Статті в журналах: "Monitoring of Land cover"

1

Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.

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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

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2

Stefanov, William L., Michael S. Ramsey, and Philip R. Christensen. "Monitoring urban land cover change." Remote Sensing of Environment 77, no. 2 (August 2001): 173–85. http://dx.doi.org/10.1016/s0034-4257(01)00204-8.

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3

Skelsey, C., A. N. R. Law, M. Winter†, and J. R. Lishman. "A system for monitoring land cover." International Journal of Remote Sensing 24, no. 23 (January 2003): 4853–69. http://dx.doi.org/10.1080/0143116031000101585.

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4

Manakos, Ioannis, Garik Gutman, and Chariton Kalaitzidis. "Monitoring Land Cover Change: Towards Sustainability." Land 10, no. 12 (December 9, 2021): 1356. http://dx.doi.org/10.3390/land10121356.

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In 2015, the United Nations member states adopted the 2030 Agenda, within which the 17 Sustainable Development Goals (SDGs) were established, with many of these goals calling for further research into sustainability [...]

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5

Cieślak, Iwona, Karol Szuniewicz, Katarzyna Pawlewicz, and Szymon Czyża. "Land Use Changes Monitoring with CORINE Land Cover Data." IOP Conference Series: Materials Science and Engineering 245 (October 2017): 052049. http://dx.doi.org/10.1088/1757-899x/245/5/052049.

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6

Potapov, Peter, Matthew C. Hansen, Indrani Kommareddy, Anil Kommareddy, Svetlana Turubanova, Amy Pickens, Bernard Adusei, Alexandra Tyukavina, and Qing Ying. "Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping." Remote Sensing 12, no. 3 (January 29, 2020): 426. http://dx.doi.org/10.3390/rs12030426.

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The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.

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7

Congalton, Russell G. "Mapping and Monitoring Forest Cover." Forests 12, no. 9 (September 1, 2021): 1184. http://dx.doi.org/10.3390/f12091184.

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Luo, H., B. He, X. Kuai, Y. Li, and R. Z. Guo. "LAND COVER EXTRACTION OF COASTAL AREA FROM GF-1 WFV IMAGERY USING ONTOLOGICAL METHOD." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 53–58. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-53-2020.

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Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.

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9

Zerrouki, Nabil, Fouzi Harrou, and Ying Sun. "Statistical Monitoring of Changes to Land Cover." IEEE Geoscience and Remote Sensing Letters 15, no. 6 (June 2018): 927–31. http://dx.doi.org/10.1109/lgrs.2018.2817522.

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10

Aryal, Rajaram. "National Land Cover Monitoring System for Nepal." Banko Janakari 32, no. 1 (May 31, 2022): 1–2. http://dx.doi.org/10.3126/banko.v32i1.45429.

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11

Lambin, Eric F. "Modelling and monitoring land-cover change processes in tropical regions." Progress in Physical Geography: Earth and Environment 21, no. 3 (September 1997): 375–93. http://dx.doi.org/10.1177/030913339702100303.

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Transformations in terrestrial ecosystems are increasingly regarded as an important element of global change. Quantitative data on where, when and why land-cover changes take place globally are still incomplete. This article reviews recent approaches to the monitoring and modelling of deforestation and dryland degradation in tropical regions. The review highlights the requirement to tailor the investigation method to the specific research question of interest. Different techniques to monitor land-cover changes at regional scales are analysed. The following modelling scenarios are discussed and illustrated by specific studies: projection of future land- cover changes with descriptive models, explanation of land-cover changes with empirical models, projection of future spatial patterns of changes with spatial statistical models, test of scenarios on future changes in land-cover with dynamic ecosystem models, and design of policy interventions with economic models. The article stresses the needs for a better integration of social science knowledge in land-cover change models and for a comprehensive theory of land-use changes.

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12

Margono, Belinda Arunarwati, Ahmad Basyirudin Usman, Budiharto ., and Ruandha Agung Sugardiman. "Indonesia’s Forest Resource Monitoring." Indonesian Journal of Geography 48, no. 1 (August 2, 2016): 7. http://dx.doi.org/10.22146/ijg.12496.

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Forest cover in term of distribution, extent and types, is major information required to manage the forest resources. Notably for Indonesia, which covers by approximately 98 Mha (>50%) forests, consist of 93 Mha (49.6%) natural forest and 5 Mha (2.6%) plantations forest. The forests are invaluable, including significantly preserve carbon, maintain unique biodiversity, support water and mineral cycle, as well as support local and global community. Here we report efforts have been made for years in the Ministry of Forestry for providing land cover information. Those efforts are including early development, data sources selection, method employed, techniques, and classification scheme, as well as problem encountered and approach for improvements.

