Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

In this paper we assess the snow cover and its dynamics for the western river basins of the Indus River system (IRS) and their sub-basins located in Afghanistan, China, India and Pakistan for the period 2001–2012. First, we validate the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products from Terra (MOD10A1) and Aqua (MYD10A1) against the Landsat Thematic Mapper/Enhanced Thematic Mapper plus (TM/ETM+) data set, and then improve them for clouds by applying a validated non-spectral cloud removal technique. The improved snow product has been analysed on a seasonal and annual basis against different topographic parameters (aspect, elevation and slope). Our results show a decreasing tendency for the annual average snow cover for the westerlies-influenced basins (upper Indus basin (UIB), Astore, Hunza, Shigar and Shyok) and an increasing tendency for the monsoon-influenced basins (Jhelum, Kabul, Swat and Gilgit). Seasonal average snow cover decreases during winter and autumn, and increases during spring and summer, which is consistent with the observed cooling and warming trends during the respective seasons. Sub-basins at relatively higher latitudes/altitudes show higher variability than basins at lower latitudes/middle altitudes. Northeastern and northwestern aspects feature greater snow cover. The mean end-of-summer regional snow line altitude (SLA) zones range from 3000 to 5000 m a.s.l. for all basins. Our analysis provides an indication of a descending end-of-summer regional SLA zone for most of the studied basins, which is significant for the Shyok and Kabul basins, thus indicating a change in their water resources. Such results are consistent with the observed hydro-climatic data, recently collected local perceptions and glacier mass balances for the investigated period within the UIB. Moreover, our analysis shows a significant correlation between winter season snow cover and the North Atlantic Oscillation (NAO) index of the previous autumn. Similarly, the inter-annual variability of spring season snow cover and spring season precipitation explains well the inter-annual variability of the summer season discharge from most of the basins. These findings indicate some potential for the seasonal stream flow forecast in the region, suggesting snow cover as a possible predictor.

Background: Ticks are ectoparasites that transmit a variety of pathogens that cause many diseases in livestock which can result in skin damage, weight loss, anemia, reduced production of meat and milk, and mortality. Aim: The aim of this study was to identify tick species and the distribution on livestock hosts (sheep, goat, dairy cattle, and buffalo) of Punjab, Khyber Pakhtunkhwa Province and Islamabad from October 2019 to November 2020. Materials and Methods: Surveillance was performed to calculate the prevalence of ticks on livestock. Tick prevalence data (area, host, breed, gender, age, and seasonal infestation rate) was recorded and analyzed. Results: A total of 2080 animals were examined from selected farms, and, of these, 1129 animals were tick-infested. A total of 1010 male tick samples were identified to species using published keys. Haemaphysalis punctata, Haemaphysalis sulcata, Hyalomma anatolicum, Hyalomma detritum, Hyalomma dromedarii, Hyalomma excavatum, Hyalomma marginatum, Hyalomma rufipes, Rhipicephalus decoloratus Rhipicephalus microplus, and Rhipicephalus sanguineus were collected from goats, sheep, buffalo, and cattle. The overall rates of tick infestation on livestock were 34.83% (buffalo), 57.11% (cattle), 51.97% (sheep) and 46.94% (goats). Within each species, different breeds demonstrated different proportions of infestation. For cattle breeds, infestation proportions were as follows: Dhanni (98.73%), Jersey (70.84%) and the Australian breed of cattle (81.81%). The Neeli Ravi breed (40%) of buffalo and the Beetal breed (57.35%) of goats were the most highly infested for these species. Seasonally, the highest prevalence of infestation (76.78%) was observed in summer followed by 70.76% in spring, 45.29% in autumn, and 20% in winter. The prevalence of tick infestation in animals also varied by animal age. In goats, animals aged 4–6 years showed the highest prevalence (90%), but in cattle, the prevalence of ticks was highest (68.75%) in 6 months–1-year-old animals. 1–3 years old buffalo (41.07%) and 6 months–1 year sheep (65.78%) had the highest prevalence rate. Females had significantly higher infestation rates (61.12%, 55.56% and 49.26%, respectively) in cattle, sheep, and goats. In buffalo, males showed a higher prevalence (38.46%) rate. Conclusions: This study showed tick diversity, infestation rate, and numerous factors (season, age, and gender of host) influencing tick infestation rate in different breeds of cattle, sheep, goats, and buffalo in Punjab Province, Khyber Pakhtunkhwa Province, and Islamabad, Pakistan. Higher tick burdens and rates of tick-borne disease reduce production and productivity in animals. Understanding tick species’ prevalence and distribution will help to develop informed control measures.

Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure.

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1km² resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.