Classification Of Landsat Images Research Articles

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

Impact of Land Use Change Due to Urbanisation on Surface Runoff Using GIS-Based SCS–CN Method: A Case Study of Xiamen City, China

Rapid urban development results in visible changes in land use due to increase in impervious surfaces from human construction and decrease in pervious areas. Urbanisation influences the hydrological cycle of an area, resulting in less infiltration, higher flood peak, and surface runoff. This study analysed the impact of land use change due to urbanisation on surface runoff, using the geographic information system (GIS)-based soil conservation service curve number (SCS–CN) method, during the period of rapid urban development from 1980 to 2015 in Xiamen, located in south-eastern China. Land use change was analysed from the data obtained by classifying Landsat images from 1980, 1990, 2005, and 2015. Results indicated that farmland decreased the most by 14.01%, while built-up areas increased the most by 15.7%, from 1980 to 2015. Surface runoff was simulated using the GIS-based SCS–CN method for the rainfall return periods of 5, 10, 20, and 50 years. The spatial and temporal variation of runoff was obtained for each land use period. Results indicate that the increase in surface runoff was highest in the period of 1990–2005, with an increase of 10.63%. The effect of urbanisation can be realised from the amount of runoff, contributed by built-up land use type in the study area, that increased from 14.2% to 27.9% with the rise of urban expansion from 1980 to 2015. The relationship between land use and surface runoff showed that the rapid increase in constructed land has significantly influenced the surface runoff of the area. Therefore, the introduction of nature-based solutions such as green infrastructure could be a potential solution for runoff mitigation and reducing urban flood risks in the context of increasing urbanization.

Analysis of the Future Land Use Land Cover Changes in the Gaborone Dam Catchment Using CA-Markov Model: Implications on Water Resources

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

Vulnerability assessment of water resources using GIS, remote sensing and SWAT model – a case study: the upper part of Dong Nai river basin, Vietnam

ABSTRACT Water resources vulnerability assessment provides the basis for the policymakers and the stakeholders regarding the state of the river basin to be focused for its better management. This research aims to assess the vulnerability index (VI) of the upper part of the Dong Nai (UPDN) river basin, which plays a crucial role for the socio-economic development of Ho Chi Minh City and 10 other provinces in the Southeast Vietnam, as well as its sub-basins. This study accounts from the year 1994 to 2030 (36 years) by using an integrated solution between the GIS, remote sensing (RS) and SWAT model in collaboration with guidelines prepared by United Nations Environment Programme (UNEP) and Peking University. Results from the land cover classification of Landsat images in the period of 1994–2020 revealed that the forest area decreased by 324,586 ha (approximately 29.4% of the basin area) and greatly influenced the VI. The UPDN basin’s water vulnerability was a moderate level of which VI was 0.276, 0.303 and 0.310 in 1994, 2004 and 2014, respectively. By 2030, the projected VI value could expect to be 0.318 under the climate change scenarios of the representative concentration pathway (RCP4.5). At the sub-basins, Dak Nong and Dak R‘Keh were two of the most vulnerable sub-basins of which VIs have could reach approximately 0.4 categorized as highly vulnerable areas. The results evidenced that the integrated techniques could be an efficient approach to provide useful information on the status of water resources to stakeholders in evaluating comprehensive options in terms of sustainable water resources management.

Assessing Forest Cover Change and Fragmentation in Northeastern British Columbia Using Landsat Images and a Geospatial Approach