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13

Mugo, Robinson, Rose Waswa, James W. Nyaga, Antony Ndubi, Emily C. Adams, and Africa I. Flores-Anderson. "Quantifying Land Use Land Cover Changes in the Lake Victoria Basin Using Satellite Remote Sensing: The Trends and Drivers between 1985 and 2014." Remote Sensing 12, no. 17 (September 1, 2020): 2829. http://dx.doi.org/10.3390/rs12172829.

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The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches.

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14

Hamdi, Halah Qahtan, and Zehraa Najim Abdul-Ameer. "Monitoring Land Change of Cover in Al-Rusafa District In Baghdad City by using Remote Sensing and GIS Techniques." Journal of Physics: Conference Series 2114, no. 1 (December 1, 2021): 012014. http://dx.doi.org/10.1088/1742-6596/2114/1/012014.

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Abstract Change detection of land surface is critical to execute precise data about territory of study for any sorts of arranging improvement. Technologies of Remote Sensing and GIS and have accomplished incredible strides to tackle the investigation issues like changes of land cover. The point of that study is to deliver guides of land front of Al-Rusafa District on year 2000, 2018 to screen the potential the expectable changes especially in vegetation land and metropolitan or built land, furthermore, identify the cycle of city settlement. Two multi-transient satellite picture information, Upgraded Topical Mapper picture information from 2000 and OLI Satellite picture information from 2018 were utilized in that task. That study direction is the major approach of classification approach to supply divided maps, and cover of land categories were recognized and map. Spectral indices (NDVI, NDBI, NDWI) utilized to identify the changes of expanding and diminishing land the change detection in (Arc Map 10.5 ) Envision was utilized to identify the urban development and the concentrated alters encompassing the urban regions. Cellular automata of Markov was utilized to mimic the patterns of land utilize and change of cover the period of 2000 to 2018 cross-tabulation lattices between diverse stages were delivered to interpret the patterns of change of covers from one cover land to another, these measurement information straight forwardly regions communicated the alter of land cover. The results about appear these demonstrate that around (31.8 %) of Change from one Kind of land cover to another one though around (68.2 %) of the region Remained unaltered between (2000, and 2018)..

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15

Y. Jamal, Assist Prof Dr Saleem. "Use of Remote Sensing and Geographic Information System for the Classification of Agricultural Land Uses and Land Cover in the Al-Sad Al-Adhim sub District – Iraq." ALUSTATH JOURNAL FOR HUMAN AND SOCIAL SCIENCES 225, no. 2 (September 1, 2018): 245–73. http://dx.doi.org/10.36473/ujhss.v225i2.151.

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Land use refers to the human activity associated with a particular area of land. The land cover refers to the pattern of appearances located on the surface of the earth. Survey, inventory, monitoring and classification of land use and land cover are a fundamental step in the land use planning process, in evaluating and comparing alternatives and in choosing the best and sustainable use of land for development, accomplishment economic and social well-being. Remote sensing and Geographic Information System provided advantages that conventional methods could not provide for surveys and monitoring of natural and human resources, and classification of agricultural land uses and land cover in the area of the Al-Sad Al-Adhim sub District – Iraq. Depending on the Anderson system and others to classify land uses and land cover, through the integration of digital interpretation with the use of Digital Image Processing (ERDAS IMAGINE) software, and visual interpretation using ArcGIS software. Classification of agricultural land use and land cover up to the third level, with over all accuracy of the map 90%. the percentage distribution of the areas shows that the agricultural lands ranked first and occupy 52%, then grassland occupies 19%, barren land is occupied 17%, urban areas and built up occupy 9% water is ranked last occupy 3% of the total area of the study area.

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16

Lin, Lili, Zhenbang Hao, Christopher J. Post, Elena A. Mikhailova, Kunyong Yu, Liuqing Yang, and Jian Liu. "Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine." Applied Sciences 10, no. 20 (October 20, 2020): 7336. http://dx.doi.org/10.3390/app10207336.

