Journal articles on the topic 'LC (Land cover)'
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1
Rahaman, S. A., S. Aruchamy, K. Balasubramani, and R. Jegankumar. "LAND USE/LAND COVER CHANGES IN SEMI-ARID MOUNTAIN LANDSCAPE IN SOUTHERN INDIA: A GEOINFORMATICS BASED MARKOV CHAIN APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 231–37. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-231-2017.
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Nowadays land use/ land cover in mountain landscape is in critical condition; it leads to high risky and uncertain environments. These areas are facing multiple stresses including degradation of land resources; vagaries of climate and depletion of water resources continuously affect land use practices and livelihoods. To understand the Land use/Land cover (Lu/Lc) changes in a semi-arid mountain landscape, Kallar watershed of Bhavani basin, in southern India has been chosen. Most of the hilly part in the study area covers with forest, plantation, orchards and vegetables and which are highly affected by severe soil erosion, landslide, frequent rainfall failures and associated drought. The foothill regions are mainly utilized for agriculture practices; due to water scarcity and meagre income, the productive agriculture lands are converted into settlement plots and wasteland. Hence, land use/land cover change deduction; a stochastic processed based method is indispensable for future prediction. For identification of land use/land cover, and vegetation changes, Landsat TM, ETM (1995, 2005) and IRS P6- LISS IV (2015) images were used. Through CAMarkov chain analysis, Lu/Lc changes in past three decades (1995, 2005, and 2015) were identified and projected for (2020 and 2025); Normalized Difference Vegetation Index (NDVI) were used to find the vegetation changes. The result shows that, maximum changes occur in the plantation and slight changes found in forest cover in the hilly terrain. In foothill areas, agriculture lands were decreased while wastelands and settlement plots were increased. The outcome of the results helps to farmer and policy makers to draw optimal lands use planning and better management strategies for sustainable development of natural resources.
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López, N., A. Márquez Romance, and E. Guevara Pérez. "Change dynamics of land-use and land-cover for tropical wetland management." Water Practice and Technology 15, no. 3 (June 12, 2020): 632–44. http://dx.doi.org/10.2166/wpt.2020.049.
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Abstract In hydrographic basins with wetlands, changes in land use (LU) and land cover (LC) impact the conservation of natural resources, leading to dynamics analysis for integral management. A method is proposed offering greater accuracy in determining the LU and LC bi-temporal and spatial change dynamics in tropical wetlands. LU and LC monitoring is based on Landsat images from 1986 to 2017. ‘Pre-classification’ and ‘post-classification’ methods are applied. In the former, reflectance image differencing and principal component N° 1 image differencing are analyzed to estimate the rate of change/no change area. In the latter, supervised classification is carried out of image pairs from different dates. The principal components method shows that principal component N° 1 collects between 88 and 93% of the reflectance variance in n spectral bands of each satellite image, which improves accuracy in determining LU and LC change dynamics.
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Sertel, Elif, Raziye Topaloğlu, Betül Şallı, Irmak Yay Algan, and Gül Aksu. "Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps." ISPRS International Journal of Geo-Information 7, no. 10 (October 15, 2018): 408. http://dx.doi.org/10.3390/ijgi7100408.
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This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes.
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Bratic, G., M. E. Molinari, and M. A. Brovelli. "VALIDATION OF THE GLOBAL HIGH-RESOLUTION GLOBELAND30 LAND COVER MAP IN EUROPE USING LAND COVER FIELD SURVEY DATABASE - LUCAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 51–59. http://dx.doi.org/10.5194/isprs-archives-xlii-4-51-2018.
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<p><strong>Abstract.</strong> High-resolution land cover maps are one of the technological innovations driving improvements in many fields influenced by Geographic Information Systems (GIS) and Remote Sensing. In particular, the GlobeLand30 (GL30), global LC map with spatial resolution of 30<span class="thinspace"></span>m, is thought to be one of the highest quality high-resolution products. However, these LC maps require validation to determine their suitability for a particular purpose. One of the best ways to provide useful validation reference data is to do a high-level accuracy field survey, but this is time consuming and expensive. Another option is to exploit already available datasets. This study assesses thematic accuracy of GL30 in Europe using LUCAS as a validation reference, because it is a free and open field survey database. The results were generally not good, and very bad for some classes. Analysis was then restricted to a small region (Lombardy, Italy) where LC data of higher resolution than those of GL30 were available. LUCAS was also found to be incoherent with this product. Further comparisons of LUCAS with other independent sources confirmed that the LC attributes of LUCAS are inconsistent with expectations. Although these findings may not be generalized to other regions, the results warn against the suitability of LUCAS as ground truth for LC validation. The paper discusses the process of thematic accuracy assessment of the GL30 and the applicability of LUCAS for high-resolution global LC validation.</p>
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Khyami, Ali. "Impact of land cover change on land surface temperature over Greater Beirut Area – Lebanon." Journal of Geoinformatics & Environmental Research 2, no. 1 (June 23, 2021): 14–27. http://dx.doi.org/10.38094/jgier2121.
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Remote sensing (RS) technology has been used together with geographic information systems (GIS) to determine the LC types, retrieve LST, and analyze their relationships. The term Greater Beirut Area (GBA) is used to refer to the city of Beirut and its suburbs which witnessed rapid urban growth, after the end of the civil war, in the last decade of the twentieth century, due to the increase in the number of its inhabitants, and the prosperity and development of sectors such as; industrial, trade, tourism, and construction. These factors led to a wide change in the land cover (LC) types and increased land surface temperature LST. The results showed an increase in built-up areas by 29.1%, and agricultural lands by 6%, while bare land, forests, and seawater decreased by 28.5%, 4.9%, and 1.9%, respectively. These changes caused large differences in the LST between built-up areas and other LC types. The highest LST recorded was in built-up areas (33.03°C in 1985, and 34.01°C in 2020), followed by bare lands (32.61 °C in 1985 and 33.49°C in 2020), cropland (31.23°C in 1985 and 32.17°C in 2020), forest (30.08°C in 1985 and 30.47°C in 2020), and water (24.97°C in 1985 and 28.15°C in 2020). Consequently, converting different LC types into built-up areas led to increases in LST and changed microclimate.
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Giuliani, Gregory, Denisa Rodila, Nathan Külling, Ramona Maggini, and Anthony Lehmann. "Downscaling Switzerland Land Use/Land Cover Data Using Nearest Neighbors and an Expert System." Land 11, no. 5 (April 21, 2022): 615. http://dx.doi.org/10.3390/land11050615.
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High spatial and thematic resolution of Land Use/Cover (LU/LC) maps are central for accurate watershed analyses, improved species, and habitat distribution modeling as well as ecosystem services assessment, robust assessments of LU/LC changes, and calculation of indices. Downscaled LU/LC maps for Switzerland were obtained for three time periods by blending two inputs: the Swiss topographic base map at a 1:25,000 scale and the national LU/LC statistics obtained from aerial photointerpretation on a 100 m regular lattice of points. The spatial resolution of the resulting LU/LC map was improved by a factor of 16 to reach a resolution of 25 m, while the thematic resolution was increased from 29 (in the base map) to 62 land use categories. The method combines a simple inverse distance spatial weighting of 36 nearest neighbors’ information and an expert system of correspondence between input base map categories and possible output LU/LC types. The developed algorithm, written in Python, reads and writes gridded layers of more than 64 million pixels. Given the size of the analyzed area, a High-Performance Computing (HPC) cluster was used to parallelize the data and the analysis and to obtain results more efficiently. The method presented in this study is a generalizable approach that can be used to downscale different types of geographic information.
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Shekar N C, Sanjay, and Hemalatha H N. "Land use and Land Cover Characteristics using Bhuvan and MODIS Satellite Data." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 289–94. http://dx.doi.org/10.35940/ijrte.f5322.019521.