Forest cover loss and fragmentation have increased in recent years due to the increasing anthropogenic land use. Whereas previous studies have focused mostly on forest cover change from other disturbances (e.g., insects, diseases, and forest fires), little is known regarding how land uses are increasing at the expense of forest cover. Thus, we fill this research gap by assessing forest cover change and fragmentation with an emphasis on how competing anthropogenic land uses are increasing at the expense of the coniferous forests using empirical evidence from northeastern British Columbia (BC). We classified Landsat images from 1985, 2000, and 2015 using the Random Forests (RF) classification algorithm. The study finds an annual net loss of 2.3% (gain: 6.9%, loss: 9.2%) in the coniferous forest cover, but the timber harvest land base’s (THLB) share of the coniferous forest gain and loss is higher than that of the area outside the THLB. We find that the period with a lower amount of fragmentation has a lesser amount of coniferous forest cover loss and vice versa. We show that coniferous forest cover is more likely to recover from barren land. In forest areas where anthropogenic land uses are on the rise, land managers could use knowledge from our study as ancillary information in assessing the sustainability and potential cumulative impacts of anthropogenic land uses. We conclude that the rate of forest recovery from anthropogenic-induced land categories is likely to account for the amount of forest fragmentation.

Impacts of Urbanization on Land Cover Pattern in Bangladesh: A Downscaled Approach for Chuadanga District

Bangladesh has been facing rapid and unplanned urbanization for last several years resulting devastating change to vegetation, fresh water body and bare soil in this area. This integrated study aimed to find out the trend of urbanization and land cover changes in Chuandanga, a western district of Bangladesh. This downscaled approach used GIS and remote sensing techniques for supervised land cover classification of Landsat images for last five years (2014-2018). The study has portrayed an increasing trend in built up area and bare soil whereas a decreasing trend in vegetation and water body. Meanwhile, it has been found that change in built up area and vegetation cover is strongly correlated whilst built area is increased by 1.28% (14.84 sq.km) and vegetation decreased by 16.54% (191.96 sq.km) during this period. Moreover, it is also observed that rapid growth of urban areas has a considerable influence on decreasing water body and bare soil as well as the changing pattern showing the forthcoming urbanization in this city. This study shows the importance of urban expansion for achieving Sustainable Development Goals (SDG) in particular Goal 11, Sustainable Cities.
 Environ. Sci. & Natural Resources, 12(1&2): 37-42, 2019

Spatial and transient modelling of land use/land cover (LULC) dynamics in a Sahelian landscape under semi-arid climate in northern Burkina Faso

Sahelian landscapes have gone through rapid changes over the past decades under high pressure from an ever-growing population, climate hazards and land degradation. Understanding drivers of change and forecasting LULC dynamics in time and space are critical for sustainable land management. This research aimed at analyzing LULC patterns and forecasting future changes in the landscape at the watershed level in semi-arid climate in northern Burkina Faso. Three LULC categories (natural vegetation, degradation soils, and cultivated areas) were considered. Seasonal vegetation dynamics were assessed through the monitoring of radiometric indices over the years 2015, 2016 and 2017. The post-harvest period (October-November) was found to be optimal for single-date LULC mapping. Furthermore, LULC maps for 1986, 1999, 2009 and 2017 were produced through a supervised classification of Landsat satellite images. For land change modelling and prediction, a Multilayer Perceptron (MLP) neural network was calibrated between 1999 and 2009 and validated against 2017 reference map, then used to simulate prediction maps for 2030 and 2050, in conjunction with population growth forecasts. Analysis of LULC changes revealed degradation soils were the major contributor to cultivated areas expansion between 1986 and 2017. Land use maps predicted for 2030 and 2050 indicated an expected increase in cultivated areas of 10.4 % and 22.7 % respectively. These results highlight the strength of the human-environment relationship in the watershed and call for careful planning and conservation actions for a sustainable balance between sustainable agricultural production and natural resources conservation.

Land use change in the Ecuadorian páramo: The impact of expanding agriculture on soil carbon storage

ABSTRACT The Andean páramo is notable for high soil carbon storage and its contribution to ecosystem services. However, the páramo’s ability to maintain high soil carbon levels is threatened by land use change from tussock grassland and shrublands to agricultural uses. A chronosequence study was conducted in the páramo around Quito, Ecuador, to determine the rate of soil carbon loss from traditional fallow agriculture. In parallel, a land use and land cover classification of Landsat images was used to measure the change in agricultural areas between 1991 and 2017. There was a significant negative relationship between the time since initial cultivation of a field and soil C: Older agricultural sites had significantly less C compared to natural ecosystems due to an average loss of 0.045 percent soil C per year. Undisturbed sites had significantly more soil C than cultivated sites but not pastures or fallow fields, indicating that cultivation is the most detrimental stage of the fallow agricultural cycle for soil C storage. There was an 838 percent increase in cultivated land between 1991 and 2017 but a 10 percent decrease in pastures, indicating a trend away from traditional regenerative agriculture toward land use types that lead to substantial losses in soil carbon.