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Island ecosystems are particularly susceptible to climate change and human activities. The change of land use and land cover (LULC) has considerable impacts on island ecosystems, and there is a critical need for a free and open-source tool for detecting land cover fluctuations and spatial distribution. This study used Google Earth Engine (GEE) to explore land cover classification and the spatial pattern of major land cover change from 1990 to 2019 on Haitan Island, China. The land cover classification was performed using multiple spectral bands (RGB, NIR, SWIR), vegetation indices (NDVI, NDBI, MNDWI), and tasseled cap transformation of Landsat images based on the random forest supervised algorithm. The major land cover conversion processes (transfer to and from) between 1990 and 2019 were analyzed in detail for the years of 1990, 2000, 2007, and 2019, and the overall accuracies ranged from 88.43% to 91.08%, while the Kappa coefficients varied from 0.86 to 0.90. During 1990–2019, other land, cultivated land, sandy land, and water area decreased by 30.70%, 13.63%, 3.76%, and 0.95%, respectively, while forest and built-up land increased by 30.94% and 16.20% of the study area, respectively. The predominant land cover was other land (34.49%) and cultivated land (26.80%) in 1990, which transitioned to forest land (53.57%) and built-up land (23.07%) in 2019. Reforestation, cultivated land reduction, and built-up land expansion were the major land cover change processes on Haitan Island. The spatial pattern of forest, cultivated land, and built-up land change is mainly explained by the implementation of a ‘Grain for Green Project’ and ‘Comprehensive Pilot Zone’ policy on Haitan Island. Policy and human activities are the major drivers for land use change, including reforestation, population growth, and economic development. This study is unique because it demonstrates the use of GEE for continuous monitoring of the impact of reforestation efforts and urbanization in an island environment.

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17

Kolar, Jan. "Land cover accounting." International Journal of Environment and Pollution 15, no. 6 (2001): 695. http://dx.doi.org/10.1504/ijep.2001.004988.

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18

Akumu, Clement E., Eze O. Amadi, and Samuel Dennis. "Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding." Land 10, no. 3 (March 20, 2021): 321. http://dx.doi.org/10.3390/land10030321.

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Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

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19

Kwan, Chiman, David Gribben, Bulent Ayhan, Jiang Li, Sergio Bernabe, and Antonio Plaza. "An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification." Remote Sensing 12, no. 23 (November 26, 2020): 3880. http://dx.doi.org/10.3390/rs12233880.

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Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

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20

Münch, Zahn, Lesley Gibson, and Anthony Palmer. "Monitoring Effects of Land Cover Change on Biophysical Drivers in Rangelands Using Albedo." Land 8, no. 2 (February 9, 2019): 33. http://dx.doi.org/10.3390/land8020033.

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This paper explores the relationship between land cover change and albedo, recognized as a regulating ecosystems service. Trends and relationships between land cover change and surface albedo were quantified to characterise catchment water and carbon fluxes, through respectively evapotranspiration (ET) and net primary production (NPP). Moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite data were used to describe trends at catchment and land cover change trajectory level. Peak season albedo was computed to reduce seasonal effects. Different trends were found depending on catchment land management practices, and satellite data used. Although not statistically significant, albedo, NPP, ET and normalised difference vegetation index (NDVI) were all correlated with rainfall. In both catchments, NPP, ET and NDVI showed a weak negative trend, while albedo showed a weak positive trend. Modelled land cover change was used to calculate future carbon storage and water use, with a decrease in catchment carbon storage and water use computed. Grassland, a dominant dormant land cover class, was targeted for land cover change by woody encroachment and afforestation, causing a decrease in albedo, while urbanisation and cultivation caused an increase in albedo. Land cover map error of fragmented transition classes and the mixed pixel effect, affected results, suggesting use of higher-resolution imagery for NPP and ET and albedo as a proxy for land cover.

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21

Li, G. M., S. Li, G. W. Ying, and X. P. Wu. "LAND COVER CHANGE MONITORING OF TYPICAL FUNCTIONAL COMMUNITIES OF SICHUAN PROVINCE BASED ON ZY-3 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 815–18. http://dx.doi.org/10.5194/isprs-archives-xlii-3-815-2018.

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According to the function, land space types are divided into key development areas, restricted development areas and forbidden development areas in Sichuan Province. This paper monitors and analyses the changes of land cover in different typical functional areas from 2010 to 2017, which based on ZY-3 high-score images data and combined with statistical yearbook and thematic data of Sichuan Province. The results show that: The land cover types of typical key development zones are mainly composed of cultivated land, forest land, garden land, and housing construction land, which accounts for the total area of land cover 87&thinsp;%. The land cover types of typical restricted development zone mainly consists of forest land and grassland, which occupy 97.71&thinsp;% of the total area of the surface coverage. The land cover types of the typical prohibition development zone mainly consist of forest land, grassland, desert and bared earth, which accounts for the total area of land cover 99.31&thinsp;%.

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22

Karpatne, Anuj, Zhe Jiang, Ranga Raju Vatsavai, Shashi Shekhar, and Vipin Kumar. "Monitoring Land-Cover Changes: A Machine-Learning Perspective." IEEE Geoscience and Remote Sensing Magazine 4, no. 2 (June 2016): 8–21. http://dx.doi.org/10.1109/mgrs.2016.2528038.

