Victorian Land Use Information System 2014/15

and cover mapping data is an annual component of the Victorian Land Use Information System, the VLUIS. The land cover information has been created specifically for the VLUIS using time series analysis of the MOD13Q1 or MYD13Q1 products produced by NASA using data collected by the MODIS sensor and freely available on the Reverb | ECHO website. Ground data is collected annually across Victoria using a stratified random sampling approach for calibration of the annual seasonal curves and validation of the classification output. The ground data is split into three groups with 50% used to develop classification rules, 25% used to produce interim validation results that feed back into the rule development process with the remaining 25% used to independently validate the final classification. Error matrices for each land cover dataset from 2009 have been produced from this final validation. The TIMESAT GUI is used to create smoothed annual time series for the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Red and Near Infrared (NIR) MOD13Q1 or MYD13Q1 bands using the Savitsky-Golay algorithm. A time series of 21 images was used and a suite of 11 seasonal parameters created that each numerically describe features of the annual seasonal curves for each band. In addition the standard deviation of the annual seasonal curve is calculated for each band and used in conjunction with the seasonal parameters. A three-tiered hierarchical classification was developed to assign a dominant land cover class to each pixel. Initially, rules developed using the data mining tool See5 and / or expert knowledge were applied to the seasonal parameters and the annual standard deviation in conjunction with a GIS data-set of water bodies greater than 12.5ha in area to classify each pixel as either Tree, Non-tree or Water based on two data sets from the corporate spatial data library, HY_WATER_AREA_POLY.shp and VM_LITE_HY_WATER_AREA.shp; and are combined to form the water bodies layer. In addition, the primary classes are cross checked using data from preceding and following years to reduce misclassification prior to the secondary classification. A secondary classification developed using rules based on expert knowledge and / or See5 is applied to split the primary class Tree into the secondary classes Native Woody Cover and Treed Production and the primary class Non-tree into the secondary classes Pasture/ Grassland and Crops. Finally, a tertiary classification further divides the secondary class Treed Production into the tertiary classes Hardwood Plantation, Softwood Plantation and evergreen or deciduous Woody Horticulture and the secondary class Crops into the tertiary classes Brassicas, Legumes, Cereals and Non-Woody Horticulture based on rules developed using the data mining tool See5 and modified where appropriate by expert knowledge. Additional information on land cover mapping, including map symbology, can be found on Victorian Resources Online.

Land cover mapping data is an annual component of the Victorian Land Use Information System, the VLUIS (refer to separate record). The land cover information has been created specifically for the VLUIS using time series analysis of the MOD13Q1 product produced by NASA using data collected by the MODIS sensor and freely available on the Reverb | ECHO website. Ground data is collected annually across Victoria using a stratified random sampling approach for calibration of the annual seasonal curves and validation of the classification output. The ground data is split into three groups with 50% used to develop classification rules, 25% used to produce interim validation results that feed back into the rule development process with the remaining 25% used to independently validate the final classification. Error matrices for each land cover dataset from 2009 have been produced from this final validation. No ground data was available prior to 2009 and so error matrices do not exist. The TIMESAT GUI is used to create smoothed annual time series for the Normalised Differenced Vegetation Index (NDVI) and the Red and Near Infrared (NIR) MOD13Q1 bands using the Savitsky-Golay algorithm. A suite of 11 seasonal parameters are created that each numerically describe features of the annual seasonal curves for each band. In addition the standard deviation of the annual seasonal curve is calculated for each band and used in conjunction with the seasonal parameters. Prior to classification, the ground data and the MOD13 data is stratified into regions with similar climate, soils and farming systems using the Primary Production landscapes developed by DEDJTR. For each stratified region a three-tiered hierarchical classification was developed to assign a dominant land cover class to each pixel. Initially, rules developed using expert knowledge were applied to the seasonal parameters and the annual standard deviation in conjunction with a GIS data-set of water bodies greater than 12.5ha in area to classify each pixel as either Tree, Non-tree or Water based on two data sets from the Victorian spatial data library, HY_WATER_AREA_POLY.shp and VM_LITE_HY_WATER_AREA.shp; and are combined to form the water bodies layer. In addition, the primary classes are cross checked using data from preceding and following years to reduce misclassification prior to the secondary classification. A secondary classification developed using rules based on expert knowledge is applied to split the primary class Tree into the secondary classes Native Woody Cover and Treed Production and the primary class Non-tree into the secondary classes Pasture/ Grassland and Crops. Finally, a tertiary classification further divides the secondary class Treed Production into the tertiary classes Hardwood Plantation, Softwood Plantation and Woody Horticulture and the secondary class Crops into the tertiary classes Brassicas, Legumes, Cereals and Non-Woody Horticulture based on rules developed using the data mining tool See5 and modified where appropriate by expert knowledge. This tertiary classification is not available for the 2006 to 2008 data. A mask has been applied to remove 'urban' pixels that comprise land parcels less than 6.25 Ha in size.

