Land Cover Conditions Research Articles

Effect of curve numbers on family benthic index in headwater streams in western North Carolina, USA

Many studies report stream degradation from direct anthropogenic impacts by correlating increased catchment impervious cover percentage (IC) with worsening stream health. However, this correlation can be less useful when predicting stream health in catchments with little to no IC. Our study characterizes catchments by combining hydrologic soil groups (HSG) with land cover and maintenance, known as Curve Numbers (CN), to predict stream health thresholds across a wide gradient of land cover conditions, particularly when IC is nominal or nonexistent. We did not compare the CN method with IC, but regressed two commonly used variables. The independent variable is the CN and the response variable is an index based on a rapid biological assessment protocol for wadable streams, known as the Family Benthic Index (FBI). The FBI is an index of organic pollution that rates stream reaches with published values that range from “Very Poor” to “Excellent” to categorize stream health. Generally, increasing CNs indicate increasing IC with slower infiltration velocity. CNs are commonly applied across the United States and other regions to estimate runoff quantity. However, we only investigated if CNs could reliably predict stream health (FBI). We sampled catchments (n = 178) in western North Carolina, US and found that CN can predict stream health (FBI) reasonably well (r2=0.78, p<0.0001). The FBI determination is based on rapid biological assessment protocol that relies on the identification of benthic macroinvertebrates collected from riffles to the family level using Hilsenhoff's Family Benthic Index (FBI). Our results suggest that a CN less than 70 indicated “Good” or better stream health categories (lower CNs have less IC and higher infiltration velocity). CNs greater than 70 suggest stream health below the “Good” classification. Future research includes replicating this method in other catchments and comparing this method to other indices such as IC or embodied energy in a landscape, which were not tested in this paper. Water quality professionals and scientists can calibrate this method for other catchments to inform decision making to address stream health.

CHANGE DETECTION OF THE LAND USE LAND COVER CATEGORIES AT LEVEL-II CLASSIFICATION USING MULTI TEMPORAL SATELLITE DATA: A CASE STUDY OF JODHPUR DISTRICT, RAJASTHAN.

Transformation of Land use Land cover is a dynamic process that takes place on the Earth’s surface and becomes a basic component in current strategies to manage natural resources and to monitor environmental changes. Remote sensing and GIS are important tools and techniques for monitoring and assessment of land use land cover change status, which can be helpful to decide the strategies for productive use of land for sustainable development. Therefore, it is important to detect the land use or land cover changes for sustaining growth and development of any region. The objective of this paper is to analyse the land use land cover changes over the last 24 years in Jodhpur district, Rajasthan, using multi season satellite data of 30 meter spatial resolution namely, Landsat-5 TM for 1993 and Landsat-8 OLI/TIRS for 2017. This paper has prepared the land use land cover status map on 1:50000 scale with the help of manual digitization technique for the years 1993 and 2017. Later, the change detection in land use land cover changes from 1993 to 2017 has been calculated using ArcGIS software. Land use land cover classes are classified into various categories such as built-up land, kharif crop, Rabi crop, double crop, fallow land, forest, scrub land, dunes, Barren/Rocky area, Quarrying, Lake/Reservoir/Tank, and others for the years of 1993 and 2017. The result of the study shows the expansion of agricultural land, built-up land, Quarrying and other manmade area during this time period and at the same time scrub land, dunes and barren rocky area are decreased. This has significant impact on the livelihood of the local community.

Automatic extraction of land cover statistics from satellite imagery by deep learning1

In this paper, we address the challenge of producing fully automated land cover estimates from satellite imagery through Deep Learning algorithms. We developed our system according to a tile-based, classify-and-count design. To implement the classification engine of the system, we adopted a cutting-edge Convolutional Neural Network model named Inception-V3, which we customized and trained for scene classification on the EuroSAT dataset. We tested and validated our system on two Sentinel-2 images representing quite different Italian territories (with an area of 751 km2 and 443 km2, respectively). Because no genuine ground-truth is available for the land cover of these sub-regional territories, we built a pseudo ground-truth by integrating land cover information from flagship European projects CORINE and LUCAS. A critical and careful analysis shows that our automatic land cover estimates are in good agreement with the pseudo ground-truth and offers extensive evidence of the remarkable segmentation ability of our system. The limits of our approach are also critically discussed in the paper and possible countermeasures are illustrated. When compared with traditional projects like CORINE and LUCAS, our automatic land cover estimation system exhibits three fundamental advantages: it can dramatically reduce production costs; it can allow delivering very timely and frequent land cover statistics; it can enable land cover estimation for very small territorial areas, well beyond the NUTS-2 level. As an additional outcome of land cover estimation, our system also automatically generates moderate resolution land cover maps that might be used in cartography projects as an agile first-level tool for map update or change detection purposes.