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23

Bektas Balcik, F., and A. Karakacan Kuzucu. "DETERMINATION OF LAND COVER/LAND USE USING SPOT 7 DATA WITH SUPERVISED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 143–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-143-2016.

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Land use/ land cover (LULC) classification is a key research field in remote sensing. With recent developments of high-spatial-resolution sensors, Earth-observation technology offers a viable solution for land use/land cover identification and management in the rural part of the cities. There is a strong need to produce accurate, reliable, and up-to-date land use/land cover maps for sustainable monitoring and management. In this study, SPOT 7 imagery was used to test the potential of the data for land cover/land use mapping. Catalca is selected region located in the north west of the Istanbul in Turkey, which is mostly covered with agricultural fields and forest lands. The potentials of two classification algorithms maximum likelihood, and support vector machine, were tested, and accuracy assessment of the land cover maps was performed through error matrix and Kappa statistics. The results indicated that both of the selected classifiers were highly useful (over 83% accuracy) in the mapping of land use/cover in the study region. The support vector machine classification approach slightly outperformed the maximum likelihood classification in both overall accuracy and Kappa statistics.

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Medina, J. M., A. C. Blanco, and C. G. Candido. "LAND COVER MONITORING OF LAGUNA LAKE WATERSHED USING MODIS NDVI DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 85–92. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-85-2020.

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Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.

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Harahap, M. M., Rahmawaty, H. Kurniawan, A. Rauf, and M. Ulfa. "Land cover changes monitoring over ten years in upstream watershed of Deli Serdang Regency North Sumatra Province." IOP Conference Series: Earth and Environmental Science 912, no. 1 (November 1, 2021): 012093. http://dx.doi.org/10.1088/1755-1315/912/1/012093.

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Abstract Deli Serdang is one of the regencies in North Sumatra Province, experiencing relatively rapid development and population. Increasing in demand for the availability of land as living space. Two sub-districts of upstream watershed experienced changes in land cover, namely; Sinembah Tanjung Muda (STM) Hilir and STM Hulu. Monitoring changes in land cover in both sub-districts is essential, given that they are located in the upstream area of the watershed and will impact other areas in the lower watershed. This study aims to analyse land cover changes in both sub-districts over ten years (2009 - 2019). The method used in calculating land changes that occur is change detection. Field surveys were carried out to ensure that the land cover conditions on the land cover maps followed the field’s actual conditions. The research shows the period of 2009 – 2019, land cover that has increased in the area are mining, industry, open land, settlements, livestock and shrubs. The decrease in the area occurred in land cover, including dryland forest, mixed gardens and cultivated land.

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Setiawan, Cahyadi, Muzani Muzani, Warnadi Warnadi, Fauzi Ramadhoan A'Rachman, Qismaraga Qismaraga, and Ermalia Ermalia. "Remote Sensing Imagery and GIS for Monitoring the Pyroclastic Material of Mount Sinabung." Forum Geografi 33, no. 2 (January 14, 2020): 184–95. http://dx.doi.org/10.23917/forgeo.v33i2.9223.

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The purpose of this study was to determine the extent of changes in land cover around the Mount Sinabung area after the 2009-2019 eruption by monitoring through remote sensing imagery and GIS. The method used in this research is descriptive quantitative. The technique of data collection used document study techniques by collecting Landsat images are among the widely used satellite remote sensing data and their spectral, spatial and temporal resolution made them useful input for mapping and planning projects (Sadidy et al. 1981). Changes in land cover that occurred around the Mount Sinabung area were dominated by pyroclastic material due to eruption. In addition, changes in land cover also occur due to the abandonment of potential lands, such as local residents who work in the plantation sector are forced to leave that, so they eventually turn into shrubs. The direction of the dominant pyroclastic material slides was directed towards the east-south and southeast of Mount Sinabung, where the area was dominated by the plantation sector. The impact of the eruption of Mount Sinabung was directly and indirectly. The total land cover changes due to pyroclastic material in 2010 was an area of 26.27 Ha, in 2014 it was 475.82 Ha, 2017 was 1339.75 Ha, and 2019 was 1196.11 Ha.

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Abuzar, Mohammad, Andy McAllister, Des Whitfield, and Kathryn Sheffield. "Remotely-Sensed Surface Temperature and Vegetation Status for the Assessment of Decadal Change in the Irrigated Land Cover of North-Central Victoria, Australia." Land 9, no. 9 (September 2, 2020): 308. http://dx.doi.org/10.3390/land9090308.