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Understanding vegetation characteristics is essential for watershed modeling, like in the prediction of streamflow and evapotranspiration (AET) estimation. So, the present study was taken to analyze the Land use/Land cover characteristics in a Sub-humid tropical river basin which is originating in the forested part of Western Ghats mountain ranges using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Bhuvan satellite data as inputs for the algorithm. All the fourteen LU/LC characteristics present in the Hemavathi basin (5427 km2 ) were analyzed in the basin using satellite data which is located in Karnataka, India. Land Surface Reflectance (LSR) and Land Surface Temperature (LST) were the two data products used as input to map the pixel-wise variations in albedo, the fraction of vegetation (FV) and Land Surface Temperature (LST). It was found from the rainfall data that the year 2019 experienced higher rainfall than the average and 2012 very low rainfall than the normal. Parameters considered in this study Albedo, LST and FV are susceptible to wetness and temperature conditions. Variations in albedo and LST were similar in that both values in the summer of 2019 and 2012 are high than winter due to the high temperature and less wetness from all the LU/LC classes. Similarly, FV variations show opposite trends that values in the summer of 2019 and 2012 are low than in winter, which is due to the high temperature and less wetness. The results and discussions show that significant realistic variations in albedo, LST and FV with respect to all LU/LC classes. All the LU/LC classes characteristics in this study show significant variations with respect to wetness and temperature conditions, so the methodology proposed in this study can be used in regional monitoring of LU/LC classes in a convenient and cost-effective manner.
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Shukla, S., M. V. Khire, and S. S. Gedam. "Monitoring Land Use/Land Cover Changes in a River Basin due to Urbanization using Remote Sensing and GIS Approach." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 949–53. http://dx.doi.org/10.5194/isprsarchives-xl-8-949-2014.
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Faster pace of urbanization, industrialization, unplanned infrastructure developments and extensive agriculture result in the rapid changes in the Land Use/Land Cover (LU/LC) of the sub-tropical river basins. Study of LU/LC transformations in a river basin is crucial for vulnerability assessment and proper management of the natural resources of a river basin. Remote sensing technology is very promising in mapping the LU/LC distribution of a large region on different spatio-temporal scales. The present study is intended to understand the LU/LC changes in the Upper Bhima river basin due to urbanization using modern geospatial techniques such as remote sensing and GIS. In this study, the Upper Bhima river basin is divided into three adjacent sub-basins: Mula-Mutha sub-basin (ubanized), Bhima sub-basin (semi-urbanized) and Ghod sub-basin (unurbanized). Time series LU/LC maps were prepared for the study area for a period of 1980, 2002 and 2009 using satellite datasets viz. Landsat MSS (October, 1980), Landsat ETM+ (October, 2002) and IRS LISS III (October 2008 and November 2009). All the satellite images were classified into five LU/LC classes viz. built-up lands, agricultural lands, waterbodies, forests and wastelands using supervised classification approach. Post classification change detection method was used to understand the LU/LC changes in the study area. Results reveal that built up lands, waterbodies and agricultural lands are increasing in all the three sub-basins of the study area at the cost of decreasing forests and wastelands. But the change is more drastic in urbanized Mula-Mutha sub-basin compared to the other two sub-basins.
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et al., Abu Haider. "Evaluation of spatiotemporal dynamics of land cover and land surface temperature using spectral indices and supervised classification: A case study of Jobai Beel Area, Bangladesh." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 12 (December 2021): 63–79. http://dx.doi.org/10.21833/ijaas.2021.12.009.
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This study aims to evaluate the spatiotemporal change of land cover (LC) and surface temperature of the Jobai Beel area, an exclusive agriculture zone, situated in the far-flung area of northwest Bangladesh using satellite data. Multi-temporal Landsat series of data from 1989 to 2020 and geospatial techniques have been employed to evaluate the LC change and land surface temperature (LST) variation. Different spectral indices such as NDVI, MNDWI, NDBal have been used to retrieve individual LC. Corresponding LST has also been extracted using the thermal bands. Supervised Classification and the post-classification change detection technique were employed to determine the temporal changes and validate the individual LC. The results were employed to assess the LST variation associated with LC changes. The results reveal that the area had undergone a drastic and inconsistent heterogeneous LC transformation during the study period. Water and vegetation areas have expanded at a rate of 0.24km2/year and 0.45km2/year respectively, while bare lands have shrunk at a rate of 0.70km2/year. In general, Bare land exhibits a significant positive correlation, when Vegetation areas show a significant negative correlation with LST. However, the correlation between water areas and LST was found statistically insignificant. Agriculture in the form of vegetation has been found the most dominating land cover character throughout the study period, which has been regulating the LST variation across the area.
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Getu Engida, Tewodros, Tewodros Assefa Nigussie, Abreham Berta Aneseyee, and John Barnabas. "Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia." Applied and Environmental Soil Science 2021 (July 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/6617541.
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Understanding the hydrological process associated with Land Use/Land Cover (LU/LC) change is vital for decision-makers in improving human wellbeing. LU/LC change significantly affects the hydrology of the landscape, caused by anthropogenic activities. The scope of this study is to investigate the impact of LU/LC change on the hydrological process of Upper Baro Basin for the years 1987, 2002, and 2017. The Soil Water Assessment Tool (SWAT) model was used for the simulation of the streamflow. The required data for the SWAT model are soils obtained from the Food and Agriculture Organization; Digital Elevation Model (DEM) and LU/LC were obtained from the United States Geological Survey (USGS). The meteorological data such as Rainfall, Temperature, Sunshine, Humidity, and Wind Speeds were obtained from the Ethiopian National Meteorological Agency. Data on discharge were obtained from Ministry of Water, Irrigation and Electricity. Ecosystems are deemed vital. Landsat images were used to classify the LU/LC pattern using ERDAS Imagine 2014 software and the LU/LC were classified using the Maximum Likelihood Algorithm of Supervised Classification. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The calibration was carried out using observed streamflow data from 01 January 1990 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2009. LU/LC analysis shows that there was a drastic decrease of grassland by 15.64% and shrubland by 9.56% while an increase of agricultural land and settlement by 18.01% and 13.01%, respectively, for 30 years. The evaluation of the SWAT model presented that the annual surface runoff increased by 43.53 mm, groundwater flow declined by 27.58 mm, and lateral flow declined by 5.63 mm. The model results showed that the streamflow characteristics changed due to the LU/LC change during the study periods 1987–2017 such as change of flood frequency, increased peak flows, base flow, soil erosion, and annual mean discharge. Curve number, an available water capacity of the soil layer, and soil evaporation composition factor were the most sensitive parameters identified for the streamflow. Both the calibration and validation results disclosed a good agreement between measured and simulated streamflow. The performance of the model statistical test shows the coefficient of determination (R2) and Nash–Sutcliffe (NS) efficiency values 0.87 and 0.81 for calibration periods of 1990–2002 and 0.84 and 0.76 for the validation period of 2003 to 2009, respectively. Overall, LU/LC significantly affected the hydrological condition of the watershed. Therefore, different conservation strategies to maintain the stability and resilience of the ecosystem are vital.
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K Ramesh, R Asha, and E Jamuna. "Assessment of land use/land cover change detection of Mettupalayam Taluk, Coimbatore district, Tamil Nadu, India, using RS and GIS." GSC Biological and Pharmaceutical Sciences 22, no. 1 (January 30, 2023): 241–46. http://dx.doi.org/10.30574/gscbps.2023.22.1.0023.
