Land Cover Research Articles

Groundwater potential zone mapping using AHP and geospatial techniques in the upper Narmada basin, central India

Water scarcity occurs in the agriculturally dominated Upper catchment area of Narmada River, Central India because of the overexploitation of underground water for residential, industrial, and other uses. Delineating the Ground Water Potential Zone (GWPZ) is critical to meeting the area’s water demand. Finding the Upper catchment Narmada River groundwater potential zone is the primary goal of this study. The study uses geographical methodologies based on the Analytical Hierarchical Process (AHP). To create a GWPZ map, ArcGIS 10.4 software compiles eight thematic layers, including elevation, slope, drainage density, geology, rainfall, soil texture, modified normalized difference water index, topographic wetness index, and land use/cover. There are five classifications for land use land cover map: Very low, low, moderate, high, and Very high. Each theme map in this study was given a weight based on its unique attributes and contribution to the GWP capacity. The AHP method, which takes into account each layer’s relative relevance regarding the others, was used to establish the weights. Four groups were created from the resulting groundwater potential map: excellent, good, moderate, and poor. According to the study, 26.05% of the basin was categorized as excellent, 34.59% as good, 23.97% as moderate, and 15.4% as poor groundwater potential.The results of this study further indicate that a sizable section of the Narmada River Basin has well to moderate groundwater potential, pointing to encouraging prospects for the area’s sustainable groundwater use. The study offers crucial insights for planners and policymakers to conscientiously harness groundwater resources, fostering sustainable development across diverse land uses in the fragile zone of the upper catchment of the Narmada, and it serves as a model for simulation in other sensitive river basins. The implications of the study are geared towards enhancing groundwater prospects, revitalizing fragile riverine ecosystems, and achieving the target outlined in Goal 6 of the Sustainable Development Goals (SDGs) of 2030.

Forest ecosystem services of water-related filtration and regulation, a multi-source assessment and economic valuation in Mangla watershed

ABSTRACT Watershed forests offer crucial ecosystem services for hydrological systems. This study provides comprehensive insights into sediment retention within the Mangla watershed. We analyzed spatial layers from Sentinel-2 imagery, land use data, weather, soil properties, and terrain characteristics to identify soil erosion and sediment delivery hotspots. Using the Integrated Valuation of Environmental Services and Tradeoffs-sediment delivery ratio (InVEST-SDR) model, we quantified sediment exported to the downstream Mangla Dam. The InVEST-sediment retention (InVEST-SR) model estimated the sediment retention capacity of various land cover types. Our findings show that forests retained 20,300.70 tons of sediment in 2017 and 29,887.60 tons in 2021, making them the primary source of sediment retention. The economic value of this ecosystem service was approximately USD 2.59 trillion for 973,168.51 ha of forest in 2017 and USD 4.02 trillion for 1,023,927 ha in 2021. Understanding the dynamics and economic value of this forest ecosystem service at a large watershed scale provides essential insights for decision-making and conservation efforts aimed at protecting water resources and ecological integrity.

Variation in species-specific responses to habitat fragmentation and land cover structure in urban small mammal communities

Abstract Urbanization is a key driver of habitat loss and fragmentation worldwide, yet many urban ecosystems contain vegetated habitat patches that support diverse wildlife communities. Managing urban systems to support robust wildlife communities requires us to understand the mechanisms that drive the response of species to the urban environment. Small mammals are key components of terrestrial ecosystems (e.g., seed predators, prey) and likely also carry out these roles in urban ecosystems; however, the effects of urbanization on small mammal communities are understudied. To identify how species-specific responses to urban environments shape community composition, we quantified both overall community and species-specific changes in small mammal abundance across an urbanization gradient in a Midwestern US metropolitan area. We combined small mammal trapping and land cover data to generate a hierarchical community abundance model. Species diversity increased with increasing proportional cover of human-modified land cover (i.e., impervious surfaces and turfgrass). This finding is driven by high species diversity on sites bordering streams in mowed parks and low diversity on sites with high tree canopy closure. Additionally, modeling results indicated that species responded differently to landscape attributes, leading to variation in small mammal community composition across the urbanization gradient: prairie-associated species tended to be more abundant in tall vegetation bordering mowed parks while habitat generalists tended to be more abundant on sites with greater canopy closure and shrub cover. Our results suggest that studies that focus on community-level responses (e.g., species richness) to urbanization may miss important species-specific responses. It may be particularly important to assess both species-specific and community-level responses in cities at ecotones (e.g., between forest and grassland) where species with different habitat requirements may replace one another in different types of green spaces, thereby changing community composition without affecting species diversity or richness. Our findings also indicate that vegetated urban patches, especially patches with tall vegetation cover and low canopy cover, are important habitat for prairie-associated small mammal communities, providing conservation options in heavily altered landscapes.