Tingkat Desertifikasi Ekosistem Karst Di Kecamatan Darul Imarah Kabupaten Aceh Besar

The research has been carried out to assess the rate of desertification of the karst ecosystem regarding land damage in the Darul Imarah, Aceh Besar District. This study aims to determine the level of damage to the karst ecosystem and its effect on water quality and to determine the condition of land cover in the Mata Ie karst ecosystem., Aceh Besar District. The data collection method used is using Landsat 8 satellite imagery to assess the level of damage to the karst ecosystem area. The assessment of water quality and quantity was carried out by means of a direct survey in the Mata Ie karts ecosystem area by analyzing BOD, COD, TDS, Turbidity, DO, Temperature and pH. The results of the assessment of the level of damage to the kart ecosystem area of the Mata Ie area have changed. The results of the study prove that due to land clearing for plantation activities in the Mata Ie area and also as a tourist spot, the conditions in the area are not desertification, Karst area which consists of desertification types, namely karst vegetation cover of 75% so it is referred to as non-desertification while rock outcrop of 5.79% is also not desertified

Complex associations of weather conditions with reproductive performance in urban population of a common waterbird.

Weather conditions are recognized as one of key determinants of animal reproductive performance; however, the effect of weather on breeding success can be modulated by different features of breeding habitat. Constantly expanding urban areas cause significant changes in land cover and environmental conditions, but whether and how urban landscape mitigates weather impact on animal fitness remains little explored. The aim of this study was to investigate the association between weather parameters and reproductive performance in a reed-nesting waterbird species, theEurasian coot Fulica atra. For this purpose, we performed a long-term monitoring of an urban coot population from central Poland, collecting data for over 400 breeding events. The results indicated that temperature may have contrasting effects on coot reproductive output at different stages of chick-rearing period (positive at early chick-rearing and negative at late chick-rearing). Also, contrary to our expectation, we found a positive relationship between mean daily precipitation in early chick-rearing period and reproductive output in our study population. Our study constitutes one of few examples showing how weather may affect fitness in urban wildlife and provides evidence for high complexity of associations between weather conditions and animal reproductive performance.

Dynamic Dust Source Regions and the Associated Natural and Anthropogenic Dust Emissions at the Global Scale

Dust emission is a key factor for reproducing dust’s physical process and its climate impact in dust modeling. However, previous studies always employed static land cover types to classify potential dust sources, ignoring dynamic variations in the surface bareness, which may lead to large uncertainties in the simulated dust emission fluxes, especially in regard to anthropogenic dust (AD) emission induced by wind erosion of human-disturbed land surfaces. Combined with anthropogenic land use and land cover change, dynamic dust source regions and the associated natural dust (ND) and AD emissions at the global scale from 2001 to 2018 are estimated in this study. The results show that the AD emissions exhibit a significant seasonal variability and dispersion at the global scale in contrast to the generally concentrated spatial distributions of ND emissions. The high-value ND areas are primarily located in the Sahara Desert, Arabian Peninsula, Karakum Desert, East Asia, Australian Desert, and other large desert areas, and the ND emission flux reaches a maximum of 50 μg m−2 s−1 or higher. High values of the annual average AD emission fluxes are located in southern Russia (124.6 ± 26.6 μg m−2 s−1), northern China (103.2 ± 21.9 μg m−2 s-1), the central and northern United States (56.0 ± 17.4 μg m−2 s−1), and the southern side of the Sahara Desert (74.1 ± 14.2 μg m−2 s−1). Especially in southern Russia and northern Kazakhstan, the AD emission flux accounts for the largest contribution to the total dust emission flux, at 98.2%. The ND emission flux demonstrates an upward trend in the central Sahara Desert, southern Arabian Desert, and Iranian regions and a significant downward trend in northwestern China. The AD emissions exhibit a decreasing trend in northern China, western India, the southern side of the Sahara Desert, and the central United States from 2001 to 2018 due to the implementation of environmental protection policies and a decrease in the wind speed, with the highest rate of decline reaching −74.9 μg m−2 s−1/decade. This study provided confidence for the further investigation of dust mass balance and the climatic impacts of natural and anthropogenic dust.