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Understanding changes in land use land cover is essential managing and monitoring natural resources and development, particularly where urbanization is expanding. In present study, describes land use/land cover (LU/LC) mapping and change detection analysis of Mettupalayam taluk of Coimbatore district in Tamil Nadu. Estimation in ArcGIS for LU/LC classification from LANSAT images, is the best method for classifier for the different features were used to classify the satellite images are used to classify the land use/land cover change s for the given period. The results indicated that land cover changes have occurred in slight decrease in forests, vegetation and mountain and a corresponding tremendous increase in settlement. The comparison of LU/LC in 2000 and 2019 derived from topo-sheet and satellite imagery interpretation indicates that the settlement area shows variations. Settlement is an area of human habitation, which has a cover of buildings and a network of transport and other civic amenities, due to increasing population and the land is converted into habitation. The (Urbanization) settlement/ built-up areas have increased by 7.29% in 2000 while in 2019 it has increased to 16.43 %. It is found urban area increased due to population growth cum rapid economic progress.
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Ampim, Peter A. Y., Michael Ogbe, Eric Obeng, Edwin K. Akley, and Dilys S. MacCarthy. "Land Cover Changes in Ghana over the Past 24 Years." Sustainability 13, no. 9 (April 28, 2021): 4951. http://dx.doi.org/10.3390/su13094951.
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Changes in land cover (LC) can lead to environmental challenges, but few studies have investigated LC changes at a country wide scale in Ghana. Tracking LC changes at such a scale overtime is relevant for devising solutions to emerging issues. This study examined LC changes in Ghana for the past almost two and half decades covering 1995–2019 to highlight significant changes and opportunities for sustainable development. The study used land cover data for six selected years (1995, 2000, 2005, 2010, 2015, and 2019) obtained from the European Space Agency. The data was analyzed using R, ArcGIS Pro and Microsoft Excel 365 ProPlus. The original data was reclassified into eight LC categories, namely: agriculture, bare area, built-up, forest, grassland, other vegetation, waterbody, and wetland. On average, the results revealed 0.7%, 131.7%, 23.3%, 46.9%, and 11.2% increases for agriculture, built-up, forest, waterbody, and wetland, respectively, across the nation. However, losses were observed for bare area (92.8%), grassland (51.1%), and other vegetation (41%) LCs overall. Notably, agricultural land use increased up to 2015 and decreased subsequently but this did not affect production of the major staple foods. These findings reveal the importance of LC monitoring and the need for strategic efforts to address the causes of undesirable change.
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Tuzcu Kokal, A., İ. İsmailoğlu, and N. Musaoğlu. "COMPARISON OF LANDSAT-9 AND PRISMA SATELLITE DATA FOR LAND USE / LAND COVER CLASSIFICATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-M-2-2022 (July 25, 2022): 197–201. http://dx.doi.org/10.5194/isprs-archives-xlvi-m-2-2022-197-2022.
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Abstract. Land use and land cover (LU/LC) detection has great significance in management of natural resources and protection of environment. Hence, monitoring LU/LC with the state-of-the-art approaches has gained importance during the recent years and free access satellite images have become valuable data source. The aim of this study is to compare classification abilities of Landsat-9 and PRISMA satellite images while applying Support Vector Machine (SVM) algorithm to distinguish different LU/LC classes. For this purpose, the study area was chosen to be of heterogeneous character that includes industrial area, roads, residential area, airport, sea, forest, vegetation and barren land. When the classification results were visually examined, it was seen that forest, industrial area and airport classes were distinguished more accurately than other classes. On the other hand, qualitative results were validated with quantitative accuracy assessment results. The overall accuracy (OA) and Kappa coefficient values were calculated as 89.33 and 0.88 for Landsat-9 satellite image and as 92.33 and 0.91 for the PRISMA satellite image, respectively. In the accuracy assessment results, although Landsat-9 and PRISMA satellite images showed similar classification performances, a slight improvement was observed by using the PRISMA satellite image. The findings indicated that although both of the Landsat-9 and PRISMA satellite images were proper data to assess the LU/LC of the complex region, a slightly more performance could be achieved by using the PRISMA satellite image.
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Liang, Li, Qingsheng Liu, Gaohuan Liu, He Li, and Chong Huang. "Accuracy Evaluation and Consistency Analysis of Four Global Land Cover Products in the Arctic Region." Remote Sensing 11, no. 12 (June 12, 2019): 1396. http://dx.doi.org/10.3390/rs11121396.
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Land cover is a fundamental component of crucial importance in the earth sciences. To date, many excellent international teams have created a variety of land cover products covering the entire globe. To provide a reference for researchers studying the Arctic, this paper evaluates four commonly used land cover products. First, we compare and analyze the four land cover products from the perspectives of land cover type, distribution and spatial heterogeneity. Second, we evaluate the accuracy of such products by using two sets of sample points collected from the Arctic region. Finally, we obtain the spatial consistency distribution of the products by means of superposition analysis. The results show the following: (a) among the four land cover products, Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy (63.5%) in the Arctic region, GlobeLand30 has an overall accuracy of 62.2% and the overall accuracy of the Global Land Cover by the National Mapping Organization (GLCNMO) is only 48.8%. When applied in the Arctic region, the overall accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) is only 29.5% due to significant variances. Therefore, MODIS and GLCNMO are not recommended in Arctic-related research as their use may lead to major errors. (b) An evaluation of the consistency of the four products indicates that the classification of the large-scale homogeneous regions in the Arctic yields satisfactory results, whereas the classification results in the forest–tundra ecotone are unsatisfactory. The results serve as a reference for future research. (c) Among the four products, GlobeLand30 is the best choice for analyzing finely divided and unevenly distributed surface features such as waters, urban areas and cropland. Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy, and its classification accuracy is relatively higher for forests, shrubs, sparse vegetation, snow/ice and water. GlobeLand30 and CCI-LC do not vary much from each other in terms of overall accuracy. They differ the most in the classification accuracy of shrub-covered land; CCI-LC performed better than GlobeLand30 in the classification of shrub-covered land, whereas the latter obtained higher accuracy than that of the former in the classification of urban areas and cropland.
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Bufebo, Belayneh, and Eyasu Elias. "Effects of Land Use/Land Cover Changes on Selected Soil Physical and Chemical Properties in Shenkolla Watershed, South Central Ethiopia." Advances in Agriculture 2020 (July 28, 2020): 1–8. http://dx.doi.org/10.1155/2020/5145483.
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Land use change from natural ecosystems to managed agroecosystems is one of the main causes of soil fertility decline. Severe soil erosion caused by agricultural expansion and poor management worsened soil nutrient depletion in cultivated outfields (crop lands). This study was conducted to examine the effects of land use and land cover changes (LU/LC) on selected soil physicochemical properties in the Shenkolla watershed. A total of 40 top soil samples at 0–20 cm depth were collected from four land use/land cover types (forest land, grazing land, cultivated outfield, and cultivated homestead garden fields). The analysis of variance (ANOVA) was applied to determine differences in soil parameters among land use types. Treatment means comparison was determined using the least significant difference (LSD) at 0.05 level of significances. The result indicated that there were significant P<0.05 differences among the four LU/LC types for soil characteristics. For most parameters evaluated, the most favorable soil properties were observed in the forest land, followed by homestead garden fields, while the least favorable soil properties were found in intensively cultivated outfields. Increase in the extent of cultivated land at the expense of forest cover associated with poor management has promoted significant loss of soil quality in intensively cultivated outfields. Reducing the land cover conversion and adopting proper management practices of the soil commonly used in homestead garden fields are very crucial in order to improve soil fertility in intensively cultivated outfields.
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Jovanović, S., T. Predić, and G. Bratić. "ANALYSIS OF FREE AND OPEN LAND COVER MAPS FOR AGRICULTURAL LAND USE PLANNING AT LOCAL LEVEL." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 5, 2022): 237–43. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-237-2022.
