Ground Cover Types Research Articles

Revealing the optimal strategy for tree plantations on controlling soil erosion in China: A meta-analysis

Tree plantation has increased globally, driven by a desire to supply multiple ecosystem services including soil erosion control. However, how to plant trees can more effectively mitigate soil erosion still lacks systematic investigation. Therefore, we conducted a meta-analysis consisting of 412 paired observations from 80 studies to rigorously compare the effects of tree plantations on soil erosion control across different plantation approaches in China. The results showed that plantation age and ground cover type were the primary factors influencing the efficiency of tree plantations on runoff and sediment reduction. Mature plantations (>20 years) showed more efficiency in runoff (80.9 %) and sediment (97.2 %) reduction than young plantations (<10 years). The benefit of tree plantations with grass cover on runoff reduction was 70 %, while the capacity of plantations without ground cover was not significant. In addition, tree plantations in areas with steep slope, low mean annual precipitation, high soil sand content, and low soil organic carbon had better benefits on soil erosion control. Loess Plateau and Karst areas suffer the most severe water erosion in China, with distinct environmental conditions. This paper further revealed the targeted plantation strategy for these two regions: tree plantations in Karst areas should focus on the lithophyticity and drought tolerance of tree species, in the Loess Plateau should consider the trade-off between water consumption and soil erosion control.

Local factors influence the wild bee functional community at the urban-forest interface

IntroductionUrban forests provide necessary habitat for many forest-associated bee species amidst development and fragmentation. These forest fragments provide a variety of important floral and non-floral resources for bees that encompass a diversity of functional guilds characterized by size, diet breadth, nesting, sociality, origin, and seasonality. The relative importance of forest edge vs. interior habitats to these organisms is not well understood.MethodsHere, we compare bee communities between forest edge and interior locations at eight locations in Athens, GA, USA. We also explore the effects of stand structure, tree composition, ground cover type, and the presence of snags and downed wood on these organisms.ResultsWe found bee abundance and richness to be higher at the forest edge than interior with distinct community compositions at both locations. Canopy cover, invasive shrub cover, ground cover, and tree diversity influenced the observed community composition. We also determined that the most impactful functional traits influencing bee community structure in urban forest fragments were nesting substrate, origin (native or exotic to North America), sociality, and diet breadth.DiscussionOur findings will help establish the effects of local forest characteristics on the community composition, diversity, and abundance of wild bees and further our knowledge of the conservation value of urban forests for preserving wild bee communities.

Moss mortality significantly altered topsoil multifunctionality and microbial networks in a temperate desert

AbstractThe continuous rise in global warming and dramatic changes in precipitation patterns have resulted in frequent extreme weather events, biodiversity loss, and ecosystem degradation. This has resulted in varying degrees of mortality in desert mosses, a ground cover type that maintains desert stability and provides important ecological functions. There is, however, a lack of research on the effects of moss mortality on soil multifunctionality (SMF) and microbial interactions in temperate deserts. This study compared and analyzed the soil nutrient content, enzyme activities, and the associated microbial community structure between living and dead moss crusts in the temperate Gurbantunggut Desert. The results indicated that moss mortality improved SMF by increasing the soil carbon content, nitrogen, phosphorus, and enzymatic activity. However, the mortality also considerably reduced the total biomass and abundance of bacteria and fungi in the topsoil, having no significant effect on their diversity. Additionally, a significant decrease in the connectivity and complexity of the soil bacterial and fungal networks was observed when compared to living moss crusts. This was especially noted in the fungal networks, where fungi were more sensitive than bacteria to moss mortality. Although the natural mortality of mosses increases SMF, there is also an increased risk of nutrient loss, thereby threatening ecosystem sustainability. It is, therefore, imperative to consider the causes of crustal degradation while developing management and restoration processes for degraded desert ecosystems.

Towards a true conservation zero tillage system: “A proposed solution based on computer vision to herbicide resistance.”