Impacts of Light Exposure and Soil Covering on Sweet Potato Storage Roots in a Novel Soilless Culture System

Soilless culture systems, which promote plant growth and enable the precise control of the root-zone environment, have yet to be fully established for sweet potatoes. In this study, we developed a soilless culture system and examined the effects of soil covering and light exposure on the storage roots of sweet potatoes. Sweet potato seedlings with induced storage roots were transplanted into five systems: a previously developed pot-based hydroponics system (Pot), an improved version with storage roots enclosed in a plastic box and covered with a soil sheet (SS), the SS system without the soil sheet (SD), the SD system with light exposure to storage roots after 54 days (SL), and a deep flow technique (DFT) hydroponics system. Our study enabled the time-course observation of storage root enlargement in the SS, SD, and SL systems. In the SL system, light exposure suppressed the storage root enlargement and reduced epidermal redness. No storage root enlargement was observed in the DFT system, even at 151 days after transplantation. Light exposure in the SL system increased the chlorophyll and total phenolic contents in the cortex beneath the epidermis, while the starch content was the lowest in this system. These findings indicate that the developed system can induce normal storage root enlargement without soil. Additionally, the observed changes in growth and composition due to light exposure suggest that this system is effective for controlling the root-zone environment of sweet potatoes.

Morphological Characterization-based Optimal Trait Selection for Improving Yield and Stability of Soybean (Glycine max L. Merrill)

The present investigation was convened during kharif, 2022 (July–October), rabi-Summer 2023 (January–May) and kharif, 2023 (July–October) at J.N.K.V.V., Jabalpur, Madhya Pradesh, India to characterize 165 diverse soybean germplasm lines in pursuance of DUS (Distinctness, Uniformity, and Stability) guidelines focusing on integrating optimal morphological traits to pre-fine soybean crop improvement. The study revealed significant variation among soybean genotypes. The hypocotyl color, controlled by a monogenic trait, was found in 77 genotypes, closely associated with violet flower color, whereas non-pigmented hypocotyls present in 88 genotypes were linked to white flowers. Growth habits were categorized as determinate (59), semi-determinate (103), and indeterminate (3), demonstrating that semi-determinate genotypes offer balanced resource allocation and reduced lodging. Most genotypes displayed medium flowering time (159) and medium plant height (134). Leaf shape was predominantly pointed ovate (121), with lanceolate leaves associated with higher number of seeds-1 pod. The presence of dark green leaves in 108 genotypes indicated higher chlorophyll content and enhanced photosynthetic efficiency to strengthen plant type. Semi-erect growth habit was prevalent in 152 genotypes, providing better soil coverage and moisture retention, while 13 genotypes showed erect growth. Pod pubescence was observed in 106 genotypes, offering protection against pests. These traits are found stable across the environment and may be used as an identification key for different varieties and donor germplasm lines.

