Acreage Estimation Research Articles

An optimized two-stage spatial sampling scheme for winter wheat acreage estimation using remotely sensed imagery

ABSTRACTTimely and reliable information on crop acreage is essential for formulating grain production policies and ensuring national food security. The combination of available satellite-based remotely sensed images and traditional sampling methods offers the possibility of improved crop acreage estimation at a regional scale. Due to the administrative convenience, reduced survey cost and workload, two-stage sampling has widely been used for crop acreage survey at the large-scale regions. However, compared with single-stage sampling, the two-stage sampling can introduce larger estimation errors, since it has multiple sampling stages. This study’s aim is to optimize the two-stage sampling scheme using satellite-based remotely sensed imagery to improve the accuracy of crop acreage estimation. Taking Mengcheng County, Anhui Province, China, as the study area, this study explored the influence of stratum boundary and sample selection method on the sampling efficiency at the first sampling stage, analysed the impact of sample size on population extrapolation accuracy and then optimized the sample size of the second sampling stage using crop thematic map retrieved by ALOS (Advanced Land Observing Satellite) AVNIR (Advanced Visible light and Near Infrared Radiometer)-2 images in 2009. The results showed that the relative error (RE), coefficient of variation (CV), standard error (SE) of population extrapolation, and sampling fraction (f) using the cumulative square root of frequency (CSRF) method is the minimum among three methods for the stratum boundary determination at the first sampling stage, followed by the equal interval (EI) and equal sample size (ESS) method. Moreover, the RE, CV, and SE of population extrapolation using the ST sampling method is the minimum, compared with simple random (SI) and systematic (SY) sampling method. Therefore, the sampling scheme of the first stage can be optimized by CSRF method for stratum boundary determination and stratified sampling (ST) sampling method for samples selection. At the second sampling stage, RE and CV values of population extrapolation decrease as the sample size increases. Comprehensively considering the accuracy, stability of population extrapolation and sampling cost, the most cost-effective sample size for estimating the winter wheat acreage of the study area is 4. From the perspective of the reasonable selection of sample selection methods, sample size and determination of stratum boundaries, this study provides an important basis for formulating a cost-effective two-stage spatial sampling scheme for crop acreage estimation.

Model-Based County Level Crop Estimates Incorporating Auxiliary Sources of Information

SummaryIn 2011, the US Department of Agriculture's National Agricultural Statistics Service started the complete implementation of the County Agricultural Production Survey (CAPS). The CAPS is an annual survey to provide accurate county level acreage and production estimates of approved federal and state crop commodities. The current top down method of producing official county level estimates that satisfy the county–district–state benchmarking constraint is an expert assessment incorporating multiple sources of information. We propose a model-based method that combines the CAPS acreage data with auxiliary data and improves county level survey estimation, while providing measures of uncertainty for the county level acreage estimates. Auxiliary sources of information include remote sensing data, weather data and planted acreage administrative data from other US agencies. A hierarchical Bayesian subarea level model is proposed and implemented, with an additional hierarchical level for the sampling variances. County level, model-based acreage estimates have lower coefficients of variation than the corresponding county level survey acreage estimates. Top down benchmarking methods are investigated and the final acreage estimates satisfy the county–district–state benchmarking constraint.

Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model

Combining information from different sources is an important practical problem in survey sampling. Using a hierarchical area-level model, we establish a framework to integrate auxiliary information to improve state-level area estimates. The best predictors are obtained by the conditional expectations of latent variables given observations, and an estimate of the mean squared prediction error is discussed. Sponsored by the National Agricultural Statistics Service of the US Department of Agriculture, the proposed model is applied to the planted crop acreage estimation problem by combining information from three sources, including the June Area Survey obtained by a probability-based sampling of lands, administrative data about the planted acreage and the cropland data layer, which is a commodity-specific classification product derived from remote sensing data. The proposed model combines the available information at a sub-state level called the agricultural statistics district and aggregates to improve state-level estimates of planted acreages for different crops. Supplementary materials accompanying this paper appear on-line.

Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km2, which was consistent with the statistics from the National Bureau of Statistics (22,948 km2). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.

Acreage estimation of mango orchards using hyperspectral satellite data

Horticultural crop plays a unique role in India's economy, therefore reliable and timely estimates of area under horticulture crops are of vital importance. Present methods of crop acreage estimation rely heavily on sample survey approach which is time consuming for a diversified and large country like India. Modern space technology with advance tools of Remote Sensing, GIS and GPS may be an alternative option for estimating areaunder horticultural crops. The advantage of using satellite data is that it provides both synoptic view and the economies of scale, since data over large areas could be gathered quickly from such platforms. This study has been undertaken to estimate the acreage under mango and to map existing orchards of Mango using hyperspectralsatellite data. The study was conducted for Meerut district of Uttar Pradesh. The hyperion hyperspectral satellitedata was evaluated to estimate the area under all mango orchards. These estimates were compared with actual area under mango orchards measured using Global Positioning System (GPS) and the total area under mangowas predicted as 961.88 ha which was 92% close to ground data 889.65 ha. The results indicated the scope of hyperspectral remote sensing in acreage estimation of fruit crops.

Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series

In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R2 = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.

A quantitative performance comparison of paddy rice acreage estimation using stratified sampling strategies with different stratification indicators

ABSTRACTVarious indicators derived from thematic maps have been widely used to determine the strata needed to perform stratified sampling. However, these indicators typically do not quantify the spatial errors in the crop thematic maps that are needed to reduce the uncertainty. To address this lack of error information, this paper introduces a hybrid entropy indicator (HEI). Two conventional indicators, the acreage indicator (AI) and the fragmentation indicator (FI), were also evaluated to compare the results of the three indicators in a homogeneous agricultural area (Pinghu, PH) and a heterogeneous agricultural area (Zhuji, ZJ). The results show that HEI performs the best in heterogeneous areas with the lowest coefficient of variation (CV) (as low as 1.59%) and also has the highest estimation accuracy with the lowest standard deviation of estimation. For both areas, the performances of HEI and AI are very similar, and better than FI. These results highlight that the HEI should be considered as an effective indicator and used in place of AI and FI to help improve sampling efficiency of crop acreage estimation, while FI is not recommended. Furthermore, the positive performance achieved using HEI indicates the potential for incorporating thematic map uncertainty information to improve sampling efficiency.

Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation

Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation

A BIRD'S-EYE VIEW OF PROTO-TEWA SUBSISTENCE AGRICULTURE: MAKING THE CASE FOR FLOODPLAIN FARMING IN THE OHKAY OWINGEH HOMELAND, NEW MEXICO

Previous research on agriculture in the American Southwest focuses overwhelmingly on archaeological survey methods to discern surface agricultural features, which, in combination with climatological, geological, and geographical variables, are used to create models about agricultural productivity in the past. However, with few exceptions, the role of floodplain irrigation and floodwater farming in ancestral Pueblo agriculture is generally downplayed in scholarly discourse. Using a variety of methods, including Unmanned Aerial Vehicles (UAV), satellite imagery, pedestrian survey, and supervised classification of remotely sensed imagery, we examine this issue through a consideration of how ancestral Ohkay Owingeh (Tewa) people solved the challenges of arid land farming in the lower Rio Chama watershed of New Mexico during the Classic period (A.D. 1350–1598). Based on acreage estimates, our results indicate that runoff and rainwater fields in terrace environments would have been insufficient to supply the nutritional needs of an ancestral Tewa population exceeding 10,000 individuals. Based on these observations, we present a case for the substantial role of subsistence agriculture in the floodplain of the Rio Chama and its associated tributaries.

