LST. The proportion of different land cover types within an LST pixel and transition amounts contributed to the quantity of increasing surface temperature, especially over impervious surface types. Diverse LULCC patterns with considerations of spatial heterogeneity improved our insight about a relatively strong effect of combined LULC types on LST responses. The climatic effect through the gradual conversion of heterogeneous land cover is necessary to be considered in climate research studies.

Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil.

In the last 30years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (- 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. Graphical abstract.

Land Use and Land Cover Change Within and Around the Greater Serengeti Ecosystem, Tanzania

Land use and land cover (LULC) changes can pose profound impacts on wildlife habitats, abundance and distribution and on human-dominated landscapes. We investigated LULC changes in the Greater Serengeti ecosystem, Tanzania, for a period of 41 years from 1975 to 2015. Specifically, we mapped LULC types for 1975, 1995 and 2015 and assessed the corresponding changes during 1975-1995, 1995-2015 and 1975-2015. We used the random forest classification algorithm to classify Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (+ETM) and Operational Land Imager (OLI) into eight main classes. We obtained accuracies of 88.4%, 90.6% and 93.4% with Kappa Indices of Agreement (KIA) of 0.86, 0.87 and 0.91 for 1975, 1995 and 2015, respectively. Grassland, shrubland and woodland were the major LULC types throughout 1975-2015 with percentage coverages of 50.6%, 23.7% and 20.9% for 1975; 54.2%, 23.5% and 15.9% for 1995; and 57.0%, 23.8% and 8.9% for 2015, respectively. Overall, woodland cover (-11.1%) was converted to most of the other cover types during 1975-2015. The loss of woodland cover is due to increasing human population size, agriculture, settlements and policy changes fires and elephant browsing. Effective conservation policies and regulation of socio-economic activities in the ecosystem and its buffer area are essential to ameliorate declining vegetation cover, especially along the protected areas boundaries.

Multi-Layer Perceptron Neural Network and Markov Chain Based Geospatial Analysis of Land Use and Land Cover Change

We combined multi-layer perceptron (MLP) neural network and Markov Chain (MC) modeling with object-based image analysis (OBIA) to map and predict land use and land cover (LULC) changes in Stoney Creek Watershed (SCW), British Columbia, Canada. Unsupervised classification was performed using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to produce LULC maps of years 1986, 1999 and 2016. The classification resulted in an overall accuracy of 91.50%. The results show that coniferous forest in SCW experienced a sharp loss while agriculture area increased (4.77% land gain) from 1986 to 2016. LULC scenarios were predicted through MLP neural network and MC modeling based on LULC change analysis data and transition potential. The results indicated that ‘Coniferous Forest’ LULC type had the highest (3.38% land loss) transition potential and ‘Water’ and ‘Urban Area’ LULC types had the lowest transition potential. Application of the proposed method provided valuable information of LULC patterns and dynamics for planners and researchers. The method also has the potential for improved management in other watersheds with similar LULC types.

Spatiotemporal variability in spate irrigation systems in Khirthar National Range, Sindh, Pakistan (case study)

Satellite remote sensing and geographical information system (GIS) have been used successfully to monitor and assess the land use and land cover (LULC) dynamics and their impacts on people and the environment. LULC change detection is essential for studying spatiotemporal conditions and for proposing better future planning and development options. The current research analyzes the detection of spatiotemporal variability of spate irrigation systems using remote sensing and GIS in the Khirthar National Range, Sindh Province of Pakistan. We use Landsat images to study the dynamics of LULC using ArcGIS software and categorize five major LULC types. We obtain secondary data related to precipitation and crop yield from the provincial department of revenue. The maximum likelihood supervised classification (MLSC) procedure, augmented with secondary data, reveals a significant increase of 86.25% in settlements, 83.85% in spate irrigation systems, and 65% in vegetation, and a substantial negative trend of 39.50% in water bodies and 20% in barren land during the period from 2013 to 2018. Our study highlights an increase in settlements due to the inflow of local population for better means of living and an increase in spate irrigation systems, which indicates the water conservation practices for land cultivation and human purpose lead to the shrinkage of water bodies. The confusion matrix using Google Earth data to rectify modeled (classified) data, which showed an overall accuracy of 82.8%–92%, and the Kappa coefficient estimated at 0.80–0.90 shows the satisfactory results of the LULC classification. The study suggests the need to increase water storage potential with the appropriate water conservation techniques to enhance the spate irrigation system in the hilly tracts for sustainable developments, which mitigates drought impact and reduces migration rate by providing more opportunities through agricultural activities in the study area.