Ecological effects of land use and land cover change in the typical ecological functional zones of Egypt

Evaluation the value of ecosystem services can provide crucial reference for formulating ecological protection plans and making informed management decision. This study utilized Landsat remote sensing images as the data source and employed land-use transfer chains and land use transfer matrix methods to compare the ecological effects of land use and land cover change(LUCC) in the two typical ecological functional zones of Egypt, one area isthe northern region of Egypt with the Priority function of Economic Development, another area is the Nasser Reservoir area with the priority function of water conservation. The key findings are as follows: (1)The northern region of Egypt and the Nasser Reservoir exhibited different trends across the ecological functional zones.From 1985 to 2020, the northern region of Egypt is characterized by an increase in arable land and impervious surface,the Nasser Reservoir area is distinguished by the expansion of water and a reduction in sand.(2) Key land transfer patterns vary significantly between the ecological functional zones of the northern region of Egypt and the Nasser Reservoir. From 1985 to 2020, the Egypt’s northern region experienced significant shifts between arable land, sand, and impervious surfaces. The conversion of arable land to impervious surfaces represented 2.41% of the total arable land transfer area, while sand converted to impervious surfaces accounted for 11.68% of the total sand transfer area. In the Nasser Reservoir region, significant conversions occurred between water, grasslands, and sand. Sand converted to water made up 3.83% of the total sand transfer area, while grassland converted to water compriseed 5.38% of the total grassland transfer area. (3) The ecological benefits of the northern region of Egypt and the Nasser Reservoir have increased significantly. In northern region of Egypt, the primary drivers of this increase in ecological service value are the conversion of arable land and grasslands, along with the ecological development and utilization of sand. In the Nasser Reservoir area,the inflow of water and the continued outflow of sands are the main factors contributing to the rise in ecosystem service value. (4) Differentiated LUCC management should be implemented for different ecological functional zones. In northern region of Egypt, the focus should be on balancing ecological security with human development, while the Nasser Reservoir area should prioritize water resource protection and sustainable development. The research findings are not only significant for optimizing LUCC but also for enhancing ecosystem service functions in Egypt.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Projected trends in ecosystem service valuation in response to land use land cover dynamics in Kishtwar High Altitude National Park, India

Projected trends in ecosystem service valuation in response to land use land cover dynamics in Kishtwar High Altitude National Park, India

Introducing a new index for flood mapping using Sentinel-2 imagery (SFMI)

Accurate surface water detection and mapping using Remote Sensing (RS) imagery is crucial for effective water and flood management and for supporting natural ecosystems and human development. In recent years, RS technology and satellite image processing have significantly advanced in flood and permanent water extraction, particularly in water index, clustering, classification, and sub-pixel analysis. Water-index-based techniques, distinguished by their quickness and convenience, offer notable advantages. The dynamic and extensive nature of surface water and flooded areas make the water index particularly effective for monitoring large areas. However, challenges arise due to the complexity of ground surfaces in aquatic environments, including shadows in built-up, vegetated, and mountainous regions, narrow water bodies, and muddy water. This research presents a new Flood Mapping Index using Sentinel-2 imagery (SFMI) designed to address these challenges and identify water and flooded areas more accurately. The SFMI utilizes visible and near-infrared bands derived from Sentinel-2 data, employing 10-m bands to compensate for errors arising from spectral and spatial changes more effectively. The SFMI index is designed based on the spectral signatures of various land cover classes, utilizing the potential of 10-m resolution bands to identify water bodies and flood areas. Unlike the most conventional methods, the SFMI identifies and extracts water and flood regions without complex thresholding, and thus mitigates the impact of irrelevant features, such as dense vegetation and rugged topography on the flood and water body maps. The proposed index was tested in two large areas with high spectral diversity, yielding promising results. The SFMI index demonstrates an average overall accuracy of 97.1% for pre-flood water extraction, 97.95% for post-flood water extraction, and 98% for flooded area extraction. Moreover, the results showed an average kappa coefficient of 0.958 for pre-flood water extraction, 0.965 for post-flood water extraction, and 0.978 for flooded area extraction. The performance of the SFMI index for extracting flooded areas (ΔSFMI) is superior to its performance for water extraction both before and after the flood. However, it is essential to note that the accuracy of the flooded area map is contingent on the accuracy of the water area map both before and after the flood. Thus, the SFMI index based on 10-m Sentinel-2 imagery accurately detects floods and water bodies over time, without relying on thresholding, making it suitable for flood management and monitoring various water bodies like dams, lakes, wetlands, and rivers. The findings underscore the applicability of the proposed SFMI index in diverse and spectrally rich areas, demonstrating its effectiveness in monitoring various surface water bodies, detecting floods, and managing flood crises.