The Zero Tillage (ZT) or Non-Tillage system constitutes an agricultural production approach designed for improved soil conservation, reduced fossil fuel usage, mitigation of waterway pollution, water and wind erosion, and addressing soil compaction, among other objectives. In this way, ZT promises more sustainable agriculture. However, current ZT production systems depend on herbicide applications for weed control. The use of herbicides with the same modes of action over many years has led to numerous resistant weed species, which end up threatening the success of the herbicide weed control and, consequently, the overall success of the ZT system. Being able to automatically detect and classify weed species directly under the sprayer during the chemical application is an alternative to control populations of weeds since it would allow selecting the herbicide and dose for those particular species. This study, conducted over 15 years in commercial fields utilizing Zero Tillage (ZT) management, examined various ground covers intrinsic to the ZT system originating from previous crops or residues post-harvest. ZT features a broad spectrum of contexts due to the complexity of different ground cover types, which challenge computer vision techniques. The assessment focused on the automatic detection and classification of among the most problematic monocotyledonous and dicotyledonous weed species in the province of Cordoba in Argentina, with the objective of enabling a more targeted and selective approach to their control. These are Amaranthus palmeri, Echinochloa crus-galli, Eleusine indica, Parietaria debilis and Conyza sumatrensis. Additionally, some species belong to the same botanical families with several morphological resemblances that defy vision algorithms. This work proceeded through a machine learning approach applied to computer vision features such as SIFT (scale-invariant feature transform), K-means (clustering), and Bag of Features over the Support Vector Machine for classification. The results showed an accuracy between 89 % and 98 % over the species, allowing a significant reduction of herbicide while applying the adequate active ingredient. Moreover, virtual binary maps from weed patches are devised to be implemented through ISOBUS protocol. Thus the current research contributes significantly to the issue of controlling populations of weeds in the ZT agriculture system.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices – Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) – indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.

The dependence of evaporative efficiency of vegetated surfaces on ground cover mass fractions in vegetated soils in mesic ecosystems

AbstractBare soil evaporation has been studied extensively, but less is certain regarding how site‐specific features, especially the overstory tree canopy and ground covers, mediate evaporation processes. Inspired by recent advances on modelling bare soil evaporative efficiency (SEE), this study explored SEE over a range of soil substrates and ground cover types, with and without the presence of an overstory canopy in three mesic ecosystems in Canadian Rocky Mountains. A significant relationship was found between the critical soil water content and ground cover mass fractions across various ground cover types, both with and without the presence of an overstory canopy. This relationship is expected to be prevalent across various ecosystems. Moreover, a simple approach for modelling SEE of vegetated surfaces and a correction method to account for below‐canopy SEE is also proposed. The model yields satisfactory simulations, and the approach is expected to be widely applicable, given the strength that its parameters are easily acquired, and its formulations are simple and straightforward. While the model may be particularly suited to mesic ecosystems, the underlying mechanism of SEE suggests that this model can also be applied in dryer conditions. This approach will greatly improve ET parameterization in land‐surface models (LSMs) and increase our knowledge of the global water cycle and ecosystem responses under climate change impacts.

A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data

Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR.

The impact of nickel mining on vegetation index in Molawe Sub-district, North Konawe District, Southeast Sulawesi, Indonesia

Abstract. Adidharma MA, Supriatna, Takarina ND. 2023. The impact of nickel mining on vegetation index in Molawe Sub-district, North Konawe District, Southeast Sulawesi, Indonesia. Biodiversitas 24: 4581-4588. Nickel mining activities in Molawe Sub-district, North Konawe District, Southeast Sulawesi, Indonesia have modified the land cover structure over time due to increased mining exploitation zones. This study intends to assess changes in land cover in the Molawe Sub-district, particularly around mining regions. The NDVI index was applied to Landsat 7 ETM+ satellite imagery data in 2001 to identify land cover conditions before mining activities, then to Landsat 8 OLI imagery data in 2015 and 2020 to identify land cover after mining activities. These data are then used to compute annual changes in the extent of each land cover. The NDVI classification produces four land cover types: non-vegetation, open soil, sparse vegetation, and moderate vegetation. The study shows that non-vegetation and open ground cover types experienced a significant increase in area from 2001 to 2015 and from 2015 to 2020. Meanwhile, sparse vegetation forest cover experienced a reduction in area from 2001 to 2015 and 2015 to 2020. In contrast to sparse vegetation, the moderate vegetation cover is experiencing a minor increase in area. Based on these results, relevant policymakers need to formulate policies to mitigate environmental impacts that may arise in the future.