Water Accounting Plus for Water Resources Reporting and River Basin Planning

اهداف: پایش تغییرات کاربری‌ها و درک پویایی آن در یک حوضۀ آبخیز، از جایگاه خاصی در مدیریت پایدار آن حوضه برخوردار است. هدف تحقیق حاضر، استفاده از سنجش از دور و GIS جهت تهیۀ نقشۀ تغییرات و شناسایی انتقالات کاربری اراضی و پوشش زمین با به‌کارگیری ماتریس انتقال و تصاویر ماهوارۀ لندست در حوضۀ آبخیز دریاچۀ ارومیه می‌باشد. روش: جهت انجام تحقیق، از تصاویر ماهوارۀ لندست در دورۀ زمانی 2015 ـ 1988 استفاده گردید. بدین‌منظور پس از انجام پیش‌پردازش‌های موردنظر، جهت انجام طبقه‌بندی از روش‌های ماشین‌بردار پشتیبان و روشیءگرا استفاده و سپس اعتبارسنجی گردیدند. همچنین جهت برآورد میزان انتقالات و دیگر ویژگی‌های حوضۀ آبخیز دریاچۀ ارومیه، ابتدا ماتریس انتقالی استخراج شده و سپس طبقه‌بندی شئ‌گرا بین دوره‌های زمانی 2015ـ1988 ارائه شد. سپس با استفاده از فرمول‌های موردنظر، میزان پایداری، افزایش، کاهش، تغییرات کل، تغییرات خالص و مبادلۀ همزمان کاربری‌های اراضی و پوشش زمین مشخص گردید. یافته‌ها/ نتایج: پس از ارزیابی صحت، صحت کلی برای نقشه‌های حاصل از ماشین بردار پشتیبان و روش شئ‌گرا به‌ترتیب برابر با 94 و 92 درصد و مقدار کاپای آنها به‌ترتیب 92 و 89 برآورد شد که نشان‌دهندۀ برتری روش شئ‌گرا در مقایسه با روش ماشین بردار پشتیبان است. در کل، هر دو روش طبقه‌بندی توانستند صحت قابل‌قبولی برای نقشه‌های کاربری اراضی و پوشش زمین ارائه دهند. نتایج حاصل از انتقالات نشان داد به‌طور میانگین، 59 درصد از چهرۀ زمین در حوضۀ آبخیز دریاچۀ ارومیه در فاصلۀ زمانی 2015ـ 1988 پایداری پوشش داشته است، که بیشترین میزان این تداوم براساس مقدار این کاربری در فاصلۀ زمانی 2015ـ1988 مربوطه به مناطق مسکونی می-باشد. حدود 14 درصد از سطح حوزۀ آبخیز دریاچۀ ارومیه به‌صورت تبادل همزمان بوده است. همچنین سطوح آبی حوضۀ آبخیز دریاچۀ ارومیه در دورۀ زمانی فوق، بیشترین ازدست‌دادگی و کمترین تبادل همزمان را تجربه کرده است. نتیجه‌گیری: حوضۀ آبخیز دریاچۀ ارومیه در این فاصلۀ زمانی (2015ـ1988) تغییرات و انتقالات شدیدی را تجربه کرده است، تاجایی‌که تنها 59 درصد از چهرۀ زمین، ثابت مانده و قسمت‌های دیگر، انواعی از انتقال‌ها را تجربه کرده‌اند. همچنین سطوح آبی و سپس مراتع، بیشترین آسیب-پذیری را تجربه کرده‌اند که نشان از افزایش اراضی فاقد پوشش و اراضی زراعی (کشاورزی) می-باشد. این تجزیه‌وتحلیل ما را به سنجش و تجسم میزان انتقالات عمدۀ LULC درجهت برنامه‌ریزی آیندۀ حوضۀ آبخیز دریاچۀ ارومیه توصیه می‌کند.