Spatial modelling of groundwater quality across a land use and land cover gradient in Limpopo Province, South Africa

The spatial variability of groundwater quality in rural Mokopane District is investigated. This was achieved by evaluating the variability in groundwater physio-chemical parameters in relation to Land Use and Land Cover (LULC)using Kriging geo-statistical technique and key groundwater physio-chemical parameters namely; ammonia, lead, Total Dissolved Solids (TDS), nitrates and chloride. Firstly, we derived the LULC from 30-m Landsat 8-OLI satellite data, with an overall accuracy of 76.67%. Water quality parameters were modelled in geographic information systems using the kriging interpolation technique. We then compared measured water quality and the drinking standards recommended by the South African Water Quality Standards (SAWS) and the World Health Organization (WHO) guideline standards for drinking water. Results indicated that there were significant differences (p < 0.05) in the levels of physio-chemical contents between borehole water and standard values according to the SAWS and WHO. For example, TDS in Mosesetjane exceeded both SAWS and WHO drinking water levels. Nitrate concentrations were also high on the first and second quarter with concentration levels of 5.56–29 mg/l and 30.9 mg/l in Mosesetjane, respectively. However, lead and ammonia were found to be evenly distributed in all the four quarters, but exhibited some slight deviations from the standard levels (±4.7 mg/l) in selected quarters. Results also indicated significant differences in TDS, chloride and nitrate concentrations across different LULC types. Subsistence farming and built-up areas were the main LULC types that were found to have high concentrations of TDS, ammonia and nitrates, exceeding the recommended standards. However, lead and ammonia, did not differ significantly (p > 0.05) and these parameters were fairly distributed, with less spatial variability . The second quarter, extending between October and December, and the fourth quarter, extending from April to June, exhibited high concentrations of nitrates, chloride and TDS during the wet season. On the other hand, the first and third quarter of the dry period were associated with low concentrations. This study indicated that LULC types and seasonal variability have influence on specific groundwater quality parameters.

Monitoring changes in land use and distribution of mangroves in the southeastern part of the Mekong River Delta, Vietnam

Mangroves are one of the most valuable and productive coastal ecosystems. Previous studies show a severe loss of mangroves around the world over the last several decades due to natural and anthropogenic activities. Mangroves located in the southeastern part of the Mekong River Delta (MRD) are also affected by these activities. Shrimp farming is considered as one of the main drivers causing the rapid loss and degradation of mangroves. The goal of this study is to assess the spatiotemporal changes in land use and distribution of mangroves in the Soc Trang and Bac Lieu provinces of the southeastern part of the MRD. Multi-temporal Landsat data were used for land use and land cover (LULC) classification using the maximum-likelihood classification algorithm. The changes in mangrove forest areas were monitored using medium spatial resolution (Landsat-5 Thematic Mapper and Landsat-8 Operational Land Imager) satellite imageries from 1988 to 2018. In the study area, there were seven major LULC types namely, dense mangroves, sparse mangroves, aquaculture farms, arable land with crop cover, arable land without crop cover, settlements, and water bodies. The overall accuracies of the LULC maps in 1988, 1998, 2008, and 2018 were 81.2%, 83.3%, 78.3%, and 81.9%, respectively. This study reveals that dense and sparse mangrove forests have decreased by 90% from 5495 hectares (ha) to 515 ha and by 55% from 14,105 to 6289 ha, respectively from 1988 to 2018. On the other hand, the aquaculture farm has increased at the rate of 5024 ha/year for a period of 30 years. This rapid growth of aquaculture farming activities caused the rapid loss and degradation of mangroves in the MRD. Quantitative information about mangrove change obtained by this study is considered to be useful for future coastal management and relevant policies in the MRD.

Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data

Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.

Land-Cover Dependent Relationships between Fire and Soil Moisture

For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

Characterization of Combined Effects of Urban Built-Up and Vegetated Areas on Long-Term Urban Heat Islands in Beijing

With the development of urbanization and industrialization, megacities have experienced more severe surface urban heat island (SUHI) effects. Land surface temperatures (LSTs) are retrieved; spatial distribution of temperature is characterized, and the relationship among temperatures or SUHIs and land-use and land cover (LULC) in Beijing City are discussed. The changing LSTs in Beijing, from 1990 to 2017, were calculated by a radiative transfer equation and mono-window algorithm. To estimate the effect of SUHI, Landsat-8 Thermal Infrared Sensor (TRIS) and Landsat-5 Thematic Mapper (TM) data were selected. There is an increasing trend toward high LSTs for different LULC types. The connection with building and vegetation density is analyzed. Results indicate that for every 1% increase in the density of buildings, the increase in amplitude of temperature in 2017 was twice as large as it was in 1995 for the study area. In terms of normalized difference vegetation index (NDVI) values, the decrease in amplitude of LST was 10 times that of the year 1995, where there is only a slight increase in the NDVI values of the area.

Multi-scale relationship between land use/land cover types and water quality in different pollution source areas in Fuxian Lake Basin.

The spatial-temporal evolution of land use and land cover (LULC) and its multi-scale impact on the water environment is becoming highly significant in the LULC research field. The current research results show that the more significant scale impact on LULC and water quality in the whole basin and the riparian buffer scale is unclear. A consensus has not been reached about the optimal spatial scale problem in the relationship between the LULC and water quality. The typical lake basin of the Fuxian Lake watershed was used as the research area and the scale relationship between the LULC and water quality was taken as the research object. High resolution remote sensing images, archival resources of surveying, mapping and geographic information, and the monitoring data of water quality were utilized as the main data sources. Remote sensing and Geometric Information Technology were applied. A multi-scale object random forest algorithm (MSORF) was used to raise the classification accuracy of the high resolution remote sensing images from 2005 to 2017 in the basin and the multi-scale relationship between the two was discussed using the Pearson correlation analysis method. From 2005 to 2017, the water quality indicators (Chemical Oxygen Demand (COD), Total Phosphorous (TP), Total Nitrogen (TN)) of nine rivers in the lake’s basin and the Fuxian Lake center were used as response variables and the LULC type in the basin was interpreted as the explanation variable. The stepwise selection method was used to establish a relationship model for the water quality of the water entering the lake and the significance of the LULC type was established at p < 0.05.The results show that in the seven spatial scales, including the whole watershed, sub-basin, and the riparian buffer zone (100 m, 300 m, 500 m, 700 m, and 1,000 m): (1) whether it is in the whole basin or buffer zone of different pollution source areas, impervious surface area (ISA), or other land and is positively correlated with the water quality and promotes it; (2) forestry and grass cover is another important factor and is negatively correlated with water quality; (3) cropping land is not a major factor explaining the decline in water quality; (4) the 300 m buffer zone of the river is the strongest spatial scale for the LULC type to affect the Chemical Oxygen Demand (COD). Reasonable planning for the proportion of land types in the riparian zone and control over the development of urban land in the river basin is necessary for the improvement of the urban river water quality. Some studies have found that the relationship between LULC and water quality in the 100 m buffer zone is more significant than the whole basin scale. While our study is consistent with the results of research conducted by relevant scholars in Aibi Lake in Xinjiang, and Erhai and Fuxian Lakes in Yunnan. Thus, it may be inferred that for the plateau lake basin, the 300 m riparian buffer is the strongest spatial scale for the LULC type to affect COD.