Land Cover Temperature Research Articles

Runoff Coefficient in the Air Bengkulu Watershed and the Evaluation of the Existing Spatial Planning

Runoff coefficient plays a crucial role in estimating the peak discharge of a river basin. Therefore, this research aimed to investigate runoff coefficient in the Air Bengkulu watershed based on temporal land cover data and evaluate the existing spatial planning. Land cover data from 1998, 2002, 2016, and 2023, with spatial patterns derived from current regional planning were used. The temporal and spatial pattern-based runoff coefficients were determined using land cover data and spatial pattern function, respectively. Meanwhile, descriptive and comparative methods were adopted based on time. The calculated runoff coefficient was 0.073, 0.093, 0.276, and 0.273 for 1998, 2002, 2016, and 2023, while the value obtained based on spatial patterns was 0.306. Additionally, a general trend of increasing the values over time was observed. Land cover change, particularly the decline of forest areas and the expansion of settlement and plantation, contributed to the rising runoff coefficient. The results showed that runoff coefficient (0.306) exceeded the current land cover, similar to other analyses with higher runoff in the predicted scenario. This research suggested a need for a more detailed classification system and scale to accommodate land cover types with relatively low runoff coefficient. In risk assessment, land cover-like spatial patterns with low runoff coefficient should be placed as capacity other than vulnerability components.

Defining priority areas for conservation of poorly known species: A case study of the endemic Brazilian three-banded armadillo

Abstract Conservation of poorly known species is challenging as lack of knowledge on their specific requirements may hamper effective strategies. Here, by integrating biogeographical and landscape analyses, we show that informed actions can be delineated for species with limited presence-only data available. We combine species distribution and connectivity models with temporal land cover changes to define priority areas for conservation of the endemic Brazilian three-banded armadillo, one of the most threatened xenarthrans that was once considered extinct in the wild. We revealed that areas of savanna and grassland are the most suitable habitats for the species and that uplands in the Caatinga ecoregion have a greater likelihood for dispersal. The few remnant armadillo populations are spatially associated with core areas of natural vegetation remnants. Worrisomely, 76% of natural core areas were lost in the past 30 years, mirroring the species’ severe population decline. Preserving the remnant core natural areas should be a high priority to ensure the species’ survival. We highlight key areas for proactive and reactive conservation actions for the three-banded armadillo that will benefit other threatened sympatric species. Our integrative framework provides a set of valuable information for guided conservation management that can be replicated for other poorly known species.

Estimation of Runoff and Sediment Yield in Response to Temporal Land Cover Change in Kentucky, USA

Land cover change is prevalent in the eastern Kentucky Appalachian region, mainly due to increased surface mining activities. This study explored the potential change in land cover and its relationship with stream discharge and sediment yield in a watershed of the Cumberland River near Harlan, Kentucky, between 2001 and 2016, using the Soil and Water Assessment Tool (SWAT). Two land cover scenarios for the years 2001 and 2016 were used separately to simulate the surface runoff and sediment yield at the outlet of the Cumberland River near Harlan. Land cover datasets from the National Land Cover Database (NLCD) were used to reclassify the land cover type into the following classes: water, developed, forest, barren, shrubland, and pasture/grassland. Evaluation of the relationship between the land cover change on discharge and sediment was performed by comparing the average annual basin values of streamflow and sediment from each of the land cover scenarios. The SWAT model output was evaluated based on several statistical parameters, including the Nash–Sutcliffe efficiency coefficient (NSE), RMSE-observations standard deviation ratio (RSR), percent bias (PBIAS), and the coefficient of determination (R²). Moreover, P-factor and R-factor indices were used to measure prediction uncertainty. The model showed an acceptable range of agreement for both calibration and validation between observed and simulated values. The temporal land cover change showed a decrease in forest area by 2.42% and an increase in developed, barren, shrubland, and grassland by 0.11%, 0.34%, 0.53%, and 1.44%, respectively. The discharge increased from 92.34 mm/year to 104.7 mm/year, and sediment increased from 0.83 t/ha to 1.63 t/ha from 2001 to 2016, respectively. Based on results from the model, this study concluded that the conversion of forest land into other land types could contribute to increased surface runoff and sediment transport detached from the soil along with runoff water. The research provides a robust approach to evaluating the effect of temporal land cover change on Appalachian streams and rivers. Such information can be useful for designing land management practices to conserve water and control soil erosion in the Appalachian region of eastern Kentucky.