As one of the most important study fields of global change, land use and land cover change has significant impacts on regional and global climate, soil characteristics, and function of terrestrial ecosystem. Most researchers, both in China and abroad, have given much more attentions to the study on land use types and the laws of regional land cover change with synthetic analysis of the factors that influence land use and land cover change. In recent years, some Chinese researchers have carried on studies in this field by stationary inspection methodology, e.g., Professor Fu Buojie and others studied the impacts of land use and land cover change on soil nutrients, regional hydrological condition in loess hilly areas and Zunhua low mountainous areas in Hebei province, Professor Shi Peijun and others studied the impacts of land use and land cover change on natural agricultural disasters in Inner Mongolia Autonomous Region on the basis of long period investigations and statistical materials. But few researchers studied the impact of land use and land cover change on soil erosion by stationary methodology, and few papers have been published in this area. This paper studied the impacts of land use and land cover change on soil erosion in Fujian mountainous areas on the basis of analysis on long period observational and experimental materials at Jianou Niukenglong Experimental Station and the Provincial Soil and Water Conservation Station, researched soil erosion mechanisms of mountain grassland ecosystem, and different soil erosion modulus under different land use and land cover types. The analytical results indicate that the coefficient of runoff has minus linear relation with grassland coverage, and the modulus of soil erosion has index relation with grassland coverage. This paper also studied the mechanisms and processes of land cover impacting runoff and soil erosion, i.e., land cover influences runoff and soil erosion through the following processes: 1) The grassland cover reduces the forces of rainfall that beats on earth surface, which will impact soil erosion on the surface of the earth, grassland cover has minus index relation with soil erosion modulus, and minus linear relation with coefficient of runoff. 2)The roots of vegetation strengthens the erosion resisting capacity of soils through interluding, twinning and fixing forces on soil particles, and increases the absorbing water capacity of soils . 3) The increase of soil organic material concentration makes the soil particles and structure more and more stable. This paper also points out that there are other mechanisms and processes that need to be further studied, e.g., the relation between land use/land cover and runoff coefficient, the variations of different vegetation's impacts on soil erosion,etc.

In recent years, land use and land cover plays a pivotal role in global environmental change. Under these circumstances,the need of a new dimension for detecting land use and cover is getting more imperative for conservation and effectivemanagement of land use and cover types. Importantly, the use of information technology to support decision making indetecting land use and cover is essential and recent. One of the technologies used is Airborne Remote Sensing. Theobjective of this study is to identify, quantify, classify and map land use and land cover mapping in Setiu, Terengganuusing UPM-APSB’s AISA airborne hyperspectral remote sensing. Detection of land use and cover was performed usingairborne hyperspectral imaging data taken on 20 April 2006 with the support of existing land use and cover maps. Thesize of the study area is 100 ha. The image was displayed in ENVI 4.0 Software using bands 202217 (RGB)combination. The data were then enhanced and classified for different land use and cover classes. From the dataanalysis, the image can be classified into eight classes. The classes are 2-3 years old oil palm plantation, 4-5 years oldoil palm plantation, young (3-4 years old) rubber plantation, matured (15-17 years old) rubber plantation, vegetationcrops, open area, road and river. The land use and land cover classes area distribution of the plots under study in Setiu,Terengganu were 4.18 ha, 8.58 ha, 6.26 ha, 70.43 ha, 2.98 ha, 2.31 ha, 2.78 ha, and 2.48 ha. Overall, the classificationaccuracy of interpretation of the airborne imagery for land use and cover in Setiu, Terengganu is 89.51 and kappacoefficient is 0.86. This study shows that, airborne hyperspectral remote sensing technique is capable in identifying,quantifying, classifying and mapping land use and cover in Setiu, Terengganu, hence a good decision support tool inland use and cover planning and management.