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Monitoring of irrigated land cover is important for both resource managers and farmers. An operational approach is presented to use the satellite-derived surface temperature and vegetation cover in order to distinguish between irrigated and non-irrigated land. Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of surface temperature and vegetation cover was achieved for each irrigation period (Spring, Summer and Autumn). The three periodic profiles were used to define irrigation land covers from 2008–2009 to 2018–2019 in a key agricultural region of Australia. The overall accuracy of identifying farms with irrigated land cover amounted to 95.7%. Total irrigated land cover was the lowest (approximately 200,000 ha) in the 2008–2009 crop year which increased more than three-fold in 2012–2013, followed by a gradual decline in the following years. Satellite images from Landsat series (L-5, L-7 and L-8), Sentinel-2 and ASTER were found suitable for land cover classification, which is scalable from farm to regional levels. For this reason, the results are desirable for a range of stakeholders.

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Nikpour, Noorallah, Samad Fotoohi, Hossein negaresh, Seyed Zeynolabedin Hosseini, and shahram Bahrami. "Monitoring land cover changes in line with land degradation (MODIS land cover croduct 2001-2013): Ilam province geographical range." Researches in Earth Sciences 11, no. 1 (March 20, 2020): 130–51. http://dx.doi.org/10.52547/esrj.11.1.130.

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Treitz, Paul. "Remote sensing for mapping and monitoring land-cover and land-use change." Progress in Planning 61, no. 4 (May 2004): 267. http://dx.doi.org/10.1016/s0305-9006(03)00062-x.

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30

Tsai, Yu Hsin, Douglas Stow, Li An, Hsiang Ling Chen, Rebecca Lewison, and Lei Shi. "Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve." Applied Geography 111 (October 2019): 102077. http://dx.doi.org/10.1016/j.apgeog.2019.102077.

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31

Hansen, Matthew C., Peter V. Potapov, Amy H. Pickens, Alexandra Tyukavina, Andres Hernandez-Serna, Viviana Zalles, Svetlana Turubanova, et al. "Global land use extent and dispersion within natural land cover using Landsat data." Environmental Research Letters 17, no. 3 (March 1, 2022): 034050. http://dx.doi.org/10.1088/1748-9326/ac46ec.

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Abstract The conversion of natural land cover into human-dominated land use systems has significant impacts on the environment. Global mapping and monitoring of human-dominated land use extent via satellites provides an empirical basis for assessing land use pressures. Here, we present a novel 2019 global land cover, land use, and ecozone map derived from Landsat satellite imagery and topographical data using derived image feature spaces and algorithms suited per theme. From the map, we estimate the spatial extent and dispersion of land use disaggregated by climate domain and ecozone, where dispersion is the mean distance of land use to all land within a subregion. We find that percent of area under land use and distance to land use follow a power law that depicts an increasingly random spatial distribution of land use as it extends across lands of comparable development potential. For highly developed climate/ecozones, such as temperate and sub-tropical terra firma vegetation on low slopes, area under land use is contiguous and remnant natural land cover have low areal extent and high fragmentation. The tropics generally have the greatest potential for land use expansion, particularly in South America. An exception is Asian humid tropical terra firma vegetated lowland, which has land use intensities comparable to that of temperate breadbaskets such as the United States’ corn belt. Wetland extent is inversely proportional to land use extent within climate domains, indicating historical wetland loss for temperate, sub-tropical, and dry tropical biomes. Results highlight the need for planning efforts to preserve natural systems and associated ecosystem services. The demonstrated methods will be implemented operationally in quantifying global land change, enabling a monitoring framework for systematic assessments of the appropriation and restoration of natural land cover.

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Guo, Andong, Yuqing Zhang, and Qing Hao. "Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes." Complexity 2020 (June 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/3547323.

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Changes in land use/cover are among the most prominent impacts that humans have on the environment. Therefore, exploring land use/cover change is of great significance to urban planning and sustainable development. In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. Moreover, with the help of ArcGIS 10.2 and TerrSet 18.07 software, we explore the current status of land use and predict future changes. The results showed that drastic changes have occurred among different land use classes in Jinzhou District over the past 13 years owing to the impact of economic development and reclamation projects. Construction land, arable land, and waters have changed by +85.09, −24.42, and −23.62 km2, respectively. By comparing the FoM and Kappa coefficients, we concluded that the prediction accuracy of partitioned MLP-MC is better than that of unpartitioned MLP-MC. Therefore, using the spatial zoning approach to identify the conversion rules among land use classes in different zones can more effectively predict future land use changes and provide a reference for urban planning and policy making.