Identifying complementary conservation and restoration priority areas for plant species

The coverage of protected areas (PAs) remains far from the Kunming-Montreal target and degraded ecosystems are greatly limiting the conservation efficiency of PAs. Therefore, this paper proposes a method to identify conservation and restoration priority areas to supplement existing PAs. A case study was conducted focusing on Yunnan, southwestern China, which intersects with three world biodiversity hotspots. First, the spatial ranges for 3768 representative conservation plant species were mapped using species distribution models. Subsequently, planning units were classified into three restorability categories, namely no-need restoration, potentially restorable and non-restorable units, according to land cover changes between 2000 and 2020. Then, conservation and restoration priority areas were identified by applying a two-step systematic conservation planning process. Finally, replacement cost analysis was applied to compare the effectiveness of existing PAs and the overall 30 % priority areas. Northwestern, southwestern, and southeastern Yunnan have high biodiversity conservation values. Especially in eastern and southeastern Yunnan, large amounts of restoration priority areas were identified. Conservation and restoration priority areas account for 15.80 % and 3.69 % of Yunnan's land, respectively. Compared to existing PAs, conservation priority areas can increase the number of species covered from 2461 to 3277, and further to 3566 when including restoration priority areas. Compared to existing PAs, the mean species coverage in the overall 30 % priority areas has increased from 27.28 % to 72.69 %. Notably, 12.86 % of existing PAs were identified as restoration priority areas. This study indicates that in addition to conservation measures, implementing restoration strategies in high conservation-value areas is equally important.

Enhanced semantic-positional feature fusion network via diverse pre-trained encoders for remote sensing image water-body segmentation

ABSTRACT In the era of increasingly advanced Earth Observation (EO) technologies, extracting pertinent information (such as water-bodies) from the Earth’s surface has become a crucial task. Deep Learning, especially via pre-trained models, currently offers a highly promising approach for the semantic segmentation of Remote Sensing Imagery (RSI). However, effectively adapting these pre-trained models to RSI tasks remains challenging. Typically, these models undergo fine-tuning for specialized tasks, involving modifications to their parameters or structure of the original architecture, which may impact their inherent generalization capabilities. Furthermore, robust pre-trained models on nature images are not specifically designed for RSI, presenting challenges in their direct application to RSI tasks. To alleviate these problems, our study introduces a light-weight Enhanced Semantic-positional Feature Fusion Network (ESFFNet), leveraging diverse pre-trained image encoders alongside extensive EO data. The proposed method begins by leveraging pre-trained encoders, specifically Vision Transformer (ViT)-based and Convolutional Neural Network (CNN)-based models, to extract deep semantic and precise positional features respectively, without additional training. Following this, we introduce the Enhanced Semantic-positional Feature Fusion Module (ESFFM). This module adeptly merges semantic features derived from the ViT-based encoder with spatial features extracted from the CNN-based encoder. Such integration is realized via multi-scale feature fusion, local and long-distance feature integration, and dense connectivity strategies, leading to a robust feature representation. Finally, the Primary Segmentation-guided Fine Extraction Module (PSFEM) further bolsters the precision of remote sensing image segmentation. Collectively, these two modules constitute our light-weight decoder, with a parameter size of less than 4 M. Our approach is evaluated on two distinct water-body datasets, indicating superiority over other leading segmentation techniques. In addition, our method also demonstrates exemplary efficacy in diverse remote sensing segmentation tasks, such as building extraction and land cover classification. The source codes will be available at https://github.com/zhilyzhang/ESFFNet.