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Abstract. According to the Law on Agricultural Land of the Republic of Srpska, municipalities and cities are obliged to prepare a planning document “Groundwork for Agricultural Land Protection, Use and Restructuring (The groundwork)”. Information related to the current state of land cover and land cover use are essential for the groundwork. Such layer was created for the municipality Laktaši in Bosnia and Herzegovina by digitization of land cover features from orthophoto imagery. Even if digitization provides highly reliable data, it is also time-consuming activity, and therefore the evaluation of Corine Land Cover (CLC) for the municipality Laktaši was performed to determine if it is accurate enough to sustain the groundwork for other municipalities. In this paper, using free open source programs, a comparison of two sets of data representing land cover was performed: manually vectorized data with orthophoto images of LC/LU and CLC. Using QGIS, the two datasets were harmonized, and then the error matrix and accuracy indexes were computed by using Python. The obtained results show that the overall accuracy of CLC with respect to LC/LU reference is 70%, but the class related to agricultural areas are overestimated in some locations and underestimated in other locations. After analyzing the results, it was concluded that the CLC in the studied area is not a sufficiently precise GIS basis for agricultural land use planning at the local level. However, it can be a good starting point for making of LC/LU, which would significantly shorten the time of its creating.
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Cui, Liu, Hui Yang, Liang Chu, Qingping He, Fei Xu, Yina Qiao, Zhaojin Yan, Ran Wang, and Hui Ci. "The Verification of Land Cover Datasets with the Geo-Tagged Natural Scene Images." ISPRS International Journal of Geo-Information 11, no. 11 (November 13, 2022): 567. http://dx.doi.org/10.3390/ijgi11110567.
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Land cover is important for global change studies, and its accuracy and reliability are usually verified by field sampling, which costs a lot. A method was proposed for the verification of land cover datasets with the geo-tagged natural scene images using a convolutional neural network. The nature scene images were firstly collected from the Land Use and Cover Area frame Survey (LUCAS) and global crowdsourcing images platform Flickr, then classified according to the Land Cover Classification System. The Nature Scene Image Classification (NSIC) model based on the GoogLeNet Inception network for recognition of natural scene images was then constructed. Finally, in the UK, as a verification area, the European Space Agency Climate Change Initiative Land Cover (ESA CCI-LC) datasets and the Global land-cover product with fine classification system (GLC-FCS) were verified using the NSIC-Inception model with the nature scene image set. The verification results showed that the overall accuracy verified by LUCAS was very close to the accuracy of the land cover product, which was 94.41% of CCI LC and 92.89% of GLC-FCS, demonstrating the feasibility of using geo-tagged images classified by the NSIC model. In addition, the VGG16 and ResNet50 were compared with GoogLeNet Inception. The differences in verification between LUCAS and Flickr images were discussed regarding the image’s quantity, the spatial distribution, the representativeness, and so on. The uncertainties of verification arising from differences in the spatial resolution of the different datasets were explored by CCI LC and GCL-FCS. The application of the method has great potential to support and improve the efficiency of land cover verification.
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Liu, Pengyu, Jie Pei, Han Guo, Haifeng Tian, Huajun Fang, and Li Wang. "Evaluating the Accuracy and Spatial Agreement of Five Global Land Cover Datasets in the Ecologically Vulnerable South China Karst." Remote Sensing 14, no. 13 (June 27, 2022): 3090. http://dx.doi.org/10.3390/rs14133090.
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Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.
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Vinayan, Midhuna, B. Gurugnanam, and S. Bairavi. "Land use/Land cover Change Detection Study in Vythiri Taluk, Wayanad District, Kerala using Geospatial Technology." Disaster Advances 15, no. 4 (March 25, 2022): 26–33. http://dx.doi.org/10.25303/1504da026033.
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Mapping and monitoring the land use/land cover (LU/LC) changes in the Vythiri taluk of Wayanad district are essential for sustainable improvement and management. Remote sensing and GIS technology are used to track the changes in land use/ land cover of Vythiri taluk of Wayanad district for the year 2015–2021. Images from Landsat-8 data were used to generate LU/LC maps. The maximum likelihood supervised classification approach was used to produce the signature class of the important land use/ land cover category. Six major land use/ land cover classes were noticed viz. agricultural land, barren land, built-up area, forest, grassland and water body. The land use/ land cover changes in the results show that built-up area and grassland have increased by 19% (115km2) and 25% (18.1km2), while agricultural land and barren land have decreased by 7% (39.4km2) and 15% (93.3km2) respectively. The area of forest land and water bodies has shown no changes. The overall accuracy of the study is 80%, 83.3% and 83.3% and Kappa Coefficient is 75.8%, 79.5%, 79.2% for the years 2015, 2018 and 2021 respectively. The analyses reveal that land use/ land cover is changed continuously due to population growth, urbanization and natural disasters like flooding and landslides. Google earth images are also used to detect the land cover changes in the study area. The outcome of the study is helpful to policymakers for sustainable land use/land cover management in the Vythiri taluk of Wayanad district, Kerala, South India.
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Bie, Qiang, Ying Shi, Xinzhang Li, and Yueju Wang. "Contrastive Analysis and Accuracy Assessment of Three Global 30 m Land Cover Maps Circa 2020 in Arid Land." Sustainability 15, no. 1 (December 31, 2022): 741. http://dx.doi.org/10.3390/su15010741.
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Fine-resolution land cover (LC) products are critical for studies of urban planning, global climate change, the Earth’s energy balance, and the geochemical cycle as fundamental geospatial data products. It is important and urgent to evaluate the performance of the updated global land cover maps. In this study, three widely used LC maps with 30 m spatial resolution (FROM-GLC30-2020, GLC_FCS30, and GlobeLand30) published around 2020 were evaluated in terms of their degree of consistency and accuracy metrics. First, we compared their similarities and difference in the area ratio and spatial patterns over different land cover types. Second, the sample and response protocol was proposed and validation samples were collected. Based on this, the overall accuracy, producer’s accuracy, and user’s accuracy were analyzed. The results revealed that: (1) the consistent areas of the three maps accounted for 65.96% of the total area and that two maps exceeded 75% of it. (2) The dominant land cover types, bare land and grassland, were the most consistent land cover types across the three products. In contrast, the spatial inconsistency of the wetland, shrubland, and built-up areas were relatively high, with the disagreement mainly occurring in the heterogeneous regions. (3) The overall accuracy of the GLC_FCS30 map was the highest with a value of 87.07%, which was followed by GlobeLand30 (85.69%) and FROM-GLC30 (83.49%). Overall, all three of the LC maps were found to be consistent and have a good performance in classification in the arid regions, but their ability to accurately classify specific types varied.
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van Duynhoven, Alysha, and Suzana Dragićević. "Exploring the Sensitivity of Recurrent Neural Network Models for Forecasting Land Cover Change." Land 10, no. 3 (March 10, 2021): 282. http://dx.doi.org/10.3390/land10030282.
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Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) architectures, have obtained successful outcomes in timeseries analysis tasks. While RNNs demonstrated favourable performance for Land Cover (LC) change analyses, few studies have explored or quantified the geospatial data characteristics required to utilize this method. Likewise, many studies utilize overall measures of accuracy rather than metrics accounting for the slow or sparse changes of LC that are typically observed. Therefore, the main objective of this study is to evaluate the performance of LSTM models for forecasting LC changes by conducting a sensitivity analysis involving hypothetical and real-world datasets. The intent of this assessment is to explore the implications of varying temporal resolutions and LC classes. Additionally, changing these input data characteristics impacts the number of timesteps and LC change rates provided to the respective models. Kappa variants are selected to explore the capacity of LSTM models for forecasting transitions or persistence of LC. Results demonstrate the adverse effects of coarser temporal resolutions and high LC class cardinality on method performance, despite method optimization techniques applied. This study suggests various characteristics of geospatial datasets that should be present before considering LSTM methods for LC change forecasting.
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Sheykhmousa, Mohammadreza, Norman Kerle, Monika Kuffer, and Saman Ghaffarian. "Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information." Remote Sensing 11, no. 10 (May 17, 2019): 1174. http://dx.doi.org/10.3390/rs11101174.