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Abdo, Ziyad Ahmed, and Satya Prakash. "A Review Paper on Monitoring Environmental Consequences of Land Cover Dynamics with The Help of Geo-informatics Technologies." Geosfera Indonesia 5, no. 3 (December 30, 2020): 364. http://dx.doi.org/10.19184/geosi.v5i3.18284.

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Land cover dynamics is a challenging and vigorous process that associates natural and human systems that have undeviating effects on atmosphere, water and soil which lead to many environmental problems worldwide. Urbanization is one of a major land cover change that is highly correlated with many environmental problems that need emphasis. This paper aimed to review and present level and effect of land use land cover changes, urbanization, factors affecting land cover change and application of geographic information system & remote sensing in monitoring land cover changes. Over the past 300 years, about 1.2 million kilometer square of forests and 5.6 million kilometer square of pasture and rangeland were replaced by other uses worldwide, while cultivated land increased by 12 million km2. In 1950, only 30 percent of the world population lived in urban settings, the fraction raised to 55% by 2018. This led to about roughly 60% of the ecosystem services are being destroyed or used in unsustainable ways worldwide. Population expansion, change of technology, high land value, corruption, lack of awareness, migration of people and political pressure are among major driving force of land cover changes. Geo-informatics technology specially GIS and Remote Sensing is found to be an excellent tool for study of land cover change that enables observation across large area of earth’s surface with low cost, better efficient and high accuracy. Therefore monitoring, analyzing and evaluation of land cover dynamics with the help of geo-informatics is decisive for improved management & characterizing land cover alteration processes, and determining its environmental consequences. Keywords : land use; land cover change; urbanization; GIS & remote sensing; environment Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Harjadi, Beny. "Monitoring Penutupan Lahan di DAS Grindulu dengan Metode Penginderaan Jauh dan Sistem Informasi Geografis." Forum Geografi 24, no. 1 (July 20, 2010): 85. http://dx.doi.org/10.23917/forgeo.v24i1.5017.

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Catchments area can be analyzed as management system. Catchments area acquire input and it processed by the system to produce output. Land covers in catchments area are closely related to land use pattern and to management system. Land use changes to building area, agriculture and another activity are related to anthropological characters effected by change in function from vegetated land to unvegetated land. This condition have negative influences to the condition of carchment area. The damaged level of catchment area can be reflected by flood susceptibility, droughness, erosion and sedimentation, related impact onsite and offsite, so it is need a comprehensive management system from up land to low land river. To give information of land use in catchments area it needs accurate data about land cover in wide range. Remote sensing and Geographic Information System (GIS) are applicable to monitor land coverage of management catchments area. The aim of this paper is to analyze land cover using remote sensing and GIS to catchments area monitoring and evaluation. Land use in watershed connection with the pattern of nature resources by the community and the management of watershed. Total area of land use Grindulu watershed was 65.539 ha. From the map of land use could be seen that the spreading of the equitable meeting forest from the upstream to lower, and most property of the people. Land use became 8 classes, that is: Agroforestry (20%), Open Land (12%), Rare Forest (1%), Dense Forest (29%), Village (34%), Paddy (0.4%), River (0.2%), and Field (3%).

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Elhag, Mohamed, and Silvena Boteva. "Quantitative Analysis of Different Environmental Factor Impacts on Land Cover in Nisos Elafonisos, Crete, Greece." International Journal of Environmental Research and Public Health 17, no. 18 (September 4, 2020): 6437. http://dx.doi.org/10.3390/ijerph17186437.

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Land Cover monitoring is an essential task for a better understanding of the ecosystem’s dynamicity and complexity. The availability of Remote Sensing data improved the Land Use Land Cover mapping as it is routine work in ecosystem management. The complexity of the Mediterranean ecosystems involves a complexity of the surrounding environmental factors. An attempt to quantitatively investigate the interdependencies between land covers and affected environmental factors was conducted in Nisos Elafonisos to represent diverse and fragile coastal Mediterranean ecosystems. Sentinel-2 (MSI) sensor and ASTER Digital Elevation Model (DEM) data were used to classify the LULC as well as to draw different vegetation conditions over the designated study area. DEM derivatives were conducted and incorporated. The developed methodology is intended to assess the land use land cover for different practices under the present environmental condition of Nisos Elafonisos. Supervised classification resulted in six different land cover clusters and was tested against three different environmental clusters. The findings of the current research pointed out that the environmental variables are independent and there is a vertical distribution of the vegetation according to altitude.