Assessing Land Use / Land Cover Changes Using GIS and Remotely Sensed Techniques (RST): A Case Study of the Etoa Clay Quarry Yaounde, Cameroon

This study highlights the use of Remote Sensing to detect environmental changes obtained on Land Use / land Cover Changes in the Etoa clay quarry, neighboring the town of Yaounde (Cameroon) during a period of 26 years (1989-2015). Remotely sensed data in the form of Landsat image and pattern classification were used to create maps. For accuracy Google Earth was used as a reference to yield data. The post classification change detection method was used to value the land cover change with the clay quarry. Land use of the urban growth was estimated by time series analysis of satellite Landsat images. Spectral indices calculated from the images were used to evaluate change in buildup area, vegetation and hydrology. Results show that vegetation, covers 200 hectares of land, build up area 240 hectares and hydrology 185 hectares. The change analysis between 1989-2015 showed a decreasing trend in vegetation from 69% to 32% (a decrease in 37% vegetation cover), hydrology from 14% to 30% (thus a 16% increase in water bodies) and build up area from 17% to 38% (a 21% increase). The excavation of clay materials increases every year leaving behind many dangerous clay pits filled with water which calls for concern.

Land cover/use change analysis and mapping of Borg El-Arab City, Egypt

One of the essential steps for agricultural development planning is the recognition of land cover and land use LCLU classes and the associated changes with time, in which LCLU avails as one of the major input criteria. The North coastal region of Egypt is exposed to significant spatial and temporal changes in LCLU classes (urban, agriculture, and water bodies) that basically affect any kind of development programs. Borg El-Arab City represents a potential community and new industrial site for future development in the region. The supervised classification of Landsat satellite images and computation of the normalized difference vegetation index (NDVI) are applied as effective tools in the study area to observe the LCLU changes. Landsat 5, 7, and 8 satellite images were used and four images dated in 1986, 1999, 2006, and 2017 were selected. The area under consideration was classified into seven main classes ,e.g., Mediterranean Sea, salt marshes, agriculture, urban, bare land, swamps and wet land, and shoreline strip. The area was subjected to significant changes in the last three decades as a result of the reclamation projects growing and industrial effectiveness as well. Taking the whole period (1986–2017) into consideration, results show that the study area is exposed to a wide range of gain or loss in the area of different land cover classes, particularly in regard to the agriculture area, urban land, and bare land. The surface area of urban land, agriculture land, and water bodies increased on the expense of decreasing the bare land, where it lost 31.3%. The urban area expanded into much of the agricultural and the agricultural land expanded into bare land. In light of the recognized change causes, approach policy suggestions for better management of LCLU can be established.

Monitoring of Land Use and Land Cover Change Detection Using Multi-temporal Remote Sensing and Time Series Analysis of Qena-Luxor Governorates (QLGs), Egypt