Mapping causal agents of disturbance in boreal and arctic ecosystems of North America using time series of Landsat data

The arctic and boreal biomes are changing as temperatures increase, including changes in the type, frequency, intensity, and seasonality of disturbances. However, our understanding of the frequency, extent, and causes of disturbance events remains incomplete. Disturbances such as fire, forest harvest, drought, wind, flooding, and insects and pathogens occur at different frequencies and severities, posing challenges to characterize and assess them under a single framework. We used the Continuous Change Detection and Classification (CCDC) algorithm on all available Landsat observations from 1984 to 2014 to detect land cover and land condition change. We mapped the following causes of disturbances annually across the study domain of NASA's Arctic Boreal Vulnerability Experiment (ABoVE): fire, logging, and pest damage. Differences between Landsat Tasseled Cap (TC) values pre- and post-disturbance were used in a random forest classifier to map causal agents. For forested ecosystems, we mapped causal agents including fire, insect, and logging. In areas that were not forest before disturbance, only the fire class was mapped. The result shows that multidimensional spectral-temporal change information is useful for mapping the causes of disturbance in arctic and boreal biomes. We employed two rounds of post-processing and used the information obtained from the comparison between the map and reference data to improve the final map. The user's and producer's accuracies of an aggregated disturbance map were 94.6% (± 2.37%) and 89.3% (± 21.78%) (95% confidence intervals in parenthesis). When evaluating the causal agents, insect damage was found the most challenging to map and validate. We estimated that 10.8% of the ABoVE core domain was disturbed between 1987 and 2012, with a margin of error of 0.5% at the 95% confidence level. Rates of disturbance due to logging remained constant over time, while fires were more episodic, and insect damage was highest between 2005 and 2010. Overall, fires affected 8.8% of the study area, while logging was 1.4% and insect damage 0.6%. Our maps indicate that pest damage became a significant issue after 2000, but it was more severe for forest ecosystems in Western Canada than in Alaska.

COMPARISON OF SPLIT WINDOWS ALGORITHM AND PLANCK METHODS FOR SURFACE TEMPERATURE ESTIMATION BASED ON REMOTE SENSING DATA IN SEMARANG

Surface temperature is one of the parameters in land–surface physical processes and is applied to global warming, climate change, and cycle hydrology. Two thermal bands in Landsat 8 imagery can be used as input for surface temperature extraction using the Split Windows Algorithm (SWA) and Planck method. This study aims to compare surface temperature estimates using the SWA and Planck methods and determine the surface temperature distribution based on the condition of land cover and its changes. The remote sensing data used are Landsat-8 OLI/TIRS Aqua MODIS images on August 27, 2013, and October 1, 2020. The results showed that Landsat 8 could obtain land cover information with an accuracy of 90% in 2013 and 91% in 2020. Planck surface temperature in 2013 was 1-3°C higher than SWA, while in 2020, Planck was 0.001-0.05°C lower than SWA but had similar distribution and pattern. The vegetation in the study area's central and south sides has a lower surface temperature than the built-up area on the north side. Land cover changes from non-built up to build-up area cause surface temperatures to increase. Each land cover has a different emissivity value and affects the surface temperature value, i.e., the lower the emissivity, the higher the surface temperature. The emissivity with surface temperature has a pearson correlation value ≥-0.8**.Keywords: Surface Temperature, Split Windows Algorithm, Planck

ANALYSIS OF INFILTRATION RATES OF SOILS ON THE INNER SLOPES OF THE LAKE SFÂNTA ANA CRATER

The present study consists of the comparative analysis of infiltration rates of soils under different land-cover conditions (dirt trails, grass plots and different types of forest stands) on the inner slopes of the Sfânta Ana crater, the catchment area of Lake Sfânta Ana in Romania. According to historical data, the depth and the surface of Lake Sfânta Ana have been undergoing significant changes during the last century, it lost in depth and the surface area shrunk approximatively 1,5 hectares. The losses are considered to be the consequences of the sedimentation as a result of the inflow processes from the catchment area, reduction of water level and eutrophication. The assessment and comparative analysis of steady infiltration rates of soils under different land-use categories in the catchment area were carried out to identify the possible sources of generation of surface flows responsible for sediment transport towards the lake using In Situ determinations with Double Ring Infiltrometers (DRI). Statistically significant differences were found between samples collected from grass plots, forests and the dirt trails, and within different forest stand structures. Results show that dirt trails and grass plots can be held responsible for the generation of surface flows. The main cause of low infiltration rates is soil compaction. Different forest stands are characterized by steady infiltration rates with high values and therefore provide effective protection. High forest coverage must be maintained in the crater, the problem of soil compaction on grass plots and dirt trails must be addressed.