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Zhou, Dong, Liu, Metternicht, Shen, You, Zhao, and Xiao. "Are There Sufficient Landsat Observations for Retrospective and Continuous Monitoring of Land Cover Changes in China?" Remote Sensing 11, no. 15 (August 1, 2019): 1808. http://dx.doi.org/10.3390/rs11151808.

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Unprecedented human-induced land cover changes happened in China after the Reform and Opening-up in 1978, matching with the era of Landsat satellite series. However, it is still unknown whether Landsat data can effectively support retrospective analysis of land cover changes in China over the past four decades. Here, for the first time, we conduct a systematic investigation on the availability of Landsat data in China, targeting its application for retrospective and continuous monitoring of land cover changes. The latter is significant to assess impact of land cover changes, and consequences of past land policy and management interventions. The total and valid observations (excluding clouds, cloud shadows, and terrain shadows) from Landsat 5/7/8 from 1984 to 2017 were quantified at pixel scale, based on the cloud computing platform Google Earth Engine (GEE). The results show higher intensity of Landsat observation in the northern part of China as compared to the southern part. The study provides an overall picture of Landsat observations suitable for satellite-based annual land cover monitoring over the entire country. We uncover that two sub-regions of China (i.e., Northeast China-Inner Mongolia-Northwest China, and North China Plain) have sufficient valid observations for retrospective analysis of land cover over 30 years (1987–2017) at an annual interval; whereas the Middle-Lower Yangtze Plain (MLYP) and Xinjiang (XJ) have sufficient observations for annual analyses for the periods 1989–2017 and 2004–2017, respectively. Retrospective analysis of land cover is possible only at a two-year time interval in South China (SC) for the years 1988–2017, Xinjiang (XJ) for the period 1992–2003, and the Tibetan Plateau (TP) during 2004–2017. For the latter geographic regions, land cover dynamics can be analyzed only at a three-year interval prior to 2004. Our retrospective analysis suggest that Landsat-based analysis of land cover dynamics at an annual interval for the whole country is not feasible; instead, national monitoring at two- or three-year intervals could be achievable. This study provides a preliminary assessment of data availability, targeting future continuous land cover monitoring in China; and the code is released to the public to facilitate similar data inventory in other regions of the world.

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37

TERSEER, SARWUAN, BEKA FRANCIS T, and OGBOLE ALICE. "MANAGING AND MONITORING NATURAL RESOURCES: A STUDY OF OBEN AREA, NIGER DELTA , NIGERIA." International Journal of Social Sciences & Economic Environment 1, no. 1 (December 30, 2016): 13–32. http://dx.doi.org/10.53882/ijssee.2016.0101002.

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ABSTRACT The main drivers that altered the character of land use/cover in the study area were oil and gas exploration and production (E&P) activities, demographic factors, infrastructural development, agricultural practices and economic factors. Markov model was used in projecting land use/cover change for 10, 20 and 30 year periods. Results of the land use/cover projection in Oben Area show an increase trend in built up and woodland/rangeland areas at the expense of forests and water cover. Suggestions are made at the end of this research work on ways to use the information as contained herein optimally.

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38

Pérez-Hoyos, Ana, Felix Rembold, Hervé Kerdiles, and Javier Gallego. "Comparison of Global Land Cover Datasets for Cropland Monitoring." Remote Sensing 9, no. 11 (November 3, 2017): 1118. http://dx.doi.org/10.3390/rs9111118.

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39

Wulder, Michael A., Joanne C. White, Samuel N. Goward, Jeffrey G. Masek, James R. Irons, Martin Herold, Warren B. Cohen, Thomas R. Loveland, and Curtis E. Woodcock. "Landsat continuity: Issues and opportunities for land cover monitoring." Remote Sensing of Environment 112, no. 3 (March 2008): 955–69. http://dx.doi.org/10.1016/j.rse.2007.07.004.

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40

Comber, A. J., A. N. R. Law, and J. R. Lishman. "Application of knowledge for automated land cover change monitoring." International Journal of Remote Sensing 25, no. 16 (August 2004): 3177–92. http://dx.doi.org/10.1080/01431160310001657795.

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41

Hollenhorst, T. P., L. B. Johnson, and J. Ciborowski. "Monitoring land cover change in the Lake Superior basin." Aquatic Ecosystem Health & Management 14, no. 4 (October 2011): 433–42. http://dx.doi.org/10.1080/14634988.2011.628242.

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42

Tateishi, Ryutaro, and Koji Kajiwara. "Land cover monitoring in Asia by NOAA GVI data." Geocarto International 6, no. 4 (December 1991): 53–64. http://dx.doi.org/10.1080/10106049109354340.

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43

Trifonova, Tatiana, Roman Repkin, and Natalia Mishchenko. "Environmental Monitoring of Land Cover in the River Basins." Biosciences, Biotechnology Research Asia 12, no. 3 (December 22, 2015): 2929–45. http://dx.doi.org/10.13005/bbra/1978.

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44

Velázquez-García, Jaime, Klaudia Oleschko, Jesus Arcadio Muñoz-Villalobos, Miguel Velásquez-Valle, Mario Martínez Menes, Jean-Francois Parrot, Gabor Korvin, and Mariano Cerca. "Land cover monitoring by fractal analysis of digital images." Geoderma 160, no. 1 (November 2010): 83–92. http://dx.doi.org/10.1016/j.geoderma.2009.11.014.

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45

Lin, Yi, Bing Liu, Feng Xie, and Wen Wei Ren. "Land Use/Land Cover Dynamic Monitoring and Analysis Using Remote Sensing and GIS Techniques: Case Study of Qingpu District, Shanghai." Advanced Materials Research 518-523 (May 2012): 5704–9. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5704.

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This paper illustrates almost twenty years (1986～2007) of Land use/land cover change (LULCC) in Qingpu-one district of Shanghai. Qingpu District is an area of Upper Huangpu Catchment for fresh water supply with considerable ecological value, but it is also experiencing urban sprawl from development. To reveal the trends underlie LULCC, we propose a novel procedure to quantify different land use/land covers and implement it in the case study. In this procedure, we first collect historical remote-sensing data and co-registered or corrected them to the same spatial resolution and radioactive level. Based upon preliminary interpretation or investigation, land use/land cover types in study area can be included in 5 categories, i.e. Water, Agricultural Land, Urban or Built-up Land, Forest Land, and Barren Land or others. Moreover, data is clipped via boundary of study area for reducing computation load, followed by FPCR-ISODATA classification to divide the data into k groups (k>the number of land types). After postprocessing, e.g., merge the same connoted subgroups and correct misclassified units accompany with validation and verification, the detailed land use/land cover results can be achieved accurately. The quantitative and regression analysis indicate that during the past twenty years the area of agricultural land of Qingpu decreased coupled with urban or built-up area increased linearly. The water area had the minimum change during the decades. Forests had the smallest average proportion (9.6%) of the total area. It occupied so small proportion of land that we can only find points of it in the maps. Barren land can be an indicator for monitoring uncompleted redevelopment or transition of land.

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46

Tol, Richard S. J. "Dynamic World: Land-Cover and Land-Use Change." Environmental Science & Policy 7, no. 1 (February 2004): 77. http://dx.doi.org/10.1016/j.envsci.2003.10.001.

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47

Phiri, Darius, Matamyo Simwanda, Serajis Salekin, Vincent R. Nyirenda, Yuji Murayama, and Manjula Ranagalage. "Sentinel-2 Data for Land Cover/Use Mapping: A Review." Remote Sensing 12, no. 14 (July 16, 2020): 2291. http://dx.doi.org/10.3390/rs12142291.

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The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

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Tao, Y., G. Lan, L. She, L. Pang, and K. Feng. "DYNAMIC MONITORING OF LAND COVER IN DONGTING LAKE AREA BETWEEN 1995–2015 WITH LANDSAT IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1651–56. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1651-2018.

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In this paper, the Landsat imagery of 1995, 2006 and 2015 is used to monitor land cover change in Dongting Lake area. Our study mainly focuses on five types of land cover: water body, wetland, built-up area, cropland and forest. The land cover change in the Dongting Lake area is comprehensively evaluated by using land cover transfer matrix, land cover change amplitude, and landscape pattern indexes. Our study shows that: (1) the cropland area increased from 48.58&thinsp;% in 1995 to 53.61&thinsp;% in 2006, then decreased to 48.85&thinsp;% in 2015; (2) different from the trend of cropland, the area of water body and wetland decreased in 2006; (3) the forest area steadily increased, to 2015, and there was an increase of 3.46&thinsp;% during 1995&ndash;2015, due to artificial afforestation in the lake area. In terms of the landscape pattern, the landscape fragmentation decreased and the patch connectivity increased.

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Buchhorn, Marcel, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, Luc Bertels, and Bruno Smets. "Copernicus Global Land Cover Layers—Collection 2." Remote Sensing 12, no. 6 (March 24, 2020): 1044. http://dx.doi.org/10.3390/rs12061044.

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In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation.

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Scarth, P., and R. Trevithick. "MANAGEMENT EFFECTS ON GROUND COVER <q>CLUMPINESS</q>: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 183–88. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-183-2017.

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Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20&thinsp;cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

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