In recent years, rapid land use land cover (LULC) changes have continuously taken place in many regions all over the world as a result of human activities. In the present study, the changes in LULC were analyzed by means of multi-temporal remote sensing of Qena-Luxor Governorates in Egypt between 1984 and 2018. In order to map and monitor the land use land cover changes, several remotely sensed data were applied to create multi-maps using (1) the normalized difference vegetation index and (2) supervised classification of Landsat images using field chick and accuracy assessment, including field verification and Google Earth Professional. Therefore, the lands in the study area can be classified as follows: (1) agricultural lands, (2) built-up areas, (3) water bodies, (4) reclaimed lands, and (5) desert lands. The results indicate that agricultural lands grew from an average of 1238.7 km2 (9.8%) in 1984 to 1707.04 km2 (13.40%) in 2018 and urban lands increased from 345.2 km2 (2.7%) in 1984 to 445.28 km2 (3.5%) in 2019. Furthermore, the reclaimed lands increased approximately from 4379.7 km2 in 1984 (i.e., 34.4% of the total study area) to 4521.05 km2 in 2000 (35.507%). However, this class was followed by a marked decline to 4373.51 km2 (34.35%) between 2000 and 2010 and then increased to approximately 4442 km2 (34.89%) between 2010 and 2018. Desert lands (limestone plateau and some lowland desert fringes) decreased from 6635.4 km2 (52.2%) to 6003.5 km2 (47.15%). The results showed that the overall accuracy of the supervised classification of Landsat satellite images ranges from 87 to 92.5% while kappa statistics were from 0.83 to 90.

USING GOOGLE EARTH™ AND GEOGRAPHICAL INFORMATION SYSTEM DATA AS METHOD TO DETECT URBAN SPRAWL AND GREEN SPACES FOR BETTER WELL BEING CASE OF A COASTAL LANDSCAPE

Coastal landscapes are facing a huge challenge to manage the spatial extension of their built-up area at the expense of the reduction of natural and cultivated areas. This is the case of Hergla city, located in the southern part of Hammamet Gulf, Tunisia. This paper firstly highlights changes of LULC in Hergla city, between 2007 and 2017 using a supervised classification of Landsat images. The evolution of built-up area between 2002 and 2020 is examined expending Google Earth images. Lastly, the geolocalization of green spaces are provided. Then, the superposition of all these analyzes will be used to propose a landscaping for a better human well-being. Finally, this research indicates the importance of analyzing LULC change at multiple scales; it revealed that built-up area has been increased and olive fields reduced from 64 % in 2007 to 30.2 %in 2017. It shows, too, an important urban expansion from 39.9 Ha in 2002 to 48.3 Ha in 2020. However, the green spaces are concentrated in the North and middle part of the city and a proposal for the development of an urban park on the south side will help to balance the spatial distribution of green spaces in this area and ensure better human well-being.

Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India

Urbanisation induced land use and a land cover change is a proliferating phenomenon worldwide. The impacts of chaotic urban growth are all well documented. To overcome the ill effects of urbanisation, continuous monitoring and sustainable planning are the keys. As the future urban growth in India is predicted to take place in small cities of less than a million populations, it is the crucial time to plan a strategy for sustainable urban growth. Mangaluru, being a small, emerging city has the basic fertility required for urban growth. But the deficiencies of spatial data availability and lack of insight for impending urbanisation scenario would hamper the sustainable management of urban growth in the region. In this context, the current study proposes to map land use and land cover and monitor the spatiotemporal dynamics of urban growth in Mangaluru urban agglomeration, India, during 1972–2018. The present study employs techniques of remote sensing and geographic information system (GIS) and uses Landsat series of imageries. The land use and land cover changes and dynamics of urban growth have been studied using change detection, Shannon entropy, and spatial trend analysis. The study reveals that the rapid increase in the built-up area is largely induced by intense in fill growth in the core range of the city which is further corroborated by the Shannon entropy value, which indicates the compact nature of city growth. It has been found that built-up area has increased from 3.68% in 1972 to 18.79% in 2018. Agricultural land on the other hand decreased from 36.51–11.22% during 1972–2018 period. The spatial trend of change analysis represents the expansion of built-up land along with the transport network, and predominantly following a north–south course. Landsat image classification has also shown that Land use and land cover have largely changed during 1972–2018.

