scholarly journals Remote Sensing for Biocultural Heritage Preservation in an African Semi-Arid Region: A Case Study of Indigenous Wells in Northern Kenya and Southern Ethiopia

2022 ◽  
Vol 14 (2) ◽  
pp. 314
Author(s):  
Pamela Ochungo ◽  
Nadia Khalaf ◽  
Stefania Merlo ◽  
Alemseged Beldados ◽  
Freda Nkirote M’Mbogori ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Land Cover ◽  
Diversity Index ◽  
Management Systems ◽  
Southern Ethiopia ◽  
Northern Kenya ◽  
Landscape Degradation ◽  
Biocultural Heritage ◽  
Semi Arid

The region of Southern Ethiopia (Borana) and Northern Kenya (Marsabit) is characterised by erratic rainfall, limited surface water, aridity, and frequent droughts. An important adaptive response to these conditions, of uncertain antiquity, has been the hand-excavation of a sequence of deep wells at key locations often along seasonal riverbeds and valley bottoms where subterranean aquifers can be tapped. Sophisticated indigenous water management systems have developed to ensure equitable access to these critical water resources, and these are part of well-defined customary institutional leadership structures that govern the community giving rise to a distinctive form of biocultural heritage. These systems, and the wells themselves, are increasingly under threat, however, from climate change, demographic growth, and socio-economic development. To contribute to an assessment of the scale, distribution and intensity of these threats, this study aimed to evaluate the land-use land-cover (LULC) and precipitation changes in this semi-arid to arid landscape and their association with, and impact on, the preservation of traditional wells. Multitemporal Landsat 5, 7 and 8 satellite imagery covering the period 1990 to 2020, analysed at a temporal resolution of 10 years, was classified using supervised classification via the Random Forest machine learning method to extract the following classes: bare land, grassland, shrub land, open forest, closed forest, croplands, settlement and waterbodies. Change detection was then applied to identify and quantify changes through time and landscape degradation indices were generated using the Shannon Diversity Index fragmentation index within a 15 km buffer of each well cluster. The results indicated that land cover change was mostly driven by increasing anthropogenic changes with resultant reduction in natural land cover classes. Furthermore, increased fragmentation has occurred within most of the selected buffer distances of the well clusters. The main drivers of change that have directly or indirectly impacted land degradation and the preservation of indigenous water management systems were identified through an analysis of land cover changes in the last 30 years, supporting insights from previous focused group discussions with communities in Kenya and Ethiopia. Our approach showed that remote sensing methods can be used for the spatially explicit mapping of landscape structure around the wells, and ultimately towards assessment of the preservation status of the indigenous wells.

A Study of Change in Agricultural and Forest Land in Gwalior City Using Satellite Imagery

2017 ◽  
Vol 9 (02) ◽  
pp. 109-112
Author(s):  
Padam Jee Omar ◽  
Nitesh Gupta ◽  
Ravi Prakash Tripathi ◽  
Shiwanshu Shekhar ◽  
Surender .
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Arid Region ◽  
Local Population ◽  
High Rate ◽  
Semiarid Region ◽  
Change Analysis ◽  
Semi Arid Region ◽  
Semi Arid

The relative evaluation of land use and land cover for various uses such as forest, agriculture and water bodies etc. is the important issue in the semiarid region. Application of Remote Sensing technology for Land Use and Land Cover (LULC) change analysis has been carried out in semi-arid region of Madhya Pradesh, central part of India and found that the use of remote sensing along with Survey of India toposheets could be used appropriately for LULC mapping. The semi-arid regions are characterized by erratic rainfall and high rate of vegetation dynamics. The increasing biotic pressure together with increasing human demands exerts pressure on the available land resources all over the region. Therefore, in order to have best possible use of land, it is not only necessary to have the information on the existing LULC, but also to monitor the dynamic land use resulting because of increasing demands aroused from the growing population. Continuous overexploitation of natural resources like land, water, and forest has caused serious threat to the local population of the semi-arid region. This causes problems like little scope for soil moisture storage, high rate of soil erosion, declining groundwater level and shortage of drinking water

scholarly journals A Remote Sensing Approach for Mapping the Development of Ancient Water Management in the Near East