VEGETATION AND LAND USE ANALYSIS FOR RUNOFF ESTIMATION IN SMALL FORESTED CATCHMENT: A CASE STUDY OF TAJOVSKÝ BROOK IN SLOVAKIA

Flash floods have been in the centre of attention for numerous hydrological studies recently and their magnitude and frequency are projected to increase due to the changing climate. This is especially important in small catchments where a local storm event can cause rapid increase in damage causing discharges. To estimate the possible impacts of a rainfall event, it is necessary to understand the precipitation/runoff conditions. Soil Conservation Service Curve Number (SCS CN) method is widely used and discussed to estimate the effect of land cover and soil moisture conditions on runoff. In this paper, we implement this standard method in the Tajovský brook catchment in Central Slovakia. The individual land cover classes were identified, and weighted CN numbers were established. Forest accounted for 67% of the entire area, grasslands for 19% and built-up areas for 10%. The final CNII number was computed as 60.8. Seven initial abstraction ratio values were tested on a monthly hydrograph data and optimal value was set to 0.01. This suggests the possible most suited regional value of the abstraction ratio that could be used for this type of small (&lt;50 km) forested catchments, but further testing on other catchments and precipitation events would be beneficial.

Avoiding Mistakes in Drone Usage in Participatory Mapping: Methodological Considerations during the Pandemic

Participatory mapping has continued to evolve with the onset of new methodologies and technology. Conventional methods for sketching have now expanded to incorporate the use of drone imagery and other sophisticated mapping approaches as a base map. However, the use of ultra-high resolution drone imagery does not mean that it will facilitate more participatory processes nor improve the quality of data and uses of information. Indeed, it has long been known that ultra-high spatial resolution can cause misinterpretation. During COVID-19, innovations are emerging to apply more remote technologies in participatory mapping. Mobility concerns, requirements, and preferences for physical distancing discourages active participation of local communities and are especially complex in contexts involving Indigenous People. This paper specifically explores the mistakes that can arise from over-reliance on employing drones as a tool in participatory mapping methods. This paper is based on a case study of participatory mapping conducted at 43 villages (around forest area) of Central Sulawesi Province and West Sulawesi Province. The participatory mapping was carried out by the Sulawesi Community Foundation (SCF) from 2019-2021. The result of the study found at least six signs of potentially negative outcomes from the use of ultra-high resolution drone imagery, starting from disorientation, misperception over the periods of drone acquisition, homogeneous land cover conditions, similar types of plants, numerous signs of nature, and labeling affixed on map. We also encourage the development of ultra-high-resolution drone imagery to take place under certain conditions and see its role as an interpretation dictionary or as a targeted tool in local contexts. In addition, we found that the level of active participation in participatory mapping during the Pandemic was higher than before the pandemic but requires some improvisations in meeting design

Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry—Results of the CORINE Land Cover Map 2018

The CORINE Land Cover (CLC) map was established in 1985 and is now one of the most widely used products from the Copernicus Land Monitoring Service. As the world’s longest consistent operational land cover monitoring product, CLC maps have been produced for reference years 1990, 2000, 2006, 2012 and now for 2018. This paper presents the results from the CLC2018 mapping project in the UK and analyses the results of the land cover status layer and the change layer from the period 2012–2018. It sets this change in context with the change results from the period 2006–2012 and finds that the rate of change between the subsequent CORINE land cover maps is continuing to increase. Changes mapped for the period 2012–2018 covered 76,032 ha greater than the change mapped between 2006 and 2012, an increase of 26% of mapped change. The area of changes mapped covered an area equivalent to 1.16% of the total land area of the UK. The number of different types of changes also continue to diversify; however, the dominance of rotational forestry is consistent with the previous map. The process of urban land take has been highlighted in the results between 2012 and 2018 and is a trend identified in previous iterations of the CLC inventories. The largest gain is in industrial or commercial units (an increase of 14.4%). This growth is mainly attributed to renewable energy infrastructure. As well as the descriptive analysis, the results have been analysed to identify the likely pressures being experienced on the land in the UK. Although the CLC mapping approach is consistent, there have been improvements to the input EO data used to map the changes. For 2018, the Copernicus Sentinel-2 system offered a consistent and reliable image source for the first time. This increased the spatial resolution of the source datasets to 10 m, allowing for more accurate identification of small features and those with fine spatial textures such as suburban, road networks and windfarms. We also look forward to the development of CLC+, the new generation of CORINE land mapping, and the improvements it could make.