Monitoring Changes in the Spectral Reflectivity of Baghdad City and the Impact of the Atmospheric Elements on it using R.S and GIS

Abstract Climate change in recent years has greatly affected the distribution of ground covers. Monitoring these changes has become very easy due to the development of remote sensitivity science and the use of satellites to monitor these changes. The aim of this research is to monitor changes in the spectral reflectivity of the Baghdad governorate center for the month (March, June, September, December) of the year 2021 using remote sensing and satellite images Sentinel 2 and knowing the climate imact on them. Fifty-one samples were selected for four types of ground cover (agricultural land, water, buildings and open space) and their spectral reflectivity was calculated using satellite images. Sentinel 2, where it was processed and analyzed, and then the spectral reflectivity values were calculated using the ArcGIS program. The effect of climate on the spectral reflectivity comes through its impact on the ground covers and thus changing the spectral signature of them. The relationship between temperature, spectral reflectivity, humidity, spectral reflectivity, and knowledge of the type of the relationship between them by means of mathematical equations. The results showed that the spectral reflection of the plant at the near-infrared beam has the most change than the rest of the ground covers.

Effects of snow cover on urban light climate environment in the high latitudes of northeast China

Light climate environment (LCE) has a significant impact on human health, behavioral characteristics, and the safety of life and property due to the high albedo of snow on the ground cover type, which in turn affects the regional climate and socio-economic development, but less relevant studies have been found. In this study, the effect of snow on daytime and nighttime light levels was quantified using comparative field observations and controlled experiments in artificial climate chambers, combined with analysis of variance and model fitting. The results of the study found that there was a significant difference between the presence and absence of snow on both daytime and nighttime light levels. During daytime, the ambient light level on the ground with snow is 5.68 times higher than without snow, an improvement of 12,711.06 Lux. At night, with moonlight, the nighttime illuminance with and without snow is 0.213 Lux and 0.01 Lux, respectively. When there is no moonlight, the snow has no significant effect on the light level. In addition, significant differences in LCE intensity with different snow depths, snow densities and black carbon (BC) pollution. At the same background light intensity, the LCE intensity varies significantly with increasing snow depth, snow density and BC pollution. The results reveals the quantitative impact of snow on LCE, providing scientific support for regional natural light energy use, human health and safety, urban environmental management and economic development.

Post‐wildfire coastal dunefield response using photogrammetry and satellite indices

AbstractFire has been suggested to be an initiation mechanism of landscape instability and coastal dune transgression, but modern evidence showing a shift to a transgressive dune phase is lacking. Following the largest wildfire in historical records on Kangaroo Island, South Australia, bimonthly uncrewed aerial vehicle (UAV) surveys were conducted on three coastal dune sites to study their post‐fire responses. The three sites studied here represent the landscape diversity of the temperate dunes of Kangaroo Island with both active coastal and inland relict stabilised dune fields studied. UAV surveys were used to reconstruct landscapes with structure from motion (SfM) photogrammetry and compared over time to illustrate significant changes in the landscape. The geomorphic and vegetation changes are compared in net and intra‐survey comparisons to illustrate the post‐fire dunefield response and trends towards stabilisation. Because of a lack of reliable baseline pre‐fire data, satellite geomedians are used to compute spectral indices to show the trajectory of ground cover in the study sites in the years preceding and following the fire. Satellite indices are used to separate 3D changes according to ground cover types and show their differing post‐fire responses. Local and regional wind, temperature and rainfall records are presented to provide weather patterns of the years preceding and following the fire, illustrating the wet and mild post‐fire weather. The overall results indicate no significant landscape instability across the studied sites and that the ground cover of vegetation is nearing pre‐fire baselines, showing that a severe fire has not caused a transgressive dunefield to develop.

Comparative study on the effects of soil quality improvement between urban spontaneous groundcover and lawn