Estimating Mangrove Above-Ground Biomass Loss Due to Deforestation in Malaysian Northern Borneo between 2000 and 2015 Using SRTM and Landsat Images

Mangrove forests are highly productive ecosystems and play an important role in the global carbon cycle. We used Shuttle Radar Topography Mission (SRTM) elevation data to estimate mangrove above-ground biomass (AGB) in Sabah, Malaysian northern Borneo. We developed a tree-level approach to deal with the substantial temporal discrepancy between the SRTM data and the mangrove’s field measurements. We predicted the annual growth of diameter at breast height and adjusted the field measurements to the SRTM data acquisition year to estimate the field AGB. A canopy height model (CHM) was derived by correcting the SRTM data with ground elevation. Regression analyses between the estimated AGB and SRTM CHM produced an estimation model (R2: 0.61) with a root mean square error (RMSE) of 8.24 Mg ha−1 (RMSE%: 5.47). We then quantified the mangrove forest loss based on supervised classification of multitemporal Landsat images. More than 25,000 ha of mangrove forest had disappeared between 2000 and 2015. This has resulted in a significant decrease of about 3.96 million Mg of mangrove AGB in Sabah during the study period. As SRTM elevation data has a near-global coverage, this approach can be used to map the historical AGB of mangroves, especially in Southeast Asia, to promote mangrove carbon stock conservation.

Environmental Analysis of Land Use and Land Change of Najran City: GIS and Remote Sensing

This study critically analyzes changes in land use and land cover by means of multi-temporal remote sensing of Najran City in Saudi Arabia between 1975 and 2019. A number of remotely sensed data were employed to create multi-maps using: (a) the normalized difference vegetation index; (b) head-up digitizing; and (c) supervised classification of Landsat images using field observation and accuracy assessment, including field verification and Google Earth Professional. Criteria from a well-known technique of environmental assessment, the Building Research Establishment Environmental Assessment Method (BREEAM), were used to critically analyze land use and evaluate levels of sustainability, with particular focus on ecology. Therefore, land around Najran can be characterized as follows: (1) Najran valley; (2) agricultural land; (3) built-up areas; (4) reclaimed land; (5) basement rock; and (6) desert. The results indicate that agricultural land grew from an average of 39.81 km2 (1.07%) in 1975 to 218.51 km2 (5.9%) in 2005, although this was followed by a marked decline between 2005 and 2019. Urban land increased from 1.12 km2 (0.031%) in 1975 to 154.35 km2 (4.13%) in 2019. Furthermore, there was approximately 1289.47 km2 of reclaimed land in 1975 (i.e., 34.64% of the total area study area) but approximately 1151.1 km2 (30.86%) in 2019. There was a small amount of desert (i.e., sand dunes) in the study area, and no change was recorded in the basement rock. This study analyzed these land changes, likening them to BREEAM criteria of ecology and land use. A number of unsustainable practices were potentially resulting in serious land contamination and pollution of both surface and ground water, as well as an increased risk of flooding.

Influence of Agro-pastoral Activities on Land Use and Land Cover Change in Karamoja, Uganda

The land use and/or land cover changes (LULCC) caused mainly by human beings for their benefits play a pivotal role in a global environment, resulting in significant ecosystem changes. Iriiri, Matany and Rengen sub-counties in Karamoja sub-region of Uganda have undergone rapid LULCC in the past three decades. Nevertheless, the extent to which these changes have occurred have not been quantified. Establishing the extent of LULCC in the study area between 1986 and 2015 formed our objective. Supervised LANDSAT image classification for years 1986, 1996, 2005 and 2015 was done using ENVI 4.7 software. The classification resulted into six land use classes; Bareland, Farmland, Woodland, Grassland, Settlement, and Wetland. The area under each LULCC was subjected to a change detection analysis using Arc-GIS (ESRI, 2009) in ten years strata. The results revealed that settlement in Iriiri expanded significantly (p < 0.05) by 71.3%, while farmland increased by 45%. Woodland and grassland significantly (p < 0.05) declined by 68% and 30% respectively. Bareland increased by 56%, while wetland decreased by 54%. Woodland and grassland significant (p < 0.05) shrunk by 87% in Matany and Rengen sub-counties. Farmland expanded significantly (p < 0.05) by 147% and Woodland shrunk significantly (p < 0.05) by 79% in Rengen sub-county. Generally, farmland and settlement increased while woodland and grassland shrunk due increased human population and farming. Expansion of farming is partially due to increased human settlement to pursue agriculture following advocacy by the government of Uganda. The removal of natural vegetation is expected to negatively impact soil quality by exposing it to agents of erosion. However, the extent of these impacts is unknown. Hence, further studies on LULCC and their impact on soil quality at sub-counties level are crucial in guiding land use policy and sustainable management practices in the area.