In recent years, the availability of georeferenced data has increased substantially, as have the number of producers and users of this information. As a consequence, there is a growing need for harmonization of data, not least in its classification descriptions. Unfortunately, inadequate metadata hampers understanding of how data sets are produced and what data classes represent. This study describes how five different categorical geodata sets for Denmark, ranging from habitat registrations through maps of agricultural land use to national topographic data, are integrated and how the integrated data set is reclassified to land-use and land-cover classes. All five data sets differ with respect to data acquisition, and description and classification methodologies, and none of the data distinguish between land use and land cover. The purpose of the reclassification was to produce maps of land use and land cover, with classes being compatible with the land cover classification system (LCCS) from the Food and Agriculture Organization of the United Nations. We identified land-cover and land-use classes from the LCCS that matched Danish conditions and cross-tabulated those classes with classes from the integrated Danish data set. Based on the semantic meaning of the class names from the integrated data set, we used heuristic associational knowledge to estimate their membership in the land-use and land-cover classes. The results are three land-use maps and five land-cover maps, indicating qualitative estimates of the presence of land-cover classes measured on an ordinal scale.

Forest and wood land ecosystems in Tanzania occupy more than 45% of the land area, more than two thirds of which made up of the Miombo woodland. The main form of land use in the Miombo region has long been shifting and small-scale sedentary cultivation. The lack of infrastructure and prevalence of deadly diseases such as malaria and trypanosiomiasis have long limited extensive clearance for cultivation, livestock farming and settlements. However, due to positives changes in the socio-economical, political and technological setup in miombo region, the types and intensity of land use are now changing. This paper discusses preliminary results from a study conducted with the aim of contributing to the understanding of dynamics of land cover and use changes in miombo woodlands of eastern Tanzania. The study area comprises four villages around the “Kitulangalo Forest Reserve”, 140 km west of Dar es Salaam on either side of the Morogoro-Dar es Salaam highway. Landsat MSS satellite images of July 1975, Landsat TM satellite images of July 2000 were used to assess land cover changes between 1975 and 2000. Participatory Rural Appraisal (PRA), questionnaire survey and checklists for key informants were the major methods used for collecting socio-economic data. The land cover/use class of woodland with scattered cultivation has recorded the highest percentage of change between July 1975 and July 2000. While all other classes have registered positive changes, only the closed woodland class has had negative change meaning that this class has been decreasing in favour of other land cover/use classes. Recent land cover and use changes are drastic in the study area. These changes have been triggered largely by varied factors including mainly increased population density and subsequent economic activities. Economic activities including charcoal business, shifting cultivation, opening up of improved highway and pastoralism in the study area have greatly contributed to deforestation and woodland degradation. In light of these findings, there is need for: (1) Adequate land use planning and survey of village lands so as to avoid exacerbation of land use conflict and environmental degradation in the study area. (2) Agrarian reforms to eliminate open access regimes to natural resources. (3) Enforcement of fiscal policies related to the extraction of natural resource products such as timber and charcoal so as to reduce pressure on woodlands. Keywords: land use – cover change – Kitulangalo – miombo woodlands

Ensuring the ecological sustainability of the territory is possible in the case of the optimal ratio of different types of lands. In particular, it is important to have sufficient areas of eco-stabilizing lands (forests, meadows, protected areas, etc.). Of great importance are the features of land use in coastal areas, areas with high steep slopes and areas with soils susceptible to degradation. Ecologically sustainable land use can reduce the risk of soil degradation processes, provide an optimal microclimate, good hydrological conditions and promote the protection of small rivers.
 To provide recommendations for improving the structure of land use in the Poltva river basin, a map of optimization of the structure of land use using the ArсGIS 10.0 program was drawn up. For this purpose, a map of slope steepness was constructed, an interpretation of space images was made to compile a map of land use structure and a map of the soil cover was digitized. As a result of the analysis of the map of land use structure the peculiarities of the ratio of types of lands in different parts of the studied territory by integral indicators are determined. The coefficients of anthropogenic load, ecological stability of land use, ecological stability of landscapes, ecological balance and others are calculated. Areas with different degrees of anthropogenic transformation of lands are identified. The relationship between the peculiarities of natural conditions (geological structure, relief, soil cover) and land use of the territory is analyzed.
 As a result of overlapping maps of slope steepness, soil cover and land use, areas with different risk of soil degradation processes and plowed coastal areas were identified. The map of land use structure optimization is compiled according to the method of allocation of ecological and technological groups of lands. According to this technique, lands are differentiated by the steepness of the slopes.
 The proposed optimization of land use involves the use of phytomeliorative and agrotechnical measures: plowing across the slope, soil-protective crop rotations, the creation of forest belts, land conservation, etc. Recommended optimization measures include the withdrawal from agricultural cultivation of peatlands, steep slopes, floodplains, hollows, coastal protection zones of rivers and streams. The implementation of the proposed optimization measures will reduce plowing, increase the share of eco-stabilizing lands and improve the integral indicators characterizing the ratio of different types of lands. A compiled map of land use structure optimization can be used for land management and environmental activities.
 Key words: anthropogenic load, land use, ecological and technological groups of lands, river basin, optimization, Bug river.