Crop classification and acreage estimation in North Korea using phenology features

In North Korea, reliable and timely information on crop acreage and spatial distribution is hard to obtain. In this study, we developed a fast and robust method to estimate crop acreage in North Korea using time-series normalized difference vegetation index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. We proposed a method to identify crop type based on NDVI phenology features using data collected in other areas with similar agri-environmental conditions to mitigate the shortage of ground truth data. Eventually the classification map (MODIScrop) was assessed using the Food and Agriculture Organization (FAO) statistical data and high-resolution crop classification maps derived from one Landsat scene (LScrop). The Pareto boundary method was used to assess the accuracy and crop distribution of the MODIScrop maps. Results showed that acreage derived from the MODIScrop maps was generally consistent with that reported in the FAO data (a relative error <4.1% for rice and <6.1% for maize, and <9.0% for soybean except for in 2004, 2008, and 2009) and the maps derived from the LScrop (a relative error about 5% in 2013, and 7% in 2008 and 2014). The classification accuracy reached 74.4%, 69.8%, and 73.1% of the areas covered by the Landsat images in 2008, 2013, and 2014, respectively. This indicates that features derived from NDVI profiles were able to characterize major crops, and the approaches developed in this study are feasible for crop mapping and acreage estimation in regions with limited ground truth data.

Estimation of mango growing areas using remote sensing

An attempt was made to estimate acreage and to depict the spatial distribution of mango growing areas using linear imaging self-scanning image of remote sensing data for Saharanpur district of Uttar Pradesh. The image was processed using ENVI software. Unsupervised, supervised and decision tree classification were used for the acreage estimation. The study has clearly demonstrated the usefulness of remote sensing image for identifying and acreage estimation of mango orchards. The results also indicated that mango acreage estimation was more accurate in decision tree approach (92.92%) as compared to other two methods (81.73 and 80.43%, respectively). The overall result revealed that the finer resolution the time series data, better the accuracy of area estimation. The above study proves that the remote sensing data could be effectively used for other perennial horticultural crops with finer resolution time series data.

Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach

Image classification is one of the crucial techniques in detecting the crops from remotely sensed data. Crop identification and discrimination provide an important basis for many agricultural applications with various purposes, such as cropping pattern analysis, acreage estimation, and yield estimation. Accurate and faster estimation of crop area is very essential for projecting yearly agriculture production for deciding agriculture policies. Remote sensing is a technique that allows mapping of large areas in a fast and economical way. In many applications of remote sensing, a user is often interested in identifying the specific crop only while other classes may be of no interest. Indian Remote Sensing Satellite (IRS-P6) LISS IV sensor image of spatial resolution 5.8 m has been used to identify the sugarcane crop for the Chhapar village of Muzaffarnagar District, India. Classification of satellite data is one of the primary steps for information extraction for crop land identification. In recent years, decision tree approach to image analysis has been developed for the assessment and improvement of traditional statistically based image classification. In this study, ISODATA, MLC, and vegetation indices based decision tree approaches are used for classifying LISS IV imagery. The 11 vegetation index images have been generated for decision tree classification. All the three methods are compared and it is found that the best performance is given by the decision tree method. Vegetation indices based decision tree method for sugarcane classification, the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient were found 88.17, 86.59, and 87.93% and 0.86 respectively.

Emerging Use of Spatial Technologies in Natural Resource Management Services

Spatial technology is a vital technology and opens up new opportunities for sustainable use of natural resources, principally land, water, bio-diversity and helps in increasing productivity in perpetuity without ecological harm. Now a day's spatial technology became an important tool for sustainable agriculture that includes environmentally sustainable, socially sustainable and economically viable. Satellite based imagery are used widely in monitoring agriculture, radar imagery are used during monsoon season. Integrated use of geospatial tools with crop models and ground truthing enables timely crop production forecasts and drought assessment and disaster management. Analysis of spatial, temporal and spectral characteristics of concern objects provide valuable information about the biomass and yield estimation, irrigation infrastructure monitoring & performance assessment, reservoir capacity &loss assessment, vegetation vigor, drought stress monitoring, assessment of crop phenological development, crop acreage estimation, cropland mapping, land-use/land-cover (present and past), and also addressing five natural disasters viz., forest fire, flood, earthquake, cyclone, and landslide. Geographical Information System with decision support tools enable combined analysis of different datasets, their visualization, monitoring and management in a timely and most cost-effective manner.

Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

Abstract. Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

An Approach to Analyse the Agriculture Acreage and Estimate Production

Background/Objective: Crop acreage and production estimate is of great challenge and requires strategic decisions to be taken by government agencies regarding controlling import and export duties and also to control inflation as a whole. Methods/Statistical Analysis: A lot of techniques exist to measures the yield and production for a given area and that too crop wise. We estimate the crop acreage with the help of remote sensing data and geographic information system techniques. In remote sensing, we acquire information related to the earth surface and to determine the crop acreage with the help of remote sensing images. We extract the useful information related to the yield estimation. Findings: In image processing, it is used to analyze or manipulate the features of an image. We used K-mean clustering and Self Organizing Map algorithms here for the crop Acreage and production estimation as a comparative approach to measure out acreage for a specific area image. By applying different image processing techniques and using the remote sensing techniques we estimated the production estimates for crops and compared the results with actual data obtained from statistical sources. Geographic Information System is widely used in crop acreage and production estimation to find out the locations of a particular image and helps in improving the accuracy of estimates. The challenge still remained regarding the crop segregation which requires lot of ground truth validation and hence makes the approach less viable as it reduces accuracy. Self organizing maps were found to be giving better results than other approaches. Applications/Improvement: It can be used by government agencies for taking decisions regarding import/export duty control and by agencies interested in procurement of crops and can be used by RBI to tackle Inflation as well.

COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION: AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS

Abstract. Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.

COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION: AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS

Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.

Modeling of groundwater draft based on satellite-derived crop acreage estimation over an arid region of northwest India

Over-exploitation of groundwater for agricultural crops puts stress on the sustainability of natural resources in the arid region of Rajasthan state, India. Hydrogeological study of groundwater levels of the study area during the pre-monsoon (May to June), post-monsoon (October to November) and post-irrigation (February to March) seasons of 2004–2005 to 2011–2012 shows a steady decline of groundwater levels at the rate of 1.28–1.68 m/year, mainly due to excessive groundwater draft for irrigation. Due to the low density of the groundwater observation-well network in the study area, assessment of groundwater draft, and thus groundwater resource management, becomes a difficult task. To overcome the situation, a linear groundwater draft model (LGDM) has been developed based on the empirical relationship between satellite-derived crop acreage and the observed groundwater draft for the year 2003–2004. The model has been validated for a decade, during three year-long intervals (2005–2006, 2008–2009 and 2011–2012) using groundwater draft, estimated through a discharge factor method. Further, the estimated draft was validated through observed pumping data from random sampled villages (2011–2012). The results suggest that the developed LGDM model provides a good alternative to the estimation of groundwater draft based on satellite-based crop area in the absence of groundwater observation wells in arid regions of northwest India.

RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

Abstract. Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R2 &gt; 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.

RICE CROP MAPPING USING SENTINEL-1A PHENOLOGICAL METRICS

Rice is the most important food crop in Vietnam, providing food more than 90 million people and is considered as an essential source of income for majority of rural populations. Monitoring rice-growing areas is thus important to developing successful strategies for food security in the country. This paper aims to develop an approach for crop acreage estimation from multi-temporal Sentinel-1A data. We processed the data for two main cropping seasons (e.g., winter–spring, summer–autumn) in the Mekong River Delta (MRD), Vietnam through three main steps: (1) data pre-processing, (3) rice classification based on crop phenological metrics, and (4) accuracy assessment of the mapping results. The classification results compared with the ground reference data indicated the overall accuracy of 86.2% and Kappa coefficient of 0.72. These results were reaffirmed by close correlation between the government’s rice area statistics for such crops (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;gt; 0.95). The values of relative error in area obtained for the winter–spring and summer–autumn were -3.6% and 6.7%, respectively. This study demonstrates the potential application of multi-temporal Sentinel-1A data for rice crop mapping using information of crop phenology in the study region.