Detection of morphometric indicators of the soil surface of a grape plantation using spectral bands of satellite images

Introduction. Soils play an important role in the approximately 30-year period of operation of a grape planting, influencing plant growth, their yield and the quality of the grapes. In this study, the morphometric parameters of the surface soil layer of a grape plantation were studied using spectral channels of satellite images.Methodology. The methodology included the use of a “random forest” algorithm to classify soil cover using spectral channels and normalized satellite image indices and analyze the main physicochemical properties of soils. Accuracy was assessed using RMSD and confidence intervals calculated via bootstrapping.Results. The study revealed significant differences in the spectral reflectivity of different site options, which was due to carbonate content, humidity levels and the amount of humus. Areas with high carbonate and moisture content showed higher standard deviation values in the spectral channels. Studying the spectral characteristics of the soil surface makes it possible to effectively classify different areas based on remote sensing data. Analysis of combinations of spectral channels revealed an optimal set of three channels (B12, B11, B8A) with a minimum standard deviation when classifying an image into six soil variants of areas. For classification, a composition of five normalized indices can also be used, but in this case the calculation time increases significantly with a larger standard deviation and a larger confidence interval range. Using machine learning, six distinct soil surface types were segmented, demonstrating the complexity of the field›s soil mosaic. These results are critical for improving vineyard management and productivity

Floods modeling and analysis for Dubai using HEC-HMS model and remote sensing using GIS

Floods accompanied by thunderstorms in developed cities are hazardous, causing damage to infrastructure. To secure infrastructure, it is important to employ an integrated approach, combining remote sensing, GIS and precipitation data. The model was developed based on the estimation of event-based runoff and investigated the relationship between runoff and impervious surfaces. The novel approach of combining Hydrologic Engineering Center’s River Analysis System (HEC-GeoRAS) along with satellite imagery was utilized, where spatial data was combined with real-time values to run the model. As a first step, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was fed with information about precipitation, slope, soil type, as well as land use and land cover. The results reveal that the subbasins of Deira, Nief and Jumeirah have the largest impervious area and, thus, a higher probability of flood occurrence. The model was calibrated and validated using previous runoff events and by comparing observed and simulated streak flow and peak discharge against those reported in previous studies. It was found that the model is efficient and can be used in similar regions.

Mapping and Identification of Ecosystem Services Hotspots in the Brazilian Pampa Biome.

The intensification and expansion of changes in land use and land cover (LULC) can reduce the availability and quality of natural habitats and ecosystem services (ES). These changes have generated environmental damage in different parts of the world, especially in biomes more susceptible to modifications, such as the Pampa biome in the extreme south of Brazil. The Pampa biome has been neglected by environmental protection laws, despite its broad ecosystem and social importance. In this study, we used InVEST models to map and quantify five ES provided (i.e., water supply, carbon stock, groundwater stock, sediment retention and habitat quality) by 14 watersheds distributed in the Brazilian Pampa biome as well as determine ES hotspots by summing the areas with high provision of ES. We identified low availability of water supply and groundwater stock in practically the entire study area. High sediment retention and carbon stock were reported in areas with the presence of native vegetation. In addition, despite the large degraded areas, we observed high habitat quality associated with native vegetation in all studied watersheds. The hotspots varied spatially in the study area according to the proposed scenarios, the supply and the overlap of ES. Scenario 1 (>50% of ES) presented a larger area of high provision of ES. In contrast, scenario 2 (>75% of ES) presented smaller patches of areas with high provision of ES distributed across different watershed. We observed that large territorial extensions with high and medium provision of ES are vulnerable to the negative effects of LULC. Our study presented scenarios that indicate areas of high provision of ES, contributing to a more practical application, being a simplified and useful tool that can assist conservation and sustainable policies.

Impacts of Land Use and Land Cover Change on Non-Point Source Pollution in the Nyabarongo River Catchment, Rwanda

The Nyabarongo river catchment in Rwanda has experienced significant changes in its land use and land cover (LULC) in recent decades, with profound implications for non-point source pollution. However, there are limited studies on non-point pollution caused by nutrient loss associated with land use and land cover changes in the catchment. This study investigates the spatiotemporal impacts of these changes on water quality considering nitrogen and phosphorus within the catchment from 2000 to 2020 and 2030 as a projection. The SWAT model was used in analysis of hydrological simulations, while the CA–Markov model was used for the future projection of LULC in 2030. The results revealed (1) the important changes in LULC in the study area, where a decrease in forestland was observed with a considerable increase in built-up land, grassland, and cropland; (2) that the R2 and NSE of the TN and TP in the runoff simulation in the catchment were all above 0.70, showing good applicability during calibration and validation periods; (3) that from 2000 to 2020 and looking to the projection in 2030, the simulated monthly average TN and TP levels have progressively increased from 15.36 to 145.71 kg/ha, 2.46 to 15.47 kg/ha, 67.2 to 158.8 kg/ha, and 9.3 to 17.43 kg/ha, respectively; and (4) that the most polluted land use types are agriculture and urban areas, due to increases in human activities as a consequence of population growth in the catchment. Understanding the patterns and drivers of these changes is critical for developing effective policies and practices for sustainable land management and protection of water resources.