Satellite image based flood classification in urban areas using B-convolutional networks

Spatial features with spectral properties enhance the quality of satellite image while mapping complex land cover. These features are integrated with the proposed classification approach for improving classification results. The ultimate objective of this investigation is to provide high-level features to the convolutional neural network (CNN) for mapping flooded regions (before and after) using remote sensing data. Here, boundary-based segmentation is done to recognize the dimensions and scales of objects. Modeling a fully trained Convolutional network is feasible for training a huge amount of data in remote sensing studies. Fine-tuned CNN is utilized with slight modification for attaining classified Landsat images. Classification outcomes and confusion matrix are manipulated using B-CNN are compared with classifiers like SVM, random forest (RF) to compute B-CNN efficiency.

Using shape extraction to enhance classification of Landsat satellite images to visualize vegetation

Identifying vegetation from satellite images is very necessary for studies related to agricultural sector. Enhancing the quality and classification of satellite images is a challenge especially when study areas contain complex vegetation. The study aimed to enhance the image classification based on using extraction tool of the ArcGIS. In the present study, the classification of Landsat satellite images before and after extracting specific areas covering vegetation using the ArcGIS was compared. Two regions in Egypt, one with complex vegetation located at Siwa Oasis and the other one with simple vegetation located at Abu Simbel region, were used in this study. Then, several polygons were used to extract specific areas covering vegetation from the satellite images. Images were subsequently classified using Iso cluster unsupervised classification tool. The classification outcomes were compared between the original images before extraction with images after extraction. The results showed that extraction tool greatly enhanced the quality and classification of the image and it was possible to identify different types of vegetation.

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

The lowland seasonally dry subtropical forests in central Argentina: vegetation types and a call for conservation

Aims: The native woody vegetation from the Espinal phytogeographic province in central Argentina, found in subtropical-warm temperate climates, represents part of the southernmost seasonally dry forest in South America. Although this vegetation has been studied for over a century, a complete phytosociological survey is still needed. This lack of knowledge makes its spatial delimitation and the establishment of efficient conservation strategies particularly difficult. The main goals of this study were to classify these forests and assess their current forest cover and to better define the extent of the Espinal phytogeographic province in Córdoba region, central Argentina. Study area: Espinal Phytogeographic Province in Córdoba region, central Argentina (ca. 101,500 km2). Methods: We sampled 122 stands following the principles of the Zürich-Montpellier School of phytosociology; relevés were classified through the ISOPAM hierarchical analysis. The extent of the Espinal phytogeographic province was established by overlaying previous vegetation maps, and a map showing the current distribution of forest patches was constructed based on a supervised classification of Landsat images. Results: Four woody vegetation types of seasonally dry subtropical forest were identified based on the fidelity and the abundance of diagnostic species: (1) Aspidosperma quebracho-blanco forest; (2) Zanthoxylum coco forest; (3) Geoffroea decorticans forest; and (4) Prosopis caldenia forest. These vegetation types were segregated along gradients of temperature and precipitation seasonality and soil-texture and sodium content. The remaining forest patches represent 3.43% of the extent of the Espinal province in Córdoba region of which only 1.05% is represented in protected areas. Conclusions: We present a classification of the Espinal forest based on a complete floristic survey. Despite the dramatic forest loss reported, our results show that some forest patches representative of the Espinal are still likely to be found in the area. However, urgent measures should be taken to establish new protected natural areas in order to preserve the last remaining forest patches. Taxonomic reference: Catálogo de las Plantas Vasculares del Cono Sur (Zuloaga et al. 2008) and its online update (http://www.darwin.edu.ar). Abbreviations: ISOMAP = isometric feature mapping; ISOPAM = isometric partitioning around medoids.