Ecological restoration of today's urban soils is a global challenge. Soil quality improvement through groundcover is one of the practical ways in urban ecological restoration practice, and the selection of suitable groundcover types is the key to successful soil ecological restoration. Spontaneous groundcover and lawn, as the two typical urban groundcovers, are mostly used for urban soil restoration. However, few studies have quantified and compared their effects on improving soil quality in urban environments, making it difficult to make scientific choices in practice. Furthermore, the improving mechanism of groundcovers on soil quality is still not quite clear. First, root biomass is commonly considered to be a direct factor affecting soil quality, however, most of the current studies focus on the relationship between a single soil quality index and root mass, and few studies systematically explore the relationship between soil quality index and root mass. Second, species richness and functional group are two main differences in species composition between autochthonous groundcover and lawn, one of which must be the driving factor for the difference in soil improvement effect. At present, there are inconsistent results on whether the driving factors of groundcover for soil quality improvement are species richness or functional groups. In order to explore the above problems, this study sets up a comparative experiment in the urban environment. this study. Twelve typical groundcovers (including six urban groundcovers and six lawns) covering five functional groups (grass (G), forbs (F) and grass (L), G + F and G + F + L) were selected for experiments. Soil quality indicators including nitrogen (N), phosphorus (P), potassium (K), boron (B), iron (Fe), soil organic carbon (SOC), catalase (CAT), root biomass and species richness were tested, and the soil quality index (SQI) reflecting soil quality was calculated. The results showed that the soil quality index (SQI) of autochthonous groundcover was 35 % higher than that of lawn, and the contents of N, P, K and SOC in autochthonous groundcover were significantly higher than those in lawn. N, P, SOC, CAT showed a response to the root mass of groundcovers. Among all the indicators, only CAT was significantly correlated with groundcover species richness, while N, P, K, and SOC were significantly dependent on their functional diversity, and showed significant differences in different functional combinations. This study shows that the improvement effect of urban autochthonous groundcover on soil quality is significantly better than that of lawn. Functional groups may be the driving factors for the change of groundcover soil quality. Different functional groups may affect the root system of groundcover through compensation effect, thus affecting soil quality, which ultimately leads to the difference in the improvement effect of spontaneous groundcover and lawn on soil quality. This study provides a theoretical basis for the application of groundcover and lawn in urban soil ecological restoration in the future. In the practice of ecological restoration, if the goal is to improve soil quality, groundcover will be a more sensible choice than lawn. At the same time, attention should be paid to the diversity of functional groups to maximize the benefits of soil ecological restoration.

Remote-Sensing-Based Change Detection Using Change Vector Analysis in Posterior Probability Space: A Context-Sensitive Bayesian Network Approach

Change vector analysis (CVA) and post-classification change detection (PCC) have been the most widely used change-detection methods. However, CVA requires sound radiometric correction to achieve optimal performance, and PCC is susceptible to accumulated classification errors. Although change vector analysis in the posterior probability space (CVAPS) was developed to resolve the limitations of PCC and CVA, the uncertainty of remote sensing imagery limits the performance of CVAPS owing to three major problems: 1) mixed pixels, 2) identical ground cover type with different spectra, and 3) different ground cover types with the same spectrum. To address this problem, this study proposes the FCM-CSBN-CVAPS approach under the CVAPS framework. The proposed approach decomposes the mixed pixels into multiple signal classes using the fuzzy C means (FCM) algorithm. Although the mixed pixel problem is less severe in the high-resolution image, the change detection performance is still enhanced because, as a soft clustering algorithm, FCM is less susceptible to cumulative clustering error. Then, a context-sensitive Bayesian network (CSBN) is constructed to establish multiple-to-multiple stochastic linkages between signal pairs and ground cover types by incorporating spatial information to resolve problems 2 and 3 discussed above. Finally, change detection is performed using CVAPS in the posterior probability space. The effectiveness of the proposed approach is evaluated on three bi-temporal remote sensing datasets with different spatial sizes and resolutions. The experimental results confirm the effectiveness of FCM-CSBN-CVAPS in addressing the uncertainty problems of change detection and its superiority over other relevant change-detection techniques.

Characteristics of Red Spruce (Picea rubens Sarg.) Encroachment at Two Central Appalachian Heathland Study Areas

During the late 19th and early 20th centuries, intensive land use nearly eliminated red spruce (Picea rubens Sarg.) throughout portions of West Virginia (WV). Red spruce has been slow to regenerate on mountaintop heathland barrens surrounding Canaan Valley, West Virginia (WV), and little is known about the nature of encroachment. Using field surveys, geospatial data, and statistical modelling, the objectives were to 1) characterize and compare red spruce encroachment at two upland heath study areas in West Virginia (Bear Rocks and Cabin Mountain), 2) characterize percent cover of major ground cover types associated with red spruce regeneration sites in order to elucidate biotic interactions, and 3) model the biophysical correlates of red spruce encroachment using geospatial data and statistical modelling. Red spruce count was similar at both study areas, but there were substantially more seedlings and saplings at Cabin Mountain. Modelling revealed a positive relationship between red spruce count and rock cover and a negative relationship between red spruce and stand distance.