VSDF: A variation-based spatiotemporal data fusion method

The fusion of spatiotemporal data provides the possibility to improve both the spatial and temporal resolution of remote sensing data. Nevertheless, the performance of current spatiotemporal data fusion methods is affected by several aspects, e.g., (1) retrieval of abrupt land cover changes, (2) recovery of detailed spatial information, and (3) the need to reduce side effects related to the performance differences between sensors. Concerning the above aspects, this study proposes the use of a Variation-based Spatiotemporal Data Fusion (VSDF) method. In VSDF, an abundant variation classification (AVC) is used to identify explicitly the land cover changes for spectral unmixing. In addition, feature-level fusion is introduced to strengthen the spatial structures, i.e., the edges and texture. Furthermore, a relative reliability index (RRI) is proposed to guide the prediction process to lower the uncertainty of the input datasets. With reference to the all-round performance assessment (APA) metrics, the performance of VSDF was compared with five popular methods, i.e., Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), FSDAF 2.0, Reliable and Adaptive Spatiotemporal Data Fusion (RASDF), and Fit-FC (regression model Fitting, spatial Filtering and residual Compensation). The experimental results demonstrated that VSDF can realize a more accurate prediction of temporal land cover changes with better spatial detail than the benchmark methods. Consequently, VSDF has the potential to generate accurate high-spatiotemporal-resolution simulations for global remote sensing studies.

Predicting spatial and temporal changes in surface urban heat islands using multi-temporal satellite imagery: A case study of Tehran metropolis

Nowadays, predicting spatial and temporal changes of surface urban heat islands (SUHIs) is both a demanding challenge and a subject of interest for researchers working on urban thermal environments. This study aimed to predict the spatial-temporal changes of SUHI in Tehran city. The research data consisted of Landsat multi-temporal images, MODIS-derived water vapor and land surface temperature (LST) products, as well as climatic data. The cellular automata (CA)-Markov model was used for predicting the spatial and temporal land cover changes. Afterward, based on the statistical and spatial information of the impact of land cover changes on LST changes, maps of the LST and SUHII were procured for future years. The results showed that the area of built-up lands increased by 88% from 1985 to 2019. The built-up lands area is expected to increase to 446.3, 463.9, and 480.1 km2 by 2026, 2032, and 2038, respectively. The mean LST of Tehran city in 1985, 1992, 2000, 2009, and 2019 was 36.45, 41.95, 43.15, 44.95, and 47.25 °C, respectively. By 2026, 2032, and 2038, the mean LST may reach 48.65, 49.95, and 51.15 °C, respectively. Analysis of the SUHI ratio index in Tehran showed an increase from 0.01 in 1985 to 0.34 in 2019, and is expected to reach 0.38, 0.45, and 0.51 by 2026, 2032, and 2038, respectively.

The composition of time-series images and using the technique SMOTE ENN for balancing datasets in land use/cover mapping

Monitoring Land-use/land-cover (LULC) changes are a significant challenge for sustainable spatial planning, particularly in response to transformation and degenerative landscape processes. These disturbances lead to the vulnerability of inhabitants and habitat and climate changes and socio-economic development in the region. Several studies have proposed different methods and techniques to monitor the spatial and temporal changes of LULC. Machine learning is a more popular method. However, the problem of data imbalance is a significant challenge, and the classification results tend to bias the majority classes for unbalanced data. Therefore, this study's objective is to develop a state-of-the-art technique to reduce the problem of data imbalance in LULC classification in Vietnam based on machine learning and SMOTE (Synthesizing Minority Oversampling Technology) with Edited Nearest Neighbor (ENN). Various statistical indices, including Kappa and Accuracy, have been used to assess the performance for the classification of Land-use/cover. The results indicate that integrating oversampling and under-sampling with SMOTE ENN gave better overall accuracy and generalization. We also find that the expected proportion of chance agreement after oversampling is higher than before (Kappa score before and after oversampling is 0.905244 and 0.974379, respectively). This study provides an effective method to monitor spatial and temporal land cover change in Vietnam; it plays a role as a framework for other relevant research related to land cover change, which can support planning and sustainable management of the territory.

Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km2. The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park.

Following the cosmic-ray-neutron-sensing-based soil moisture under grassland and forest: Exploring the potential of optical and SAR remote sensing

Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods. So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap. So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet. However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC. Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal. Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling. We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68. It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution. However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNS-based SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression. Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site. When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types.