2018 ◽  
Vol 10 (12) ◽  
pp. 2042 ◽  
Author(s):  
Louise Rayne ◽  
Daniel Donoghue
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Satellite Imagery ◽  
Agricultural Landscape ◽  
Image Interpretation ◽  
Agricultural Practices ◽  
Management Systems ◽  
Novel Approach ◽  
Canal Systems ◽  
The 1960S

We present a novel approach that uses remote sensing to record and reconstruct traces of ancient water management throughout the whole region of Northern Mesopotamia, an area where modern agriculture and warfare has had a severe impact on the survival of archaeological remains and their visibility in modern satellite imagery. However, analysis and interpretation of declassified stereoscopic spy satellite data from the 1960s and early 1970s revealed traces of ancient water management systems. We processed satellite imagery to facilitate image interpretation and used photogrammetry to reconstruct hydraulic pathways. Our results represent the first comprehensive map of water management features across the entirety of Northern Mesopotamia for the period ca. 1200 BC to AD 1500. In particular, this shows that irrigation was widespread throughout the region in the Early Islamic period, including within the zone traditionally regarded as “rain-fed”. However, we found that a high proportion of the ancient canal systems had been damaged or destroyed by 20th century changes to agricultural practices and land use. Given this, there is an urgent need to record these rapidly vanishing water management systems that were an integral part of the ancient agricultural landscape and that underpinned powerful states.

Local sensitivity analysis of Satellite Monitoring of Irrigation software (SAMIR) over Semi-Arid climate of Morocco

2021 ◽  
Author(s):  
Aicha Moumni ◽  
Alhousseine Diarra ◽  
Abderrahman Lahrouni
Keyword(s):  
Remote Sensing ◽  
Sensitivity Analysis ◽  
Water Resources ◽  
Land Cover ◽  
Remote Sensing Data ◽  
Soil Parameters ◽  
Sensing Data ◽  
Annual Crops ◽  
Irrigated Area ◽  
Semi Arid

<p>Nowadays, the assessment of agricultural management is based mainly on the good management of water resources (i.e., to estimate the crops water consumption and provide their irrigation requirements). In this context, several agro-environmental models, (i.e., STICS, AQUACROP, TSEB, …) have been developed to assess the agricultural needs such as grain yield and/or irrigation demand prediction. These models are mainly based on the remote sensing data which contribute highly to the knowledge of some key-variables of crop models, in particular their time and space variations. The study area is the Haouz plain located in central Morocco. The climate of the plain is semi-arid continental type characterized by strong spatiotemporal irregular rains (mean annual precipitation up to 250 mm).The region relies mainly on the agricultural activities. Therefore, about 85% of available water is used for irrigated crops within the plain. The irrigated area is covered by 25% tree plantations and 75% annual crops. However, the annual crops extent depends strongly on the water availability during the season. Hence, for sustainable monitoring and optimal use of water resources (using physical modeling, satellite images and ground data), SAMIR software is developed in order to spatialize the irrigation water budget over Haouz plain. SAMIR (Simonneaux et al., 2009; Saadi et al., 2015; Tazekrit et al., 2018) is a tool for irrigation management based mainly on the use of remote sensing data. It estimates the crop evapotranspiration (ET) based on the FAO-56 model. This model requires three types of data: climatic variables for calculation of reference Evapotranspiration (ET0), land cover for computing crop coefficient Kc, and periodical phonological information for adjusting the Kc. SAMIR offers the possibility to calculate the ET of a large agricultural areas, with different land use/ land cover types, and subsequently deduce the necessary water irrigation for these areas. This model has been calibrated and validated over R3 perimeter (Diarra et al., 2017). In the present work, we studied the sensitivity (local sensibility analysis) of SAMIR software to the variations of each input parameter (i.e., ET0, precipitations, soil parameters, and irrigation configuration “real or automatic”). The simulations were made using the ground truth observations and irrigation dataset of the agricultural season of 2011/2012 over an irrigated area of Haouz plain. For the climatic variables, the obtained results showed that the effect of the ET0 is more significant compared to the effect of precipitations. It led to large shifts of the actual ET simulated by SAMIR compared to all tested parameters. For soil parameters, the sensitivity analysis illustrates that the effect is almost linear for all parameters. But the proportion of total available water, P, is the high sensitive parameter (Lenhart, et al., 2002). Finally, the comparison between the simulation of real evapotranspiration using automatic irrigation or real irrigation configuration offers an interesting result. The obtained ET values are similar for both configurations. Thus, this result offers the possibility of using only automatic irrigation configuration, in case of non-availability of the real irrigation.</p>