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Post-disaster recovery (PDR) is a complex, long-lasting, resource intensive, and poorly understood process. PDR goes beyond physical reconstruction (physical recovery) and includes relevant processes such as economic and social (functional recovery) processes. Knowing the size and location of the places that positively or negatively recovered is important to effectively support policymakers to help readjust planning and resource allocation to rebuild better. Disasters and the subsequent recovery are mainly expressed through unique land cover and land use changes (LCLUCs). Although LCLUCs have been widely studied in remote sensing, their value for recovery assessment has not yet been explored, which is the focus of this paper. An RS-based methodology was created for PDR assessment based on multi-temporal, very high-resolution satellite images. Different trajectories of change were analyzed and evaluated, i.e., transition patterns (TPs) that signal positive or negative recovery. Experimental analysis was carried out on three WorldView-2 images acquired over Tacloban city, Philippines, which was heavily affected by Typhoon Haiyan in 2013. Support vector machine, a robust machine learning algorithm, was employed with texture features extracted from the grey level co-occurrence matrix and local binary patterns. Although classification results for the images before and four years after the typhoon show high accuracy, substantial uncertainties mark the results for the immediate post-event image. All land cover (LC) and land use (LU) classified maps were stacked, and only changes related to TPs were extracted. The final products are LC and LU recovery maps that quantify the PDR process at the pixel level. It was found that physical and functional recovery can be mainly explained through the LCLUC information. In addition, LC and LU-based recovery maps support a general and a detailed recovery understanding, respectively. It is therefore suggested to use the LC and LU-based recovery maps to monitor and support the short and the long-term recovery, respectively.
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Khairul Amri Kamarudin, Mohd, Kabir Abdulkadir Gidado, Mohd Ekhwan Toriman, Hafizan Juahir, Roslan Umar, Noorjima Abd Wahab, Salisu Ibrahim, Suriyani Awang, and Khairul Nizam Abdul Maulud. "Classification of Land Use/Land Cover Changes Using GIS and Remote Sensing Technique in Lake Kenyir Basin, Terengganu, Malaysia." International Journal of Engineering & Technology 7, no. 3.14 (July 25, 2018): 12. http://dx.doi.org/10.14419/ijet.v7i3.14.16854.
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Geographical information system (GIS) techniques and Remote Sensing (RS) data are fundamental in the study of land use (LU) and land cover (LC) changes and classification. The aim of this study is to map and classify the LU and LC change of Lake Kenyir Basin within 40 years’ period (1976 to 2016). Multi-temporal Landsat images used are MSS 1976, 1989, ETM+ 2001 and OLI 8 2016. Supervised Classification on Maximum Likelihood Algorithm method was used in ArcGIS 10.3. The result shows three classes of LU and LC via vegetation, water body and built up area. Vegetation, which is the dominant LC found to be 100%, 88.83%, 86.15%, 81.91% in 1976, 1989, 2001 and 2016 respectively. While water body accounts for 0%, 11.17%, 12.36% and 13.62% in the years 1976, 1989, 2001 and 2016 respectively and built-up area 1.49% and 4.47 in 2001 and 2016 respectively. The predominant LC changes in the study are the water body and vegetation, the earlier increasing rapidly at the expense of the later. Therefore, proper monitoring, policies that integrate conservation of the environment are strongly recommended.
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Susanto, Slamet Arif, Heru Joko Budirianto, and Agatha Cecilia Maturbongs. "KATEGORI KONSERVASI VEGETASI TUMBUHAN BAWAH DI LAHAN BERA WOMNOWI DISTRIK SIDEY MANOKWARI PAPUA BARAT." VOGELKOP: Jurnal Biologi 2, no. 1 (September 8, 2020): 1–10. http://dx.doi.org/10.30862/vogelkopjbio.v2i1.13.
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Trees vegetation are obviously dominate at the old fallow lands of Papua Indonesian. Fallow lands in the edge of primary forest is generally at Sidey District Manokwari. The purpose of this study is to determinate understory cover vegetation conservation based list of IUCN at the fallow land Womnowi Sidey Manokwari. An inventory of vegetation has done using analysis of vegetation―continuous line sampling technique, 2 x 2 meters for sampling seedlings and understory non-woody plant cover and 5 x 5 for saplings. At one hectare fallow land we found 1482 an individual of 122 species understory cover, only 158 an individual of 22 species had entered in IUCN redlist. Species with status least concern (LC) are dominate (>80%) compare with status data deficient (DD), near threatened (NT), and vulnerable (V). The important value index (IVI) of species on list IUCN showing 22.60% at seedlings and non-woody understory cover and 19.81% at the saplings phase. Aglaia odorata Lour.(seedling and sapling) is LC category, Intsia bijuga (Colebr.) Kuntze (seedling) V category, and Pandanus tectorius var., uapensis (non-woody plant) DD category, each species is the only one. The further study should be more intensive compare primary forest and old fallow lands of Papua―the conservation list of understory cover vegetation is lowest, so we conclude this is obviously understory vegetation at old fallow lands.Key word: fallow land, conservation, Sidey, understory, analysis of vegetation
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Said, Mateso, Canute Hyandye, Ibrahimu Chikira Mjemah, Hans Charles Komakech, and Linus Kasian Munishi. "Evaluation and Prediction of the Impacts of Land Cover Changes on Hydrological Processes in Data Constrained Southern Slopes of Kilimanjaro, Tanzania." Earth 2, no. 2 (May 30, 2021): 225–47. http://dx.doi.org/10.3390/earth2020014.
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This study provides a detailed assessment of land cover (LC) changes on the water balance components on data constrained Kikafu-Weruweru-Karanga (KWK) watershed, using the integrated approaches of hydrologic modeling and partial least squares regression (PLSR). The soil and water assessment tool (SWAT) model was validated and used to simulate hydrologic responses of water balance components response to changes in LC in spatial and temporal scale. PLSR was further used to assess the influence of individual LC classes on hydrologic components. PLSR results revealed that expansion in cultivation land and built-up area are the main attributes in the changes in water yield, surface runoff, evapotranspiration (ET), and groundwater flow. The study findings suggest that improving the vegetation cover on the hillside and abandoned land area could help to reduce the direct surface runoff in the KWK watershed, thus, reducing flooding recurring in the area, and that with the ongoing expansion in agricultural land and built-up areas, there will be profound negative impacts in the water balance of the watershed in the near future (2030). This study provides a forecast of the future hydrological parameters in the study area based on changes in land cover if the current land cover changes go unattended. This study provides useful information for the advancement of our policies and practices essential for sustainable water management planning.
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Tiwari, Jagriti, S. K. Sharma, and R. J. Patil. "Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin." Current World Environment 12, no. 3 (December 25, 2017): 678–84. http://dx.doi.org/10.12944/cwe.12.3.19.
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The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.
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Gupta, Garima, R. S. Yadav, and Deepak Maurya. "Decomposition and Nitrogen Dynamics of Tree Pruned Biomass Under Albizia Procera Based Agroforestry System in Semi Arid Region of Bundelkhand, India." Current World Environment 12, no. 3 (December 25, 2017): 725–33. http://dx.doi.org/10.12944/cwe.12.3.24.
Full textAbstract:
The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.
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Wang, Libo, Paul Bartlett, Darren Pouliot, Ed Chan, Céline Lamarche, Michael A. Wulder, Pierre Defourny, and Mike Brady. "Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada." Remote Sensing 11, no. 19 (September 30, 2019): 2286. http://dx.doi.org/10.3390/rs11192286.
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Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work.
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Der Sarkissian, Rita, Mario J. Al Sayah, Chadi Abdallah, Jean-Marc Zaninetti, and Rachid Nedjai. "Land Use Planning to Reduce Flood Risk: Opportunities, Challenges and Uncertainties in Developing Countries." Sensors 22, no. 18 (September 14, 2022): 6957. http://dx.doi.org/10.3390/s22186957.