The present study focuses on the economic, political/institutional, technological, cultural, demographic and environmental drivers of land use change. It aims to understand the factors influencing land use decisions at the household level, in particular the influence of migration. The study is guided by the hypothesis that international migration is driving land use change through the investment of remittances, funds sent back by migrants to their families in the country of origin. This research is based on a political ecology approach and the conceptual framework relies on three theoretical concepts. First, the concepts of proximate causes and driving forces were used to identify the factors behind changing land use. In addition, the concept of remittance landscapes, a concept developed in the framework of this study, which is defined as an emerging type of landscape driven by the investment of remittances, was used to evaluate the impact of remittances on land use in the study area. Fieldwork was conducted in the municipality of Autlán in the state of Jalisco in Mexico over a total period of 8 months between 2002 and 2004. Land use changes between 1990 and 2000 were quantified based on satellite image analysis. Underlying driving forces of these changes were examined based on land use change data collected by survey as well as data available from municipal, state and federal agencies. Land use changes observed in the study area between 1990 and 2000 include a slight increase of agricultural land (2%), of urban land cover (0.5%) and of pine-oak forest (0.7%). Over the same period, pasture increased by 18% while dry forest decreased by 10%. Rapid and extensive land use change is occurring on rainfed agricultural land, as maize cultivation is converted to the cultivation of agave azul used for the production of tequila. The first plantations of agave azul were established in 1996 and by 2002, agave azul was planted on 33% of all rainfed agricultural land of the municipality. 84% of owners of rainfed land included in the survey had changed land use from maize to agave during this time period. The dynamics of several proximate causes are driving this change: 1) Market prices for maize decreased by 46% between 1994 and 2004 while the costs for agricultural inputs continually increased so that the cultivation of rainfed maize was no longer economically profitable; 2) The variability of rainfall combined with a lack of irrigation water limits the choice of economically viable alternatives to agave azul; 3) In the large majority of cases, landowners rent out their land to tequila companies in reverse leasing arrangements for seven-year periods (the duration of one growing cycle of agave azul). During this time they do not have to work on their own fields and are free to find off-farm employment or to migrate to the US and; 4) Landowners continue to receive agricultural subsidies even though the land is rented out, as agave azul is one of the eligible crops. Overall, the main driving forces identified in the study area are economic (market prices), environmental (variability of rainfall, soil quality, topography), political/institutional (agricultural subsidies, land tenure) and demographic (labor availability). Technology and culture appear to be less important. Results of the present study confirm the hypothesis that global factors, especially international trade agreements such as NAFTA (North American Free Trade Agreement) increasingly influence land use change. However, they are not sufficient to function as a sole driver of land use change. Environmental factors are a critical determinant of whether a certain land use change will occur or not. The decisive aspect behind the observed land use changes are the multiple interactions between specific factors at different levels and not the predominance of one particular driving force functioning at a particular level. International migration is a significant livelihood strategy in the study area, especially for lower-income communities. On average, 50% of all households have or had at least one family member in the US as a migrant between 1980 and 2004, and remittances represent 45% of total household income. In general, the bulk of remittances income is used for subsistence needs and to repay debts. Nevertheless, on average, 30% of migrant households invest remittances in land, livestock, agricultural production and in house construction. All these investments lead to land use changes. The impact of remittances on land use changes is variable, and depends on the socio-economic, political and environmental context of the community and the individual situation of the migrant household. In low-income communities, remittances might be used to repair existing housing, while in higher-income communities, remittances are used to construct a new house, converting agricultural to urban land. With regard to changes in labor availability due to out-migration, the results are ambiguous. Migration can drive land use change by encouraging a shift to low-labor land use systems, but these land use changes that require less labor can also drive migration. The concept of remittance landscape developed by the researcher has proved useful for analysing the impact of remittances on land use changes. A combination of area-based and actor-based evaluation criteria are effective in order to describe quantitative as well as qualitative landscape transformations driven by the investment of remittances. Landscapes where the investment of remittances leads to a change of land use from subsistence to cash crop cultivation should be included as a potential type of remittance landscape, even though the basic type of the landscape (agricultural) remains unchanged. Accordingly, at least six different types of transformations into remittance landscapes are possible: a) forest to pasture, b) forest to agriculture, c) forest to urban, d) agriculture to pasture, e) agriculture to urban and f) change of agricultural system. In conclusion, the study area on which this research focused is not considered to contain any remittance landscapes because remittances are only partially driving the extensive land use changes occuring in the region.