Urban expansion influences on land use and land cover changes in Kolkata metropolitan region: a geo-spatial study

Urban expansion influences on land use and land cover changes in Kolkata metropolitan region: a geo-spatial study

Evaluating Burn Severity and Post-Fire Woody Vegetation Regrowth in the Kalahari Using UAV Imagery and Random Forest Algorithms

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas.

Challenge of political integration in the territorial management of a protected area based on the analysis of land use and land cover change

Challenge of political integration in the territorial management of a protected area based on the analysis of land use and land cover change

Spatial Assessment of Urban Heat Island (UHI) in Kütahya using Landsat-8 Satellite Data

The Urban Heat Island (UHI) effect refers to the phenomenon where urban areas experience significantly higher temperatures than their rural surroundings due to human activities and modifications, such as replacing natural land cover with impervious surfaces, reducing vegetation, and increasing heat-absorbing materials. Kütahya central district has a dense population and building layout, which contributes to the intense UHI. Geographic Information System (GIS) and its tools are widely used in assessing UHI in urban areas, providing insights for both researchers and local authorities. Landsat-8 images acquired on August 8, 2023 (Path: 179, Row: 33, Cloud Cover: 0%) were utilized to create the Urban Heat Island (UHI) map for the study area. Through spatial analysis using Landsat-8 data for Kütahya city center, six different classes were defined, with the UHI map created using land surface temperature values. The highest and lowest UHI values vary between 4.245°C and -3.457°C. It has been observed that the average UHI increases by 12% in areas lacking dense building structures, characterized by rock surfaces and limited green areas. Conversely, in areas with parks, forests, and sparse settlements, the UHI decreases by 10%. With this study, a comprehensive evaluation of UHI was conducted for Kütahya city center. While the results obtained are expected to be an important resource for researchers and local authorities, they are also anticipated to be beneficial in the fields of engineering geology, urban geology, and urban planning.

Scale-Dependent Effects of Urban Canopy Cover, Canopy Volume, and Impervious Surfaces on Near-Surface Air Temperature in a Mid-Sized City

Cities are significantly warmer than their surrounding rural environments. Known as the ‘urban heat island effect’, it can affect the health of urban residents and lead to increased energy use, public health impacts, and damage to infrastructure. Although this effect is extensively researched, less is known about how landscape characteristics within cities affect local temperature variation. This study examined how tree canopy cover, canopy volume, and impervious surface cover affect daytime near-surface air temperature, and how these effects vary between different scales of analysis (10, 30, 60, 90 m radii), ranging from approximate street corridor to city block size. Temperature data were obtained from a car-mounted sensor, with traverse data points recorded during morning, afternoon, and evening times, plotted throughout the city of Portland, OR. The variability in near-surface air temperature was over 10° F during each traverse period. The results indicate that near-surface air temperature increased linearly with impervious surface cover and decreased linearly with tree canopy cover, with canopy volume reducing the temperature by 1° F for every 500 cubic feet of canopy volume for evening temperatures. The magnitude of the effect of tree canopy increased with spatial scale, with 60 and 90 m scales having the greatest measurable effect. Canopy volume had a positive relationship on presumed nighttime and early-morning temperatures at 60 and 90 m scales, potentially due to the impacts of wind fluctuation and air roughness. Canopy cover still contributed the largest overall decrease in street-scale temperatures. Increasing tree canopy cover and volume effectively explained the lower daytime and evening temperatures, while reducing impervious surface cover remains critical for reducing morning and presumed nighttime urban heat. The results may inform strategies for urban foresters and planners in managing urban land cover and tree planting patterns to build increased resiliency towards moderating urban temperature under warming climate conditions.