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Land use planning for flood risk reduction has been significantly addressed in literature. However, a clear methodology for flood mitigation oriented land-use planning and its implementation, particularly in developing countries like Lebanon, is still missing. Knowledge on land use planning is still in its earliest stages in Lebanon. A lack of hazard-informed land use planning coupled to random land cover pattern evolution characterize the country. In response, this study focuses on the opportunities, challenges and uncertainties resulting from the integration of land use planning into efficient Disaster Risk Reduction (DRR). For this purpose, GIS-based analyses were first conducted on the current land use/land cover (LU/LC) of the Assi floodplain. Then, the areas land cover was retraced and its evolution after several flood occurrences was assessed. Subsequently, a flood hazard-informed LU/LC plan was proposed. The latter is mainly based on the spatial allocation of land-uses with respect to different flood hazard levels. This approach resulted in the production of a land use planning matrix for flood risk reduction. The matrix approach can serve as a tool for designing sustainable and resilient land cover patterns in other similar contexts while simultaneously providing robust contributions to decision-making and risk communication.
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KC, Aman, Nimisha Wagle, and Tri Dev Acharya. "Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020." ISPRS International Journal of Geo-Information 10, no. 10 (September 23, 2021): 635. http://dx.doi.org/10.3390/ijgi10100635.
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Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agriculture, water, and forest classes had been subjected to fluctuations with barren land in the decreasing trend. This alteration in the area of the LC classes also resulted in varying ESVs for individual land cover and total values for the years. The accuracy for the RF classifier was under substantial agreement for such fragmented LCs. Using LC, CoG, and ESV, the paper discusses the need for spatiotemporal analysis studies in Nepal to overcome the current limitations and later expansion to other regions. Studies such as these help in implementing proper plans and strategies by district administration offices and local governmental bodies to stop the exploitation of resources.
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Belcore, Elena, Marco Piras, and Alessandro Pezzoli. "Land Cover Classification from Very High-Resolution UAS Data for Flood Risk Mapping." Sensors 22, no. 15 (July 27, 2022): 5622. http://dx.doi.org/10.3390/s22155622.
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Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping difficulties due to the high spectral homogeneity of cover types. The training-test dataset was manually defined. The segmentation resulted in an F1_score of 0.70 and a median Jaccard index of 0.88. The RF model performed with an overall accuracy of 0.94, with the grasslands and the rocky clustered areas classes the least performant.
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Bachantourian, Margarita, Kyriakos Chaleplis, Alexandra Gemitzi, Kostas Kalabokidis, Palaiologos Palaiologou, and Christos Vasilakos. "Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape." Land 11, no. 9 (September 1, 2022): 1453. http://dx.doi.org/10.3390/land11091453.
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Land cover can reflect global environmental changes if their associated transitions are quantitatively and correctly analysed, thus helping to assess the drivers and impacts of climate change and other applied research studies. It is highly important to acquire accurate spatial land cover information to perform multidisciplinary analyses. This work aims at estimating the accuracy of three widely used land cover products, the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1), the European Space Agency Climate Change Initiative land cover (ESA-CCI-LC), and the EU CORINE land cover (CLC), all for the reference year of 2018, by comparing them against a fine resolution land cover dataset created for this study with combined ground surveys and high-resolution Large Scale Orthophotography (LSO 25/2015). Initially, the four datasets had their land cover classes harmonized and all were resampled to the same spatial resolution. The accuracy metrics used to conduct the comparisons were Overall Accuracy, Producer’s Accuracy, User’s Accuracy, and the Kappa Coefficient. Comparisons with the reference dataset revealed an underestimation of the forested areas class in all three compared products. Further analysis showed that the accuracy metrics were reasonably high for the broad classes (forest vs. non-forest), with an overall accuracy exceeding 70% in all examined products. On the contrary, in the detailed classification (total land cover mapping), the comparison of the reference dataset with the three land cover products highlighted specific weaknesses in the classification results of the three products, showing that CLC depicted more precisely the landscape characteristics than the two other products, since it demonstrated the highest overall accuracy (37.47%), while MODIS and ESA-CCI-LC revealed a percentage that did not exceed 22%.
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Hua, Ting, Wenwu Zhao, Yanxu Liu, Shuai Wang, and Siqi Yang. "Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO." Remote Sensing 10, no. 11 (November 21, 2018): 1846. http://dx.doi.org/10.3390/rs10111846.
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Numerous global-scale land-cover datasets have greatly contributed to the study of global environmental change and the sustainable management of natural resources. However, land-cover datasets inevitably experience information loss because of the nature of the uncertainty in the interpretation of remote-sensing images. Therefore, analyzing the spatial consistency of multi-source land-cover datasets on the global scale is important to maintain the consistency of time and consider the effects of land-cover changes on spatial consistency. In this study, we assess the spatial consistency of five land-cover datasets, namely, GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO, at the global and continental scales through climate and elevation partitions. The influencing factors of surface conditions and data producers on the spatial inconsistency are discussed. The results show that the global overall consistency of the five datasets ranges from 49.2% to 67.63%. The spatial consistency of Europe is high, and the multi-year value is 66.57%. In addition, the overall consistency in the EF climatic zone is very high, around 95%. The surface conditions and data producers affect the spatial consistency of land-cover datasets to different degrees. CCI LC and GLCNMO (2013) have the highest overall consistencies on the global scale, reaching 67.63%. Generally, the consistency of these five global land-cover datasets is relatively low, increasing the difficulty of satisfying the needs of high-precision land-surface-process simulations.
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Mukaetov, Dusko Mukaetov, Ivan Blinkov, and Hristina Poposka. "DYNAMIC OF LAND DEGRADATION NEUTRALITY BASELINE INDCATORS IN THE REPUBLIC OF MACEDONIA." Contributions, Section of Natural, Mathematical and Biotechnical Sciences 40, no. 1 (June 10, 2019): 39. http://dx.doi.org/10.20903/csnmbs.masa.2019.40.1.130.
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Land degradation neutrality (LDN) is defined as a "state whereby the amount and quality of land resources nec-essary to support ecosystem functions and services and enhance food security remain stable or increase within specified temporal and spatial scales and ecosystems". The baseline is expressed as the initial (t0) estimated value of each of the three indicators, used as proxies of land-based natural capital and the ecosystem services that flow from that land base: land cover/land use change, land productivity status and trends, soil organic carbon status and trends. The baseline of LDN was calculated with estimation of the average values across the 10 years baseline period of the following indica-tors: Land Cover/Land Cover change (LC/LCC), Land Productivity Dynamics (LPD) and Soil Organic Carbon (SOC). Three tier approaches for computation of the selected indicators were used: Tier 1: Global/regional Earth observation, geospatial information and modelling; Tier 2: National statistics (only for LC/LCC) and Tier 3: Field survey. Most sig-nificant changes in LC for the period 2000/2012 are in the categories of Forest land and Shrubs/grasslands. According the global data sets used for analysis of LPD, the total affected area with depletion of Land productivity for the period 2000/2010 is identified on a only 2.35 % of the country territory. The available global data sets gives a model SOC lev-els for the period 2000/2010. According these data, the total loss of SOC in our country is estimated on 3951 t.
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Mallupattu, Praveen Kumar, and Jayarama Reddy Sreenivasula Reddy. "Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India." Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/268623.
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Land use/land cover (LU/LC) changes were determined in an urban area, Tirupati, from 1976 to 2003 by using Geographical Information Systems (GISs) and remote sensing technology. These studies were employed by using the Survey of India topographic map 57 O/6 and the remote sensing data of LISS III and PAN of IRS ID of 2003. The study area was classified into eight categories on the basis of field study, geographical conditions, and remote sensing data. The comparison of LU/LC in 1976 and 2003 derived from toposheet and satellite imagery interpretation indicates that there is a significant increase in built-up area, open forest, plantation, and other lands. It is also noted that substantial amount of agriculture land, water spread area, and dense forest area vanished during the period of study which may be due to rapid urbanization of the study area. No mining activities were found in the study area in 1976, but a small addition of mining land was found in 2003.