Tracks of Death: Elephant Casualties along the Habaipur–Diphu Railway in Assam, India

Railway development is an important component of sustainable transportation systems but also affects wildlife habitats worldwide. Here, we assess spatiotemporal patterns of elephant–train collisions and mortalities within the state of Assam, India, and relate them to spatial and temporal land cover change (LCC) from 1988 to 2018. The results indicate that an extension of railways into forested landscapes is associated with large-scale LCC and increased elephant–train collisions and mortality. Prior to 1997, when the railway system used narrower gauge rails, elephant deaths from collisions occurred at a rate of one or two per year. After 1997, when the system was converted to larger gauge rails, elephant deaths increased starkly and now occur at a rate approaching ten per year. While the rail gauges were being converted, the landscape around the Habaipur–Diphu railway line saw a sevenfold increase in annual net loss of dense forest. The transition from forest to croplands was the most dominant process of deforestation and forest fragmentation during the postconversion period. Although elephant–train collisions are strongly associated with the land use transitions shown here, conservation and remediation measures can help to stem further declines in forest habitats and promote safe movement by elephants between resource patches.

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

This study aimed to analyze fragments of rangelands through spectral responses and land cover change by livestock in regions of the Caatinga biome through remote sensing. For spectral behavior, the surface reflectance bidirectional (SRB) and spectral indexes of vegetation were used to verify the ragelands seasonality. Land cover change detection of Ouricuri and Tauá through Landsat-8 images with a 16-day revisit interval, were processed in the Google Earth Engine platform (GEE) and software Quantum GIS version 2.18 (QGIS). In the GEE platform, annual mosaics and stacking of the spectral bands were generated for the classification of images, and in sequence the production of thematic maps in QGIS. The analysis of land cover change considered the classes: thinned Caatinga, conserved Caatinga, herbaceous vegetation, bare soil, water and others. The analysis of the spectral responses showed that the vegetation monitored in Ouricuri presented higher SRB in the infrared band and lower SRB in the red and blue bands, and that caused the pasture to produce higher vegetation indexes than the other locations. Through validation, it was observed that in Tauá, there was an overall accuracy of 91% and Kappa index of 89%, and in Ouricuri there was an overall accuracy of 90% and Kappa index of 86%, indicating excellent correctness of the classification model. The classification model proved to be effective in verifying the temporal and spatial land cover change, making it possible to identify places with the vegetation that was most affected and susceptible to degradation and generation of political support to minimize damage to the Caatinga Biome.

Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas

Accurate temporal land use mapping provides important and timely information for decision making for large-scale management of land and crop production. At present, temporal land cover and crop classifications within a study area have neglected the differences between subregions. In this paper, we propose a classification rule by integrating the terrain, time series characteristics, priority, and seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on the time series of Normalized Difference Water Index (NDWI) and Vegetation Index (NDVI), a dynamic decision tree for forests, cultivation, urban, and water was created in Google Earth Engine (GEE) for each subregion to extract cultivated land. Then, with or without this cultivated land mask data, the original classification results for each subregion were completed based on composite image acquisition with five vegetation indices using Random Forest. During the post-reclassification process, a 4-bit coding rule based on terrain, type, seasonal rhythm, and priority was generated by analyzing the characteristics of the original results. Finally, statistical results and temporal mapping were processed. The results showed that feature importance was dominated by B2, NDWI, RENDVI, B11, and B12 over winter, and B11, B12, NDBI, B2, and B8A over summer. Meanwhile, the cultivated land mask improved the overall accuracy for multicategories (seven to eight and nine to 13 during winter and summer, respectively) in each subregion, with average ranges in the overall accuracy for winter and summer of 0.857–0.935 and 0.873–0.963, respectively, and kappa coefficients of 0.803–0.902 and 0.835–0.950, respectively. The analysis of the above results and the comparison with resampling plots identified various sources of error for classification accuracy, including spectral differences, degree of field fragmentation, and planting complexity. The results demonstrated the capability of the TTPSR rule in temporal land use mapping, especially with regard to complex crops classification and automated post-processing, thereby providing a viable option for large-scale land use mapping.

Improving the consistency of multi-temporal land cover mapping of Laguna lake watershed using light gradient boosting machine (LightGBM) approach, change detection analysis, and Markov chain

The enhanced temporal capability of today's satellite sensors gives us large volumes of data to be processed, analysed, and visualized. Most of the conventional remote sensing software and land cover classification approaches, however, are only designed for single-date observations. To fully utilize the amount of data we receive and to improve land use/land cover mapping (LULC), technological advancements in machine learning, open-source processing, and GPU-accelerated hardware should be utilized. In this paper, a methodology for classification of temporal sequence of Sentinel-2 images was developed using open-source Python libraries. Light Gradient Boosting Machine, a machine learning algorithm that uses tree-based learning, was used to classify different land cover types based on a temporal sequence of Sentinel-2 satellite images. Although the use of powerful machine learning algorithm resulted to more accurate land cover maps, temporal inconsistencies are still pervasive when dealing with time series outputs. To remove these temporal inconsistencies that resulted from misclassifications, temporal land cover filter based on transition probability matrix was applied on the time series land cover maps to modify the illogical land cover transitions. Accuracy assessment revealed good performance of the approach, which produced higher overall accuracy.