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

Modification of the Earth’s surface i.e. land use change, is the main human activity for survival and is the key player in the management of natural resources, including water. Little attention has, however, been given to understand the role the territorial vegetation changes may play in strategic management of water resources. In the basin of Aswa northern Uganda, the changes in land use due to complex demographic and social economic factors is among the numerous challenges faced in management of the limited water resources in the area. The aim of the current study was to explore the opportunities land use changes in the basin may offer to water resources management, looking mainly at the expansion in future agriculture and afforestation as the critical land use change issues. The study was structured into four broad objectives: The first objective was to generate the reference land use dataset (1986 & 2001). The available techniques (the supervised and the unsupervised image classification) were explored using Landsat multi-spectral images. Through careful evaluation, the supervised image classification with the best classification accuracy of 81.48% was used to generate 1986 and 2001 land use maps. The second objectives of the study was to generate experimental land use scenarios required for testing the effect of spatial land use policies on hydrologic processes in the basin. The Multi-criteria-GIS methodology was developed and six experimental land use scenarios were generated using simple but consistence set of bio-physical and socio-economic parameters. The third objective was to customise the hydrologic process model SWAT that was used to simulate the hydrologic impact of the land use change scenarios. The calibration of the hydrologic model SWAT used monthly historical streamflow records from 1970 to 1974 recorded at the basin outlet. The model was manually calibrated using the Nash-Sutcliffe coefficient as objective function. The efficiency of the model during calibration was 0.46. Validation of the model using an independence monthly streamflow records from 1975 to 1978 was done and the model efficiency was 0.66, much better than in calibration period. The forth and last objective of the study was to simulate the hydrologic processes in the reference years and the hydrologic processes impacted by the land use change scenarios and to evaluate how this impact affects water resources management strategies. An independent validation of the model to identify the validity of extending the optimal parameters set in simulation of 2001 and land use change hydrologic processes was carried out by comparing the simulated actual evapotranspiration fraction with estimated actual evapotranspiration fraction obtained using surface energy balance method and the thermal MODIS images. Validation indicated acceptable model performance in simulating 2001 hydrologic processes, with a spatial correlation coefficient of 0.45. The application of the model in simulations of the hydrologic processes in the reference years noted that 2001 had more water yield than 1986 by 9.2 mm. The analysis of the impact of land use change in the reference years indicated an increase of 2.52 mm of water yield in the year 2001. Simulation of the hydrologic impact of the experimental land use indicated that Land use types, which in this study were restricted to plantation forest and generic agriculture, land use extent and location of the land use with respect to precipitation rate and amount, greatly influence the hydrologic process of the basin and the net water yield. It was noted that the water yield of the basin can be significantly decreased by over 15%, if more than 37% of the plantation forests are introduced in the wet zone. In the dry sub-basins however, afforestation of up to 42% had insignificant effect on water yield, which could therefore be exploited so as to offset the afforestation pressure in the wet sub-basin while at the same time enhancing the basin water yield. The effect of agricultural land use change on water yield was however less sensitive to climatic zones. 53% increase in agricultural land cover responded with an increase in water yield by about 27%.

Land Use Information is a key dataset required to enable an understanding of the changing nature of our landscapes and the associated influences on natural resources and regional communities. The Victorian Land Use Information System (VLUIS) data product has been created within the State Government of Victoria to support land use assessments. The project began in 2007 using stakeholder engagement to establish product requirements such as format, classification, frequency and spatial resolution. Its genesis is significantly different to traditional methods, incorporating data from a range of jurisdictions to develop land use information designed for regular on-going creation and consistency. Covering the entire landmass of Victoria, the dataset separately describes land tenure, land use and land cover. These variables are co-registered to a common spatial base (cadastral parcels) across the state for the period 2006 to 2013; biennially for land tenure and land use, and annually for land cover. Data is produced as a spatial GIS feature class.

Obtaining land-use information from a remotely sensed land cover map: results from a case study in Lebanon

Mapping farming systems in an agricultural landscape is important for quantifying ecosystem services provided by landscapes such as management of pests and pathogens, conservation of beneficial arthropods, improving crop pollination and crop yields. Landscape structure is defined as the spatial arrangement (configuration) of land cover types (composition) that influence ecological processes and biodiversity distribution at varying scales. Human induced landscape modification of once-pristine natural environment has resulted to habitat loss and disturbance of species communities and their interaction, which has compromised on ecosystem functioning and service provision. Big data from earth observation and machine learning algorithms provide unprecedented opportunity for mapping these agricultural landscapes. However, continuous cloud cover and intercropping in smallholdings poses challenges in landscape characterisation especially in the tropics. We conducted our study in Murang'a County, which is one of the five counties in central Kenya that grow coffee and tea. Coffee grows in four sub zones of the upper Midland (UM) agro-ecological zone that cuts across an elevation gradient of 1300 – 2000 masl. UM1 is the transition zone for growing tea and coffee. UM2 is the major coffee growing zone whereas UM3 is the marginal coffee zone. At UM4, coffee is grown under irrigation. We explored multi-source satellite images to evaluate the best dataset with least cost for mapping coffee farms and identified fragmentation levels of cover types in each agroecological subzone. Using reference data from Google Earth, commercially available PlanetScope (3m spatial resolution) and freely available Sentinel 2 (10m spatial resolution) and Landsat 8 (30m spatial resolution) images combined with vegetation indices (VI) were tested using a random forest classifier. The dataset with the highest accuracy for land use land cover classification was further analyzed using fragstats software to measure landscape fragmentation. 13-band Sentinel 2 had the highest accuracy for mapping coffee (kappa - 0.98) compared to 4-band PlanetScope (kappa – 0.85) and 11-band Landsat 8 (kappa – 0.88). Despite having the highest spatial resolution, PlanetScope had the lowest accuracy which only improved when combined with VI. The final land use land cover map from Sentinel 2 mapped the following land use land cover classes: annuals, bananas, bareland, coffee, agroforest, grassland, perennials/shrubs, settlements, tea and waterbody. Coffee covered over 50% of the total landscape in UM1 and UM2 while annual crops occupied 43% of the total landscape in UM3. Other perennials and annual crops occupied 29% and 22% of UM4 respectively. The results also showed that Coffee was highly fragmented in UM3 and UM4 with the largest patch occupying 3% and 1.4% of the total landscape compared to UM1 which occupied 62% of the total landscape. Additionally, Agroforest and bananas were more fragmented in UM2 and UM3 while annual crops, shrubs, grassland and settlements in UM4. We show in this study that Sentinel 2 is the most robust satellite data to map land use and cover types with high level of accuracies even in challenging landscapes such as smallholder agroecosystems. This is due to high number of spectral bands that delineates vegetation type more accurately compared to other satellites. Furthermore, it has the added advantage of being freely available; this dataset can be taken up easily in resource-constrained projects. UM3 and UM4 are the marginal coffee growing zones. Within these landscapes, coffee is highly fragmented and interspersed with annual crops which are the dominant cover type. Naturally, coffee grows as an understorey crop. At UM3 and UM4, agroforest trees are also highly fragmented, hence, within these landscapes coffee is more vulnerable to pest and disease infestation, low crop yields and adverse effects of climate change compared to coffee in UM1 and UM2.