Bird carcass detection from integrated trials at multiple wind farms

AbstractIt is often necessary to estimate the number of wind turbine collision fatalities to assess impacts to birds following construction of wind farms. Detection of bird carcasses at wind turbines in the field is affected by carcass persistence and searcher detection rate. Integrated detection trials, which integrate carcass persistence and searcher detection trials into the periodic fatality search, have been proposed as an effective method for estimating these parameters. The purpose of our study was to test whether and how environmental factors affect integrated detection trial outcomes at multiple wind farms. We conducted this study at 10 wind farms in various environments of Japan. Binary data on trial outcomes in open versus forested areas served as our response variable in a generalized additive mixed model informed by days into trial, carcass body mass, season, whether snow covered the ground, and precipitation. For both ground cover types, days into trial and body mass were included in all the top models, suggesting that these factors most influenced bird carcass detection probability in integrated trials. The best model in open areas included days into trial, body mass, snow, and precipitation, and the best model in forested areas included days into trial, body mass, snow, precipitation, and season. Values of area under the curve indicated high accuracy of the best model for both ground cover types. The survey design needs to be appropriate to the size of the target species and to the environment in which the impacts will occur, such as the site's seasonality, its ground cover, and whether snow will cover the ground. Frequency of post‐construction fatality monitoring should also be set cautiously, especially at wind farms located on small‐bird migration routes, at wind farms in open areas, in areas with snow‐covered ground in winter, or in forested areas during spring and summer because detection probabilities decline fastest under such conditions.

Integrating Social and Ecological Predictors to Understand Variation within Ecosystems: A Case Study of the Great Smoky Mountains National Park PACE

Land management decisions have profound impacts on biological communities. Various administrative units are likely to implement different management decisions, with the result that consistent biological patterns that track administrative boundaries may emerge. To assess these relationships, we collected data on land cover, disturbance evidence, and tree communities in randomized sampling sites located in National Park, US Forest Service Wilderness, and US Forest Service Non-wilderness distributed across the Great Smoky Mountains National Park Protected Area-Centered Ecosystem (GRSM PACE). We examined how present-day values for these ecological responses were linked to both biophysical site characteristics (as the foundation for ecological communities) and jurisdiction type (as categories that integrate the combination of dynamic social decisions and management practices since initial designation). The variable representing administrative unit, jurisdiction type, acted as an umbrella indicator of the management trajectory and social influences that have affected a site and emerged as an important predictor of groundcover types (grass and litter), tree diversity, and wildlife sign at sites across the GRSM PACE. This was an exploratory study with limited sample size, so more data are needed to explore the mechanisms driving these relationships. These initial findings, however, suggest that differences in management activities alter the ecological trajectory of sites across the PACE. Given dynamic and diverse management practices, our data suggest jurisdiction overlaid on biophysical site information may be useful to rapidly assess how socially defined landscape activities impact biological responses, particularly when both are stressed by global change.

Sonic boom measurements: Practical implications considering ground effects, microphone installation, and weather hardening.

Sonic boom measurements from recent flight tests have provided an opportunity to investigate effects of microphone installation on sonic boom waveforms, spectra, and metric levels in support of NASA X-59 flight test planning. While those flight tests used N-wave aircraft (F-18s), modeling studies were also conducted using source characteristics for a shaped low-boom aircraft. Of particular interest were the effects of receiver height on boom waveforms and metrics at elevated receiver positions, microphone installation, and local ground cover type. Reductions of more than 2 dB in A-weighted sound exposure level and perceived level were shown for 1.6 ft (0.48 m) microphone heights for 35º ray elevation angle. Measured and modeled results are described in this letter.