Analysis of human exposure to landslides with a GIS multiscale approach

Decision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.

Uso e Cobertura do Solo após Eventos de Queimadas no Município de Floresta em Pernambuco

Queimadas são recorrentes no Brasil para limpeza e aumento de territórios. Análises temporais sobre o tema demonstram que o município de Floresta em Pernambuco se destaca no número de casos no mês de novembro de 2019. Este artigo tem como propósito coletar e quantificar a incidência dos focos de calor e áreas queimadas na região e mês supracitados, além das condições de uso e cobertura do solo baseado nas técnicas do sensoriamento remoto. Utilizou-se informações da Agência Pernambucana de Águas e Clima (APAC) para a climatologia. As bases do Banco de Dados de Queimadas foram empregadas na coleta e quantificação dos focos de calor. Imagens MODIS/TERRA, MODIS/Combinado e Landsat-8/OLI foram aplicadas na caracterização das áreas queimadas, interpretação de imagens e cálculo de índices de vegetação e de queimada. Foram identificadas 35 áreas de fogo, sendo 31 de focos de calor confirmados, totalizando 208. Dessas áreas, 7 coincidiram com o dia 06 de novembro de 2019, dia Juliano 310. Observou-se pelos índices de vegetação WDRVI, IAF e NDVI que o comportamento da vegetação saudável é uniforme, com densidade de baixa a moderada. Após a queimada, o uso e cobertura do solo se regenerou, não havendo modificação. O SAVI e os NBR e NBR2 expõem coeficientes de correlação positivos muito forte e forte, respectivamente, indicando a dependência de uma variável em relação a outra. Desse modo, o sensoriamento remoto se demonstrou eficaz no estudo das condições do uso e cobertura do solo, bem como a identificação das queimadas no município de Floresta.Palavras-Chave: sensoriamento remoto, imagens de satélites, focos de calor, índices de vegetação, índices de queimada. Land use and coverage after fire events in the municipality of Floresta in Pernambuco ABSTRACTFires are recurrent in Brazil to clean up and increase territories. Temporal analyzes on the subject show that the municipality of Floresta in Pernambuco stands out in the number of cases in the month of November 2019. This article aims to collect and quantify the incidence of hotspots and burned areas in the aforementioned region and month, in addition to of land use and cover conditions based on remote sensing techniques. Information from the Pernambuco Water and Climate Agency (APAC) was used for climatology. The databases of the Burning Database were used to collect and quantify hotspots. MODIS/TERRA, MODIS/Combinado and Landsat-8/OLI images were applied in the characterization of burned areas, image interpretation and calculation of vegetation and fire indexes. 35 fire areas were identified, 31 of which were confirmed hotspots, totaling 208. Of these areas, 7 coincided with November 6, 2019, Julian day 310. It was observed by the WDRVI, IAF and NDVI vegetation indices that the Healthy vegetation behavior is uniform, with low to moderate density. After the fire, the use and cover of the soil regenerated, with no change. The SAVI and the NBR and NBR2 exhibit very strong and strong positive correlation coefficients, respectively, indicating the dependence of one variable on the other. In this way, remote sensing proved to be effective in the study of land use and cover conditions, as well as the identification of fires in the municipality of Floresta.Keywords: remote sensing, satellite images, hotspots, vegetation indices, fire índices.