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Wang, Hui, Xiaojin Wen, Yijia Wang, Liping Cai, Da Peng, and Yanxu Liu. "China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC." Remote Sensing 13, no. 3 (January 20, 2021): 341. http://dx.doi.org/10.3390/rs13030341.
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New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCFMCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.
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Nivedita Priyadarshini, K., M. Kumar, S. A. Rahaman, and S. Nitheshnirmal. "A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 665–70. http://dx.doi.org/10.5194/isprs-archives-xlii-5-665-2018.
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<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>
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Leinenkugel, Patrick, Ramona Deck, Juliane Huth, Marco Ottinger, and Benjamin Mack. "The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification." Remote Sensing 11, no. 19 (September 27, 2019): 2249. http://dx.doi.org/10.3390/rs11192249.
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This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%.
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Adegbola, P. A., J. R. Adewumi, and O. A. Obiora-Okeke. "Application of Markov Chain Model and ArcGIS in Land Use Projection of Ala River Catchment, Akure, Nigeria." Nigerian Journal of Technological Development 18, no. 1 (June 24, 2021): 30–38. http://dx.doi.org/10.4314/njtd.v18i1.5.
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Increase land use change is one of the consequences of rapid population growth of cities in developing countries with its negative consequences on the environment. This study generates previous and present land use of Ala watershed and project the future land use using Markov chain model and ArcGIS software (version 10.2.1). Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 used to generate land use (LU) and land cover (LC) images for the years 2000, 2010 and 2019. Six LU/LC classes were considered as follows: developed area (DA), open soil (OS), grass surface (GS), light forest (LF), wetland (WL) and hard rock (HR). Markov chain analysis was used in predicting LU/LC types in the watershed for the years 2029 and 2039. The veracity of the model was tested with Nash Sutcliffe Efficiency index (NSE) and Percent Bias methods. The model results show that the study area is growing rapidly particularly in the recent time. This urban expansion results in significant decrease of WL coverage areas and the significant increase of DA. This implies reduction in the available land for dry season farming and incessant flood occurrence.
Keywords: Land cover, land use change, Markov chain, ArcGIS, watershed, urbanization
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Obiefuna, J. N., P. C. Nwilo, C. J. Okolie, E. I. Emmanuel, and O. Daramola. "Dynamics of Land Surface Temperature in Response to Land Cover Changes in Lagos Metropolis." October 2018 2, no. 2 (October 2018): 148–59. http://dx.doi.org/10.36263/nijest.2018.02.0074.
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Land Surface Temperature (LST) is one of the key environmental parameters affected by land cover change. Lagos State has been experiencing an increase in surface temperature due to growing areas of impervious surfaces caused by anthropogenic urban sprawl. While the change in LST has been established, its continuous monitoring and relationship with continuing Land Cover (LC) changes have become imperative for appropriate management and policy actions. This study investigated the effect of land cover change on LST in the rapidly urbanising Lagos metropolis. Using spatio-temporal Landsat imageries with their thermal bands and ancillary data, land cover and LST changes were assessed from 1984 - 2015. The spatial patterns of LST and LC were derived to examine the response of LST to urban growth. Findings confirmed urban sprawl in previously rural areas northward of the metropolis in LGAs such as Ikorodu, Kosofe and those fringing the state’s border with Ogun State. This also confirmed new growth areas as occurring west of the metropolis in Amuwo-Odofin LGA. The results further showed that the rapid urbanisation in Lagos metropolis has altered the surface thermal environment as indicated by increased LST. Built-up area and bare land accounted for the highest increase in LST (as high as 1.5℃ in some areas) while wetlands and other vegetated areas played a vital role in moderating the surface temperature in areas they still occupy. This provides reasonable evidence for the appropriate authorities to institute requisite policies and actions towards moderating urban sprawl while ramping up the development of urban green infrastructure to counter global warming.
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Ma, L., Z. Chen, Y. Li, D. Zhang, J. Li, and M. A. Chapman. "MULTISPECTRAL AIRBORNE LASER SCANNING POINT-CLOUDS FOR LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 79–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-79-2019.
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<p><strong>Abstract.</strong> This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.</p>
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Bratic, G., S. Peng, and M. A. Brovelli. "BENCHMARKING OF HIGH-RESOLUTION LAND COVER MAPS IN AFRICA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 707–14. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-707-2020.
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Abstract. This paper addresses the issue of increased validation demands due to growth in the production of land cover (LC) maps, especially those with large coverage and high-resolution. The inter-comparison of two high-resolution LC (HRLC) maps – GlobeLand30 for the year 2015 (GL30-2015) and S2 Prototype LC 20m map of Africa for 2016 (CCI Africa Prototype) – was done to estimate the degree to which they share the information, as this can serve as a benchmark of their accuracy. Since the two maps compared are independently classified, there is a higher probability that areas where they share information are correctly classified. CCI Africa Prototype and GL30- 2015 have not been yet validated for whole Africa and therefore benchmark accuracy can be used to better design the validation and to make it more efficient. Based on the pixel-by-pixel comparison of GL30-2015 and CCI Africa Prototype, the error matrix and accuracy indexes (Overall, User’s and Producer’s accuracy) were derived. Overall accuracy on the continent level is estimated to be around 66%, which is not considered satisfactory. The low value of overall accuracy is mostly due to the low accuracy of classes Shrubland, Wetland, and Permanent ice and snow, as their User’s and Producer’s accuracies are below 0.4. On the opposite, benchmark accuracy is fairly high for Forest (0.68), Water bodies (0.86) and Bareland (0.93). Nevertheless, class benchmark accuracies are different from country to country, so as the Overall accuracy. Benchmark accuracy was not estimated for Cultivated, Grassland and Artificial surface classes due to the large difference between User’s and Producer’s accuracies.
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Kumar, P., S. Ravindranath, and K. G. Raj. "OBJECT ORIENTED CLASSIFICATION AND FEATURE EXTRACTION FOR PARTS OF EAST DELHI USING HYBRID APPROACH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 749–54. http://dx.doi.org/10.5194/isprs-archives-xlii-5-749-2018.
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<p><strong>Abstract.</strong> Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.</p>
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Ayele, Gebiaw T., Ayalkibet M. Seka, Habitamu Taddese, Mengistu A. Jemberrie, Christopher E. Ndehedehe, Solomon S. Demissie, Joseph L. Awange, Jaehak Jeong, David P. Hamilton, and Assefa M. Melesse. "Relationship of Attributes of Soil and Topography with Land Cover Change in the Rift Valley Basin of Ethiopia." Remote Sensing 14, no. 14 (July 6, 2022): 3257. http://dx.doi.org/10.3390/rs14143257.
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Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and transport, thereby affecting the entire basin hydrology. In this study, we analyzed watershed-scale processes in the study area, where agriculture and natural shrub land are the dominant LCs. The objective of this study was to assess the time series and spatial patterns of LCC using remotely-sensed data from 1973 to 2018, for which we used six snapshots of satellite images. The LC distribution in relation to watershed characteristics such as topography and soils was also evaluated. For LCC detection analysis, we used Landsat datasets accessed from the United States Geological Survey (USGS) archive, which were processed using remote sensing and Geographic Information System (GIS) techniques. Using these data, four major LC types were identified. The findings of an LC with an overall accuracy above 90% indicates that the area experienced an increase in agricultural LC at the expense of other LC types such as bushland, grazing land, and mixed forest, which attests to the semi-continuous nature of deforestation between 1973 and 2018. In 1973, agricultural land covered only 10% of the watershed, which later expanded to 48.4% in 2018. Bush, forest, and grazing land types, which accounted for 59.7%, 16.7%, and 13.5% of the watershed in 1973, were reduced to 45.2%, 2.3%, and 4.1%, respectively in 2018. As a result, portions of land areas, which had once been covered by pasture, bush, and forest in 1973, were identified as mixed agricultural systems in 2018. Moreover, spatial variability and distribution in LCC is significantly affected by soil type, fertility, and slope. The findings showed the need to reconsider land-use decision tradeoffs between social, economic, and environmental demands.
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Rousset, Guillaume, Marc Despinoy, Konrad Schindler, and Morgan Mangeas. "Assessment of Deep Learning Techniques for Land Use Land Cover Classification in Southern New Caledonia." Remote Sensing 13, no. 12 (June 9, 2021): 2257. http://dx.doi.org/10.3390/rs13122257.
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Land use (LU) and land cover (LC) are two complementary pieces of cartographic information used for urban planning and environmental monitoring. In the context of New Caledonia, a biodiversity hotspot, the availability of up-to-date LULC maps is essential to monitor the impact of extreme events such as cyclones and human activities on the environment. With the democratization of satellite data and the development of high-performance deep learning techniques, it is possible to create these data automatically. This work aims at determining the best current deep learning configuration (pixel-wise vs. semantic labelling architectures, data augmentation, image prepossessing, …), to perform LULC mapping in a complex, subtropical environment. For this purpose, a specific data set based on SPOT6 satellite data was created and made available for the scientific community as an LULC benchmark in a tropical, complex environment using five representative areas of New Caledonia labelled by a human operator: four used as training sets, and the fifth as a test set. Several architectures were trained and the resulting classification was compared with a state-of-the-art machine learning technique: XGboost. We also assessed the relevance of popular neo-channels derived from the raw observations in the context of deep learning. The deep learning approach showed comparable results to XGboost for LC detection and over-performed it on the LU detection task (61.45% vs. 51.56% of overall accuracy). Finally, adding LC classification output of the dedicated deep learning architecture to the raw channels input significantly improved the overall accuracy of the deep learning LU classification task (63.61% of overall accuracy). All the data used in this study are available on line for the remote sensing community and for assessing other LULC detection techniques.
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Smaliychuk, A. "Geoecological analysis of modern landscape dynamic of the low mountain Ukrainian Carpathians." Visnyk of the Lviv University. Series Geography 2, no. 40 (December 12, 2012): 152–62. http://dx.doi.org/10.30970/vgg.2012.40.2099.
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This paper analyses change of land cover, as central component of biogenic geoecosystems (GES), with respect to the natural landscape structure, proximity to roads and settlements and land use structure. Five model municipalities were selected in the low mountain Ukrainian Carpathians. The land cover pattern of 1970–80s was digitized from the topographic maps, while its recent change was manually detected using high resolution images. After the natural GES data were overlaid with actual land cover the biogenic GES were distinguished. Six types of LC change were detected in this study. A common trend ion study areas is the increase of the forested area owing to forest succession on former agricultural land. Key words: Ukrainian Carpathians, geoecosystems, land cover, modern dynamic, GIS.
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Mohanrajan, Sam Navin, and Agilandeeswari Loganathan. "Novel Vision Transformer–Based Bi-LSTM Model for LU/LC Prediction—Javadi Hills, India." Applied Sciences 12, no. 13 (June 23, 2022): 6387. http://dx.doi.org/10.3390/app12136387.
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Continuous monitoring and observing of the earth’s environment has become interactive research in the field of remote sensing. Many researchers have provided the Land Use/Land Cover information for the past, present, and future for their study areas around the world. This research work builds the Novel Vision Transformer–based Bidirectional long-short term memory model for predicting the Land Use/Land Cover Changes by using the LISS-III and Landsat bands for the forest- and non-forest-covered regions of Javadi Hills, India. The proposed Vision Transformer model achieves a good classification accuracy, with an average of 98.76%. The impact of the Land Surface Temperature map and the Land Use/Land Cover classification map provides good validation results, with an average accuracy of 98.38%, during the process of bidirectional long short-term memory–based prediction analysis. The authors also introduced an application-based explanation of the predicted results through the Google Earth Engine platform of Google Cloud so that the predicted results will be more informative and trustworthy to the urban planners and forest department to take proper actions in the protection of the environment.
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Ambrose, S. M., and S. M. Sterling. "Global patterns of annual actual evapotranspiration with land-cover type: knowledge gained from a new observation-based database." Hydrology and Earth System Sciences Discussions 11, no. 10 (October 31, 2014): 12103–35. http://dx.doi.org/10.5194/hessd-11-12103-2014.
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Abstract. The process of evapotranspiration (ET) plays a critical role in the earth system, driving key land-surface processes in the energy, water and carbon cycles. Land-cover (LC) exerts multiple controls on ET, yet the global distribution of ET by LC and the related physical variables are poorly understood. The lack of quantitative understanding of global ET variation with LC begets considerable uncertainties regarding how ET and key land-surface processes will change alongside ongoing anthropogenic LC transformations. Here we apply statistical analysis and models to a new global ET database to advance our understanding of how annual actual ET varies with LC type. We derive global fields for each LC using linear mixed effect models (LMMs) that use geographical and meteorological variables as possible independent regression variables. Our inventory of ET observations reveals important gaps in spatial coverage that overlie hotpots of global change. There is a spatial bias of observations towards the mid latitudes, and LCs with large areas in the high latitudes (lakes, wetlands and barren land) are poorly represented. From the distribution of points as well as the uncertainty analysis completed by bootstrapping we identify high priority regions in need of more data collection. Our analysis of the new database provides new insights into how ET varies globally, providing more robust estimates of global ET rates for a broad range of LC types. Results reveal that different LC types have distinct global patterns of ET. Furthermore, zonal ET means among LCs reveal new patterns: ET rates in low latitudinal bands are more sensitive to LC change than in higher latitude bands; LCs with a higher evaporation component show higher variability of ET at the global scale; and LCs with dispersed rather than contiguous global locations have a higher variability of ET at the global scale. Results from this study indicate two major advancements are required to improve our ability to predict how ET will vary with global change. First, further collection of ground truth observations of ET is needed to fill gaps in LC types and spatial location identified in this paper. Second, LC types need to be de-aggregated into finer categories to better characterize ET, to reduce uncertainty and weakened strength to predictor variables, associated by aggregation of heterogeneous LC types into one group; this will require the development of higher-resolution LC databases.
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Rahman, Munshi K., Thomas W. Schmidlin, Mandy J. Munro-Stasiuk, and Andrew Curtis. "Geospatial Analysis of Land Loss, Land Cover Change, and Landuse Patterns of Kutubdia Island, Bangladesh." International Journal of Applied Geospatial Research 8, no. 2 (April 2017): 45–60. http://dx.doi.org/10.4018/ijagr.2017040104.
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This study utilizes geospatial tools of remote sensing, geographical information systems (GIS), and global positioning system (GPS) to examine the land loss, land cover (LC) change, landuse of Kutubdia Island, Bangladesh. Multi-spectral Scanner (MSS), Thematic Mapper (TM), and Landsat8 OLI imageries were used for land cover change. For assessing the landuse patterns of 2012, spatial video data were collected by using contour GPS camera. Using remote sensing analysis three different land cover classes (water, trees and forest, and agriculture) were identified and land cover changes were detected from 1972 to 2013. The results show from 1972 to 2013, an estimated 9 km2 of land has been lost and significant changes have taken place from 1972 to 2013. Only an estimated .35 km2 area of accretion has taken place during the study period. Using GIS eight different landuse patterns were identified based on spatial video data.
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Majasalmi, Titta, Stephanie Eisner, Rasmus Astrup, Jonas Fridman, and Ryan M. Bright. "An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data." Biogeosciences 15, no. 2 (January 18, 2018): 399–412. http://dx.doi.org/10.5194/bg-15-399-2018.
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Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.