Runoff observation in a tropical Brantas watershed as observed from long-term globally available TerraClimate data 2001–2020

Runoff information and its dynamics are critical for supporting watershed management; however, spatio-temporal data about runoff is rare or unavailable in data-scarce regions. Information about the performance of remote sensing-based runoff and its potential application is limitedly known. In data-scarce regions, this condition impedes comprehensive watershed assessment especially in the midst of climate change impacts. This study examined the performance of globally available monthly runoff dataset provided by TerraClimate at ~ 4 km spatial resolution and employed them to assess the runoff dynamics in a humid tropic watershed. Monthly TerraClimate data shows a moderate performance with an r of 0.63, RMSE of 57–127 mm/month and NRMSE of 18–30% to the simulated runoff from a well-calibrated model. The upper region of Brantas watershed was found to be the hotspot of high runoff. About 25% of the study area belongs to high runoff (0–33rd percentile). Over the last two decades, runoff has been slightly increased across the study area. Green vegetation fraction (GVF), precipitation, and topography are critical for regulating runoff dynamics. While topography and precipitation impact on runoff are straightforward, the GVF’s role is complex and site-specific. High runoff was found mostly to be associated with high precipitation and steep slope. GVF appears to be less effective in representing ground cover against runoff generation due to high variability of actual ground cover types. Using time-series and change vector analysis (CVA) of runoff and GVF, the dynamics of watershed condition was examined. Long-term CVA analysis also found that the condition in Brantas watershed was fluctuated with slight increase in impaired condition. The study exemplified the potential use of the remote sensing-based runoff data in a tropical data-scarce region. Despite limitation of the runoff data due to its moderate performance, the globally available monthly runoff data from TerraClimate can be used to support regional water resource assessment in data-scare regions. Future improvement that includes downscaling and use of machine learning can be considered to improve the remotely sensed runoff data to deliver the bigger benefits of such data.

Characterizing and correcting phase biases in short-term, multilooked interferograms

Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a “fading signal”) in the interferometric phase. We isolate the signature of the phase bias by constructing “daisy chain” sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms.

Clima Urbano e Ilhas de Calor no Miolo Urbano na Cidade de Salvador (BA)

O objetivo desta pesquisa foi analisar as ilhas de calor na cidade de Salvador nos bairros do Cabula e Pernambués ambos localizados no miolo urbano da cidade. Foram discutidos a relevância dos fatores geográficos de localização, altitude, influência dos sistemas de brisas marítimas e continentais, assim como o efeito do aquecimento das superfícies devido ao uso do solo urbano. Foi utilizada a concepção teórico-metodológica do Sistema Clima Urbano. Neste contexto, foram gerados mapas de usos dos solos das localidades através do programa Arcview 9.1. Posteriormente, foram efetuadas tomadas das temperaturas de superfície através de termômetros sensores de infravermelho nos diversos tipos de coberturas do solo em visitas técnicas à diversas ruas e avenidas nos bairros. Tais informações subsidiaram a organização do relatório final da pesquisa que se utilizou do Índice de Desconforto Térmico (ID). A utilização do ID detém extrema relevância pois busca estabelecer correlações entre as ilhas de calor e o desconforto físico que detém impacto direto na saúde e qualidade de vida das pessoas. Nos bairros do Pernambués e do Cabula foram identificadas classes de zona de desconforto. Porém, cabe destacar a relevância do fragmento da Mata Ombrófila Densa como elemento fundamental no estabelecimento de ilha de frescor no bairro Cabula.Palavras-chave: ilhas de calor, sistema clima urbano, fatores geográficos, uso do solo, desconforto térmico. Urban Climate, Heat Islands and Thermal Comfort in the Neighborhoods of Cabula and Pernambués in the Urban Center of Salvador (BA) A B S T R A C TThe objective of this research was to analyze the heat islands in the city of Salvador in the neighborhoods of Cabula and Pernambués both located in the urban core area of the city. The relevance of geographic factors of location, altitude, influence of sea and continental breeze systems, as well as the effect of heating of surface areas to urban land use were addressed. The theoretical and methodological conception of the Urban Climate System. In this context, maps of land uses of localities were generated through the arcview 9.1 programa. Subsequently, surface temperatures were made through infrared sensor thermometers through the various types of ground cover through technical visits to several streets and avenues in the neighborhoods. This information supported the organization of the final report of the research that used the Thermal Discomfort Index (ID). The use of ID has extreme relevance because it seeks to establish correlations between heat islands and physical discomfort that has a direct impact on people's health and quality of life. In the neighborhoods of Pernambués and Cabula, discomfort zone classes were identified. However, it is worth highlighting the relevance of the fragment of the Dense Ombrófila Forest as a fundamental element in the establishment of a frescor island in the Cabula neighborhood.Keywords: heat islands, urban climate system, geographic factors, land use, thermal discomfort.