Identification of Land Cover Changes in The MK-PHR Conservation Area Associated With Fire Events

Peat fire is one of the most environmental issues in Indonesia, particularly in the region of South Sumatra. The fires usually occur during the dry season from Juni to November. This study is designed to evaluate the impact of peat fires on changes in land cover in the Merang Kepahyang Peat Hydrological Region (MK-PHR) for the period of 2002 to 2015. The changes identified by the Normalized Difference Vegetation Index (NDVI) method using Landsat satellite imagery. The results of the study show that extreme peat fire associated with climate anomalies events occurred in 2006, 2012, and 2015. The largest peat fires during the observation period occurred in 2015 coincided with extreme climate anomalies generating negative changes in land cover in the most of MK-PHR areas. The changes occurred in almost half (49%) of the MK-PHR area, where it moved from the dense to moderate vegetation class. The spatial and temporal land cover changes will be discussed in more detail.

Spatial–temporal variation of land use and land cover change in the glacial affected area of the Tianshan Mountains

During the past decades, land use/land cover change (LUCC) has occurred in the glacial affected areas at unpredictable rates driven by natural and human factors. Monitoring and identifying the spatial and temporal land cover changes and driving forces in this unique type of region provides a scientific basis for understanding the changing process of the Earth's surface, helping to reveal the impact of climate change and human activities on LUCC. In this study, the Tianshan Mountains (TSMs), which are located in the hinterland of Eurasia, were selected to investigate the LUCC of its typical glacial affected areas that have significant regional particularities. LUCC processes in the TSMs in China over the past 35 years were analyzed using a dynamic change model, a landscape pattern index, a centroid transfer model, and geoinformation Tupu based on the land use data of 1980, 1990, 2000, and 2015. The results show that the areas of cultivated and built-up lands immensely increased by 45.87% and 187.10%, respectively. Correspondingly, the areas of bare land and ice and snow cover decreased by 26.76% and 37.93%, respectively. The land use change in the TSMs was characterized by different stages, and high conversion rates and intensities were obtained from 2000 to 2015. The landscape change was mainly reflected in the significant increase in the number of patches and the simplification and regularization of patch shapes. The spatial connectivity of different land use types also increased. The temperature and precipitation in the region showed increasing trends, and the melting rate of ice and snow cover significantly accelerated. This study can help achieve a dynamic LUCC model to investigate the interacting influences of climate change and human activities in glacial affected areas.

Analyzing the Impact of Temporal Landcover Changes on the Runoff Component of a Catchment Using Geospatial Techniques

Globally, the issue of urbanization has been a matter of concern over the past few decades. Unplanned urban growth alters the natural drainage pattern and the water cycle, consequently, giving rise to floods. The floods are attributed to heavy precipitation but, human activities act as a catalyst in creating an imbalance in the environmental harmony. Predominantly, urban floods in the developing nations are known to affect quite a large population and infrastructure. Nagpur, in Maharashtra, India has been facing hydro-meteorological events in the last decade along with a persistent reduction of natural land and rapid urban growth. The paper attempts to establish a relationship between temporal landcover change for years 1995, 2005, 2015 and increased surface runoff eventually causing urban floods through application of the Soil Conservation Services Curve Number method. These years marked a remarkable urban development in Nagpur hence were considered. The outcome demonstrated a prominent urban sprawl of 12.84 percent and a severe loss of 16.32 percent in natural vegetation between 1995 and 2015. This paper examines the relationship between urban expansion and runoff at the catchment level by integrating land use/cover mapping with the hydrological model. Although the potential impact is expected, this study can further confirm it and facilitate policymakers developing policies to mitigate flooding-caused damages.