Detection of Surface Water Bodies and Cyanobacterial Blooms using Satellite based Remote Sensing

For many environmental applications, accurate surface water maps are crucial. It is possible to construct surface water maps by merging data from multiple sources. Due to sensor limitations, complex land cover, geography, and atmospheric conditions, accurate surface water estimation using satellite imagery is still a difficult undertaking. In this study, Sentinel 1 images from Google Earth Engine (GEE) was used to derive a high-resolution water mask. To overcome the limitation of visible satellite images which is the presence of clouds, on this research was focused on using Synthetic-Aperture Radar (SAR) sensors, like Sentinel-1, which penetrate clouds and permitting to get a view of the earth surface, even when clouds are presence. To validate the proposed method, it was applied to three study cases which are located in different climate zones in Sri Lanka. In the meanwhile, this research implemented a time series of Sentinel 2 satellite images from GEE to monitor the distribution of cyanobacterial blooms on those lakes. Recent years have seen an increase in the frequency and severity of cyanobacteria in recreational lakes and reservoirs, which has become a serious concern for the public’s health and a serious environmental danger. Monitoring cyanobacterial blooms serves as the foundation for early detection and treatment. Therefore, the results of this study could be used as a benchmark for future environmental monitoring and management of these Sri Lankan lakes and reservoirs.

Impact of mining on land use land cover change and water quality in the Asutifi North District of Ghana, West Africa

Surface and groundwater are important resources for human life and health. Land use changes and mining activities, however, have the potential to compromise the safety of these resources. This study aims to investigate the land cover conditions before and during mining activities and assess the impact on forests, surface, and groundwater resources in the Asutifi North District, Ghana. Landsat data of 30 m resolution was used to assess the land cover change from 2000 to 2020. A cross-sectional analysis of data between April to June 2004 to 2019 was used to assess water quality. The overall accuracy of the land cover classification ranged between (91.33–99.41)% and (78.01–85.95)% respectively for ground-truth validation and validation using the MODIS annual 500 m land cover dataset. Forest cover decreased by 11.47% while agriculture doubled from 12.13% to 24.06% between 2000 and 2020. The introduction of mining is responsible for major land cover changes including the conversion of forest to agricultural land through shifting cultivation. For a general assessment of water quality, the percentage of pH, TB, TDS, NH3, Mg and SO4 measurements outside the EPA admissible limits for drinking water is 16.67%, 83.33%, 0.69%, 0%, 10.42%, and 16.67% respectively. Turbidity is the most impacted water quality parameter affected by land cover changes while NH3 is the least impacted parameter. The incidences of water quality outside EPA/WHO limits for drinking water at all monitoring points call for education and collective action to safeguard water sources from further deterioration. It is necessary mining companies adhere to environmental protection guidelines.

VEGETATION CHANGES ANALYSIS USING NDVI (NORMALIZED DIFFERENCE VEGETATION INDEX) METHOD IN KALIANDA SUBDISTRICT, LAMPUNG SELATAN DISTRICT, INDONESIA

&lt;div&gt;&lt;p class="Els-history-head"&gt;Each year, the population in Kalianda subdistrict, Lampung province keeps increasing. The increase in population means that the need for living space also increasing, this can lead to land cover conversion from a vegetation area to a residential area. To find how much the change in land conversion over the year, a spatial analysis over the period of 18 years was needed. So that it is expected to be able to manage spatial planning to cope with disasters in Indonesia. This research uses Landsat 7 and 8 images that have been calculated using the Normalized Difference Vegetation Index (NDVI) method. The results show that between 2010 - 2019, there is an increase in land cover changes in open land/house/building/no vegetation around 814 ha (6.9%), in low vegetation increase 975 ha (8.4%). However, in the medium vegetation was decreasing around 1123 ha (9.5%). Overall observation sample of the ground check is following the actual condition of land cover in Kalianda Sub-district. The data show that in the Kalianda subdistrict, land management is necessary to suppress land cover changes so that it won’t lead to environmental disasters&lt;/p&gt;&lt;/div&gt;

Forest area changes throughout the years in Bjelovarsko-Bilogorska County

The results of forest cover reduction were obtained using raster data and administrative borders for the Republic of Croatia. Examples are taken from other countries to compare the results and show the reduction of cover, both forest and agricultural. The first part of this paper describes the situation in the Republic of Croatia, where Ministry of Environmental Protection provided analysis. The condition of land cover in the Republic of Croatia is presented. The second part of this paper is a description of the task development process in the software package "QGIS". From adding CLC raster data to, the actions performed in the program that were performed until the results arrived. Finally, the interpretation of the obtained data and the conclusion follow.

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium).

Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012–2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes

Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% ± 1.4%) compared to a generalized, national model (70.3% ± 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures.