30-m Spatial Resolution Research Articles

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

The objective from this article evaluates the thematic quality of automatic mappings with supervised classification for land use and land cover, using high spatial resolution satellite images as "ground truth". This consider the advancement of remote sensing technologies has enabled the acquisition of satellite images with various spatial resolutions, which are essential for thematic mapping and automatic classifiers in the context of land use and land cover mapping. In fact, the wide availability of high, medium, and low spatial resolution satellite images has significantly optimized the time and resources required by using more accurate classifiers during data processing. The image used for verification of this paper was GeoEye, with a spatial resolution of 0.5m, dated October 2023. The images submitted to the automatic classifier were Sentinel-2A with a spatial resolution of 10m and Planet with a spatial resolution of 5m, both from the same satellite pass period (October 2023) over the study area, aiming to avoid seasonal and phenological variations in vegetation, as well as changes in the environment due to anthropogenic intervention. The classification method adopted was Maximum Likelihood (MAXVER). The classification accuracy was rigorously evaluated to ensure the reliability of the results using the Kappa index, assessing the agreement between the observed and expected classifications. Based on the methods presented, the set of mapped classes in this study showed good accuracy for the Planet image and very good accuracy for the Sentinel image.

A dataset of NDVI for the vegetation growing season in Central Asia with a resolution of 250 m (2001–2020) and 30 m (2020)

Central Asia is the largest arid and semi-arid region in the Northern Hemisphere, and its ecological environment is extremely fragile and susceptible to the effects of global climate change. Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its unique geographic location. Vegetation serves as a significant indicator of the ecological environment, and the spatial and temporal distribution pattern, as well as the change trend of vegetation, are important indicators in evaluating the ecological status of the region. The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to study vegetation, which characterizes the spatio-temporal changes of vegetation. This dataset utilized MODIS13Q1 to generate long-term time series growing season mean NDVI data with a spatial resolution of 250m in Central Asia from 2001 to 2020. To obtain the 2020 growing season mean NDVI data with a higher spatial resolution of 30m, the Cubist algorithm based on rule segmentation regression was utilized to fuse the Landsat data and MODIS data. Meanwhile, this dataset carried out quality control of data products from three aspects: data source quality control, model training optimization, and model independent verification to ensure the accuracy and reliability of data. The generation of this dataset provides powerful data support for the analysis of vegetation dynamic change and spatial pattern in Central Asia.

Spatial high-resolution modelling and uncertainty assessment of forest growing stock volume based on remote sensing and environmental covariates

Accurate forest growing stock (GSV) mapping is crucial for informed land management decisions, climate change mitigation, and sustainable resource utilization. This study aimed to digital mapping and uncertainty assessment of GSV levels in the Bashkiriya Nature Reserve, Russia. A random forest approach was used to predict GSV at a spatial resolution of 10 m (10 ×10 m) using 8395 plots and a set of 36 environmental covariates representing remote sensing data, relief and climate variables. The results revealed that the GSV levels ranged from 1 to 439 m3/ha, with a median of 200 m3/ha. Our model was evaluated using cross-validation and three performance metrics: root mean square error (RMSE=76 m3/ha), coefficient of determination (R2 =0.55) and Nash-Sutcliffe efficiency coefficient (NSE=0.55). In addition, we assessed the uncertainty of our predictions using a 90% prediction interval. According to the assessment of covariate importance, the significance of explanatory variables decreased in the following order: remote sensing > terrain > climate. We found, that Sentinel-2A satellite data, along with spectral indices, played a pivotal role in our spatial prediction model. Among these variables, the SWIR 12 band emerged as a standout contributor, highlighting the importance of this spectral information in GSV mapping. According to the generated map, the highest GSV values were identified primarily in regions characterized by low-lying terrain with mature trees of the reserve, while the lowest levels were distinctly evident on treeless mountain peaks. While it is widely acknowledged that remote sensing constitutes a primary source for modelling forest properties, our research has underscored the significance of the SWIR spectrum and elevation in future forest investigations. An advantage of the produced map of GSV and their associated uncertainties is that the end users can incorporate these uncertainties into decision-making processes involved in forest and environmental planning.

A dataset of normalized difference vegetation index in the Three-river Headwaters during 2000-2018

Vegetation is an important component of the terrestrial ecosystem. The changes of vegetation are assumed to well indicate the dynamic changes of the ecosystem. However, the changing global climate and the intensifying human activities have a great effect on vegetation growth, which particularly highlights the implications to monitor and assess vegetation changes. Vegetation changes are usually measured by vegetation indexes. The normalized difference vegetation index (NDVI) based on remote sensing is widely used in the studies on vegetation changes and climate impact. In this study, we used the spectral reflectance data product (MOD09Q1) of the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2018 to calculate the NDVI (with a spatial resolution of 250m and temporal step of 8 days). The S-G filtering method of the TIMESAT3.2 software is applied to remove the noise in the NDVI time series for the reconstruction of time series. In this way, we finally obtained this dataset, which is open to the public for sharing and downloading. It is expected to support further studies on the dynamic changes of vegetation in the Three-river Headwaters.

Temporal expansion of the nighttime light images of SDGSAT-1 satellite in illuminating ground object extraction by joint observation of NPP-VIIRS and sentinel-2A images

Poverty is the leading cause of social instability around the world. Reducing poverty has become a crucial objective for global sustainable development. Timely and consistently monitoring poverty status plays a vital role in evaluating the efficiency of poverty reduction policies. Nighttime light images from satellites allow for frequent monitoring of poverty status by recording lighting intensity. The coarse spatial resolution of the recent, widely used, publicly available nighttime light images hinders research on smaller unit scales, such as county-level. The SDGSAT-1 satellite, with a glimmer sensor and a spatial resolution of 10-m, was launched in 2021 to meet the sustainable development goals. This study proposes a model to sharpen the existing nighttime light images with a coarse spatial resolution to expand the available period of nighttime light images with a 10-m spatial resolution in discriminating illuminating ground objects. The expanded nighttime light images with discriminated illuminating ground objects are then utilized to calculate the time-series county-level poverty index for three typical Chinese urban agglomerations with a statistically significant correlation coefficient of 0.675 (p < 0.01). Compared to the poverty statistics derived from the Chinese county statistical yearbook, the estimated poverty index can explore county-level poverty status change more effectively by completing the missing census. The time-series county-level poverty analysis demonstrates the effectiveness of Chinese endeavor in poverty reduction. >90% of the counties experienced a reduction in poverty in Beijing–Tianjin–Hebei region due to time-series economic development. Statistical analysis of the poverty index of impoverished counties demonstrates a remarkable reduction of poverty in the study areas and the significance of the persistent poverty reduction policy implementation. This study's proposed pipeline compensates for the lack of nighttime light images with a higher spatial resolution prior to 2021 and provides a reliable poverty analysis foundation for sustainable development and policymaking.

A dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015

The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.

Mapping and dynamic monitoring of military training-induced vegetation cover loss using Sentinel-2 images and method comparison.

Sustainable management of the US Army installations is critical for military training and readiness of forces. However, monitoring military training-induced vegetation cover disturbances using remote sensing data is challenging due to the lack of methodology for optimizing the selection of spectral variables or predictors and spatial modeling methods. This study aimed to propose and demonstrate a methodological solution for this purpose. The study was conducted in the Fort Riley installation in which three training areas were selected to map and monitor the training-induced vegetation cover loss. Sentinel-2 images and field observations of percentage vegetation cover (PVC) were combined at a spatial resolution of 10m by 10m to map PVC and its dynamics by comparison of two predictor selection methods and five spatial modeling algorithms based on a total of 304 spectral variables from the images before and after the training. Results showed that overall, the correlation-based predictor selection method reduced the relative root mean square error (RRMSE) of PVC predictions by 4.44% than the random forest (RF)-based predictor selection. Machine learning methods including RF, neural network, and support vector machine overall reduced the RRMSE of PVC predictions by 42.83% compared with multiple linear regression and k-nearest neighbors. Combining correlation-based predictor selection and RF modeling, coupled with leave one out cross validation, provided the greatest potential of increasing the accuracy of monitoring the vegetation cover loss. The findings provided useful implications to develop a near real-time system of monitoring military training-induced vegetation cover loss.

Site suitability analysis for rural development using geomatics technology in Maysan province/Iraq

Based on the latest statistics, the number of villages decreased by (18%) of the total number of villages between 1982 and 2019, which indicates an urgent need for a plan to develop the rural settlements in Iraq for spatial balance with the urban area, and the most important challenges and risks that facing this process are the mechanism and criteria for selecting a (mother) village that is a candidate for development with the current situation, to serve the optimum number of inhabitants in a group of neighbouring villages and building modern villages. The overall and main objective of this research was to select potential areas suitable for villages that will be candidates for rural development in Maysan Province south of Iraq by using geographic information system (GIS) methods and remote sensing (RS) techniques. The main data used for this study were Sentinel-2 satellite images with a spatial resolution of 10m; a digital elevation model (DEM) with 12.5m spatial resolution, a topographical map of the study area, the layer of village locations in rural areas, with the data of all these villages included in the comprehensive survey of villages in the Maysan Province. Population, agriculture activity, infrastructure, public services, economic projects, natural characteristics, and site properties, for rural regions are defined as important criteria for identifying sites of select villages for rural development. So, all the thematic layers are assigned weightage depending on importance and priority. Then all the layers are integrated into (The suitability Modeler) one layer by one to produce suitable village sites and demarcated as mother villages candidates for rural development in the study area. The final suitability map for villages of the study area was prepared on ArcGIS Pro 2.8 program and labelled as very high, high, moderate, low, and very low suitable regions out, of the total villages, 52 villages (9.2%) lie in 13 sub-districts (called Nahia), on the middle-south-west parts, along the rivers in Maysan Province, was very high suitable to the candidate for rural development.

Characteristics of Tidal Zone in Kalubhar Island, Gulf of Kachchh, Gujarat and Its Geomorphological Implications

Tidal networks, tidal flats and mangroves are some of the important features of tidal areas in the Gulf of Kachchh. Insight on the patterns of tidal networks, vegetation and sedimentation is useful in understanding geomorphological processes active on the region. In the present study features of tidal zone of Kalubhar island, located in the south-central part of the Gulf have been studied with emphasis on tidal networks using Sentinel-2 Multispectral Imager (MSI) data having spatial resolution of 10m. Typology of tidal network is described for the first time. The typology of tidal network pattern, their density, presence of mangroves and beaches indicate low energy depositional setting in eastern, south-eastern and southern parts. This coupled with continued sedimentation and long sub-aerial exposure during low tides may not be ideal environmentin future for live corals present in the intertidal zones. Keywords: Tidal Network Pattern, Sentinel-2 MSI, Kalubhar Island, Gulf of Kachchh

Suspended Particulate Matter Analysis of Pre and During Covid Lockdown Using Google Earth Engine Cloud Computing: A Case Study of Ukai Reservoir.

Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.

Monitoring vegetation phenology using MODIS NDVI 250m in the City of Tshwane, South Africa

The unprecedented influx of people into urban areas has led to the horizontal and vertical growth of urban environments. One of the notable impacts of urbanisation is the encroachment of urban-like environments into non-urban areas. This is common in both developed and developing countries, and South Africa’s City of Tshwane, the administrative capital of the country, has been affected by urbanisation because of migration. One of the parameters or proxies used to quantify urban growth is vegetation cover. There is a consensus that with the increase in the population of urban dwellers, vegetation cover will decrease. To assess and monitor vegetation cover, the Normalised Difference Vegetation Index (NDVI) is commonly used. In this study, MODIS NDVI data with a 250m spatial resolution was used to assess the impact of urban growth on vegetation. A time series analysis of the MODIS NDVI with a spatial resolution of 250m was used to establish the patterns of vegetation cover. Trends in vegetation change were determined in newly developed residential areas, informal settlements, and various vegetated areas. Sen's slope estimator and Mann-Kendall’s statisticwere used to analyse the spatial trends and variations in trends among different land cover classes. The slope of the trends differs significantly but there is a general decline in vegetation cover. The temporal profiles revealed the high and low NDVI values, respectively showing greening (high vegetation) and browning (low vegetation) trends from 2000 to 2016. It is concluded that urban growth has an impact on vegetation cover. However, this does not disturb the seasonal changes in vegetation where high NDVI values prevail in summer and low values in winter.

Extraction of drainage Characteristics and Morphometric analysis using GIS and SRTM Digital Elevation Model Data in Sathyavedu area, Chittoor District, Andhra Pradesh, India

The aim of the present study is to extraction of the drainage characteristics from SRTM 30M resolution data processed in Arc GIS 10.1and develops the different maps such as Drainage fill, flow accumulation, stream link, flow direction, stream order map. The study area stream order map shows 5 orders. Most of the stream area flow in the direction of North West to south west and it determine the less porosity and permeability due to presence of the granite schist, quartzite's and laterite group of rocks and SRTM DEM with spatial resolution of 30m.

Mapping Apple Orchards in the municipality of São Joaquim (Santa Catarina, Brazil) using Sentinel-2 data

Abstract This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow.

Near-daily monitoring of surface temperature and channel width of the six largest Arctic rivers from space using GCOM-C/SGLI

Spatio-temporal changes in water temperature and discharge of the Arctic rivers are important variables to be observed not only for estimating runoff contributions from snow melt, rainfall, and thawing of permafrost over the continents but also for assessing their impacts on the Arctic sea-ice and marine ecosystem. Nevertheless, the number of ground water gauging stations and the frequency of in-situ measurements for temperature and discharge has been decreasing since the 20 Century due to the shrinkage of budgets for maintaining the in-situ stations. In this study, we explored the possibility to perform near-daily monitoring of river surface temperature (RST) and river channel width (RCW) from space using a Japanese satellite-borne optical sensor named SGLI which can observe radiances at the spectral bands from near-ultraviolet to thermal infrared regions at the same spatial resolution of 250-m on a global scale. River surface brightness temperature (RSBT) measured with SGLI TIR band was used as RST without atmospheric correction. RCW was derived as the widths of water pixels identified along river channels using SGLI reflectances at visible to shortwave infrared bands. Analysis results of two-year SGLI data acquired in 2018 and 2019 show that RSBT and RCW can be retrieved successfully from the SGLI observations on a daily basis after applying a spatio-temporal interpolation. The accuracies of the retrieved RSBT evaluated with in-situ water temperatures are about 2.57 K with the interpolation (and 1.84 K without the interpolation but with lower observation frequencies). Retrieved RCWs are correlated well with in-situ river water discharges indicating potentials to assess variations of not only snow melt water but also precipitation within the river basin. Thus, SGLI data can be used to reconstruct not only the river water temperature but also the river water discharges along the continental river channels for assessing heat flux flowing into the Arctic Ocean.

Modelling climate change impacts on regional net primary productivity in Turkey.

This study projects and models the terrestrial net primary productivity (NPP) considering the representative concentration pathways (RCPs) scenarios of Turkey using remote-sensing-based biogeochemical modelling techniques. Changes in annual NPP between 2000-2010 and 2070-2080 were projected with the biogeochemical ecosystem model NASA-Carnegie Ames Stanford Approach (CASA). A multi-temporal data set, including 16-day MODIS composites with a spatial resolution of 250m, was used within the CASA model. The 5th Assessment Report (AR5) of the IPCC presented several scenarios for RCPs named RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 that laid the foundation for the future climate projections. The futuristic NPP modelling was based on the assumptions of maintaining CO2 level in the range of 421 to 936ppm and a rise in temperature from 1.1 to 2.6°C. The NPP in Turkey averaged 1232g C m2year-1 as per the model results. Considering 2000-2010 as the baseline period, the NPP was modelled within the range of 9.6 and 316g C m2year-1. Modelled average NPP was 1332.4g C m2year-1 per year between 2061 and 2080. The forest productivity was also estimated to be increased up to 113g C m-2year-1 under the climate change scenarios. However, there were minor differences in the projected average NPP under the baseline period covering years from 2000 to 2080 from those under RCPs. It appeared that variation in temperature and precipitation as a result of climate change affected the terrestrial NPP. The regional environmental and socio-economic consequences of climate change on diverse landscapes such as Turkey were properly modelled and analysed to understand the spatial variation of climate change impacts on vegetation. Changes in NPP imply that forests in Turkey could be carbon sinks in the future as their current potential that would profile Turkey's climate mitigation. This is one of the pioneering studies to estimate the future changes of regional NPP in Turkey by integrating various spatial inputs and a biogeochemical model.

A diachronic study of Ourika watershed land in the High Atlas of Morocco

The Ourika watershed, located in the North-West of Moroccan High Atlas, has undergone several spatio-temporal changes and accelerated land use dynamics as a result of the interaction of climatic, topographic and anthropogenic factors. The objective of this study is to monitor the evolution of land use in the study area over the past 33 years. Landsat satellite imagery has been chosen for land cover mapping, providing a sufficient detail to identify land cover characteristics while providing more or less complete coverage of the area of action. Landsat 5 Thematic Mapper satellite images from 1987 and Landsat 8 Operational Land Imager from 2019 were used, with a spatial resolution of 30m. The images were treated and classified using Support Vector Machine algorithm (SVM) implemented on QGIS Geographic Information System software. The classification evaluation shows a Kappa coefficient of 85% and 84% and an overall accuracy of 95% and 94% for 1987 and 2019 respectively. Furthermore, the results showed a 10% decrease in the forest as well as a significant increase in the pasture, arboriculture, bare land and buildings with a respective percentage of 5.99%, 1.67%, 1.48% and 1.37% accordingly.

A distributed optical fiber sensor system for intrusion detection and location based on the phase-sensitive OTDR with remote pump EDFA

In this paper, a distributed optical fiber senor system for multi-intrusion monitoring has been proposed along a 25 km distance based on phase-sensitive optical time domain reflectometer (Δt=100 ns) and a remote optically pump amplifier (ROPA). The ROPA is based on Raman pump relay free amplification and remote connected in erbium-doped fiber (EDF). It makes the detection distance increase 108 %, from 12km to 25km. The best amplification effect is achieved when the EDF is connected in the middle area. In the experiments, several vibrations of the fiber at different locations given by piezoelectric transducers (PZTs) simulate the vibrations caused by intrusions. Three remote intrusions have been detected and located simultaneously along a 25km optical fiber with a spatial resolution of 10m. In order to improve the signal-to-noise ratio (SNR) and detection distance, the semiconductor optical amplifier (SOA) with high extinction ratio and narrow pulse width has been used to modulate light into pulses. The SNR of 9.5dB has realized for intrusion detection at remote tail end of 25km distance. The experimental results can be the references for improving the remote intrusion detection and location technology of Φ−OTDR sensor system.

Soil Erosion Estimation and Watershed Prioritization in Kulfo Watershed, Ethiopia

Soil loss by runoff is a severe and continuous environmental and infrastructural problem in Kulfo watershed. Deforestation and improper cultivation have resulted in accelerated soil erosion. Information on soil loss is essential to support agricultural productivity and watershed management. Thus, study was aimed to estimate and map the soil loss by using GIS and RUSLE techniques raster format in GIS environment. Topographic map of 1:250,000 scale. Aster Digital Elevation Model (DEM) of 30 m spatial resolution. Digital soil map of 1:250.000 scales, fifteen years rainfall records of six stations nearby watershed and land sat imagery (ETM+) with spatial resolution of 30m were used to derive RUSLE's soil loss parameter. The RUSLE parameters were analyzed and superimposed using raster calculator in the geo-processing tools in ArcGIS10.2 environment to estimate and map the annual soil loss of the study area. The result showed that the mean annual soil loss of the watershed range from none significant (0 tons ha -1 y -1 ) to highly sever (253.38 ton ha -1 y -1 ). The mean annual soil loss of the whole watershed was 88.57 tons ha -1 y -1 . The total annual soil loss in the watershed was 3,862,077 ton per year from 39900 ha of land. The soil erosion affected is spatially situated in the upper steepest slope part and middle part of the watershed. This area where acrisols with high soil Erodibility character are dominant Hence, Slope gradient and length followed by soil Erodibility factors were found to be the main factors of soil erosion .The morphological character also the determinant factor for soil and water conservation practice. Thus, sustainable soil and water conservation practices should be adopted in the most sever area of the watershed. Keyword s : Geographical Information System, Erosion, RUSLE, watershed prioritization DOI : 10.7176/JNSR/10-1-01 Publication date: January 31 st 2020

Cartografia do Invisível: Revelando a Agricultura de Pequena Escala com Imagens Rapideye na Região do Baixo Tocantins, Pa.

Dados derivados de sensoriamento remoto aliados ao uso de técnicas de classificação digital de imagens fornecem uma visão sinóptica e informações sobre a dimensão temporal dos fenômenos espaciais, possibilitando gerar informações sobre a dinâmica e os padrões espaciais da paisagem em áreas de grandes extensões territoriais como a Amazônia. Nos mapeamentos extensivos, como os realizados com imagens de satélite pelo INPE para a Amazônia Legal, é comum a definição e descrição de classes de uso e cobertura da terra em função da resolução espacial e radiométrica dos sensores utilizados. Por essa razão, pequenas áreas que não atingem o tamanho mínimo da área de mapeamento e/ou com usos diversificados são, em geral, agregadas em uma única classe. Esse é o exemplo da classe denominada Mosaico de Ocupação presente na legenda do sistema de monitoramento do uso e cobertura da terra da Amazônia-TerraClass. Esta classe procura representar, em parte, a agricultura familiar, no entanto, como o mapeamento é feito pelo sensor TM ou OLI dos satélites da série Landsat, com resolução espacial de 30m e como o TerraClass define uma área mínima de mapeamento de 6,25 hectares (ha), a identificação de pequenas áreas agrícolas é comprometida, pois essas categorias são agregadas em classes mistas e não são mais distinguíveis. Para estudos que procuram dar visibilidade aos padrões espaciais de atividades de produção de pequena escala torna-se necessário realizar o refinamento dessas classes com dados de resolução espacial com melhor definição. Neste contexto, o objetivo desse trabalho é testar três algoritmos semiautomáticos de classificação, baseados em pixel e em regiões: os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo (KNN), para avaliar a capacidade de refinamento da classe Mosaico de Ocupação do dado produzido pelo TerraClass-2014. A área de estudo compreende parte dos municípios de Cametá, Mocajuba e Baião, localizados na região Nordeste do Pará, onde a produção de mandioca, pimenta-do-reino, cacau e açaí têm grande importância econômica para população local. Para o mapeamento das categorias contidas na classe Mosaico de Ocupação, foram utilizadas imagens do RapidEye, sensor REIS, ortoimagens com 5m de resolução espacial. Foi estimada a exatidão global dos algoritmos testados obtendo-se índices de 26%, 38% e 78%, para os algoritmos MAXVER, Bhattacharya e K-Vizinho Mais Próximo, respectivamente. Além da maior exatidão Global (78%), o algoritmo K-Vizinho Mais Próximo apresentou melhores resultados relativo às classes de vegetação secundária, hidrografia, e pasto sujo, com mais de 90% de acerto. A classe agricultura anual de pequena escala apresentou acerto de 62%, enquanto o índice de acerto dos outros dois algoritmos testados não passou de 8%. A abordagem metodológica desenvolvida demonstrou a viabilidade do uso das imagens de alta resolução espacial e de métodos semiautomáticos para a classificação de classes de uso e cobertura da terra associadas à classe Mosaico de Ocupação do TerraClass. A metodologia pode ser utilizada para complementar às atuais bases de dados existentes para a Amazônia (TerraClass, MapBiomas e IBGE), explicitando as categorias agrícolas de produção em pequena escala, dando visibilidade a esses sistemas de produção, frequentemente negligenciados nos mapeamentos que abrangem grandes extensões territoriais.

Assessment of Agricultural Vulnerability to Flood in Ngaski, Kebbi State, Nigeria

The assessment of agricultural vulnerability to flood in Ngaski Local Government Area of Kebbi State, Nigeria was carried out. The study made use of ASTER data of 2017 with spatial resolution of 30m, topographical map at a scale of 1:50,000, monthly rainfall data for 35 covering the study area and soil map at a scale of 1:50,000. Thematic maps for soil, rainfall and elevation were produced converted to raster data in GIS environment. Each data set in a single map was given weight by pair-wise comparison; reclassification of each map was done based on the weights generated from the pair-wise comparison of each dataset. The results showed that the rainfall recorded in the study area ranges from 950mm to 1150mm and this is categorized between high and very high under the pair-wise comparison rating. The elevation is such that parts of the study area had high elevation that ranges between 226m and 255m and low elevation that ranged between 125m and 167m giving room to flooding. The soil types in the study area are such that encourage flooding coupled with high amount of rainfall on one hand and the high and low elevations experience across the study area. For the farmers to sustain agricultural activities as a result of flooding, they practiced mixed farming, shifting cultivation, terrace farming, fallow and arable farming. The above farming systems were practiced by the farmers to alternate, avoid or take advantage of the floods. In conclusion, the study recommends the use of more resistant seedlings and crops to flooding, channelization of the river should be carried out by the government to reduce the level of flooding across the study area among others.