Analysis of Urban Expansion and its Impacts on Land Surface Temperature and Vegetation Using RS and GIS, A Case Study in Xi’an City, China

The present study aims to explain the impacts of urban expansion on land surface temperature and vegetation of Xi’an City, China using integrated techniques of remote sensing (RS) and geographic information system (GIS). Urbanization is one of the potential driving factors for land use/land cover (LULC) change, vegetation decrease, and land surface temperature (LST) increase. To access information about the spatial and temporal land cover change, the use of two essential sources RS and GIS is indispensable. This study focuses on LULC changes in six districts of Xi’an City, China for the last three decades (1987–2018) using Landsat satellite images. The images from Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) of Landsat satellite are utilized. The results are as follows: (1) Xi’an experienced strong urbanization over the last 30 years with an increase of 69.6% in the urban area. The urban expansion pattern from 1987 to 2002 is alternate infilling and edge expansion in central districts, while from 2002 to 2018 is dominantly edge expansion toward outer districts. (2) Urbanization has negative effects on normalized difference vegetation index (NDVI); from 2002 to 2018 vegetation cover has decreased sharply in outer districts of Xi’an. (3) There is a synergetic relationship between urban expansion and LST. (4) The correlation between LST and NDVI is strongly negative, which indicates vegetation can relieve the effects of LST to some extent.

Assessment of Land Cover changes on long-term treated industrial effluent irrigation using Remote sensing and GIS techniques

A study was under taken to identify and map the changes in land use and land cover over a period of (1992 to 2017) 25 years in an area irrigating with treated industrial waste water using remote sensing and GIS technologies. Four LANDSAT TM and ETM+ images of 1992, 1999, 2006 and 2017 with a spatial resolution 30mx30m were used to determine the temporal land cover changes. Consequently, ground truth confirmation was done in the study area. Supervised classification was carried out in ArcGIS to identify the LULC classes. The study area was classified into four major classes; Water bodies, Settlements, Vegetation and Fallow lands. The study revealed that fallow lands was decreased by 99.8%, vegetation was found to be increased by 90.2% and settlement area was found to increase by 65.7 % over the period of 15 years. Livelihood increased with increased socio-economic status of the people.

Assessing inconsistency in global land cover products and synthesis of studies on land use and land cover dynamics during 2001 to 2017 in the southeastern region of Bangladesh

The high-quality Land Use and Land Cover data is important for monitoring and analyzing environmental changes in the background of global warming. This study accessed the spatial and areal inconsistencies in the four most recent multi-resources land cover products in a complex manner using the common classification systems of IGBP-17, IGBP-9, IPCC-5 and TC (vegetation, wetlands and others only). Based on inconsistencies and multi temporal land cover datasets, a synthesis of study was triggered out on land use and land cover dynamics during 2001-2017 in the southeastern region of Bangladesh. The overall areal and spatial inconsistencies decreased from high to low levels of aggregation (IGBP-17 to TC), indicating that the inconsistencies are not only influenced by the level of thematic detail and landscape complexity but also related to the conversion uncertainties. Overall areal inconsistency in the comparison of the FROM-GLC and GlobeLand30 datasets was the smallest among the six pairs, while, the pair of MODISLC and LULC was observed the highest inconsistencies. Based on overall lower inconsistencies classification system (IGBP-9), the synthetic land use cover changes at the study area were assessed. During the period of study, the areal distribution of forest cover, built-up areas and water were found increased in annually by 0.4%, 1.32%, and 0.3% respectively, while the croplands and wetlands were respectively decreased by 0.5% and 0.3%. The dynamic changes of croplands, forest, and artificial surface were identified the prime cyclic land cover change. This research is helpful in providing training areas for the producer of land cover products.

Fragmentación de pastizales en el centro de la provincia de Buenos Aires, mediante imágenes LANDSAT

En el presente trabajo, se empleó una combinación de mapas dinámicos de cobertu- ras y métricas de paisaje basados en imágenes satelitales para cuantificar los cam- bios espacio-temporales de coberturas y los patrones de fragmentación en el centro de la provincia de Buenos Aires (Argentina), en el período 1974-2011. Esta área de estudio cubre dos zonas agroecológicas: la Pampa Deprimida y la Pampa Interior. A diferencia de otras regiones de Los Pastizales del Río de La Plata, el pastizal nativo ha sido reemplazado por una matriz de pastos cortos (más adaptada al pastoreo vacuno): durante todo el período de estudio, el pastizal nativo decreció 2762,1 Nm2 en un área de estudio de 21,911.4Nm2. El patrón de fragmentación en el paisaje presentó diferencias espaciales significativas entre las zonas agroecológicas anali- zadas, con un mejor estado de conservación de los pastizales en la Pampa Deprimi- da. La identificación de este patrón espacial debería promover políticas de conservación de la biodiversidad y manejo sustentable de este paisaje.

The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.