Delineating fragmented grassland patches in the tropical region using multi-seasonal SAR and Optical Satellite Images

2020 ◽  
Author(s):  
Abhishek Samrat ◽  
M. S. Devy ◽  
Ganesh
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
High Resolution ◽  
Land Cover ◽  
South Asia ◽  
Spatial Resolution ◽  
Post Monsoon ◽  
Habitat Matrix ◽  
Semi Arid ◽  
Sentinel 2

Globally grasslands are declining and are in highly degraded conditions. In south Asia grasslands are neglected and treated as wastelands. They remain unprotected, highly fragmented, and poorly understood which has led to a loss of unique biodiversity and livelihoods. Mapping grasslands accurately is a challenge and current maps based on optical remote sensing often over- or underestimate grasslands in south Asia due to a prevalant complex landscape matrix, small patch sizes, and obscuring monsoonal clouds. Synthetic Aperture Radar (SAR) fused with moderate spatial resolution has been used to delineate grasslands but, high-resolution, freely available ESA’s sentinel-1(SAR) and -2(optical) provides an opportunity to map small and fragmented patches that were not possible earlier with the publically available moderate or medium spatial resolution remote sensing dataset. Further, high resolution imageries require high computing power which is often limited with stand alone machines. Here we demonstrate that using cloud computing and optimal use of multi-seasonal imagery one can obtain a highly accurate land cover/use classification for a complex habitat matrix. We used freely accessible cloud computing platforms like Google Earth Engine (GEE) and land cover/use classification of sentinel-1 and -2. We compared the accuracy of grassland delineation between 1) seasonal (pre, during, and post-monsoon) sentinel-1, 2) post-monsoon sentinel-2, and 3) combined sentinel-1 and -2. We tested this method at two sites in a highly fragmented habitat matrix in semi-arid areas of Western and Southern India. The classification result has shown the overall accuracy of for the combined image was higher than only sentinel-2 and sentinel-1 alone for both sites. Grasslands habitat accuracy was also consistent with combined image classification across the sites. Our results identified newer grassland areas that coarse landuse management maps used by the government did not. The computation was done on a basic laptop and processing completed very quick. We, therefore, suggest that this novel approach of using cloud computing and optimal use of resource-hungry (computation and storage) high-resolution ESA’s sentinel-1 and -2 data, can be used to identify major land classes and small patchy grassland in the semi-arid regions of Asia and has the potential to map at continent level.

Identification of droughts from monitored and modelled vegetation condition for improved water management in semi-arid areas

2020 ◽  
Author(s):  
Anita Bayer ◽  
Christine Mihalyfi-Dean ◽  
Robert Behling ◽  
Christof Lorenz ◽  
Saskia Foerster ◽  
...  
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Water Availability ◽  
Standardized Precipitation Index ◽  
Integrated Analysis ◽  
Arid Areas ◽  
Large Variability ◽  
Vegetation Condition ◽  
Drought Indicators ◽  
Semi Arid

<p>Semi-arid areas suffer from small amounts and a large variability in rainfall combined with an increasing risk of droughts under climate change. These long and short-term changes in water availability directly affecting regional livelihoods are depicted in the condition of the rather sparse vegetation. In this study, seasonal and long-term trends in indicators of the vegetation condition related to water availability and droughts (NDVI vs. fAPAR, NPP, soil water content, excess water) are identified from remote sensing data (MODIS) and a process-based dynamic vegetation model (LPJ-GUESS) for at least two semi-arid river basins. Identified trends of both methods are compared and evaluated based on the underlying processes and related to knowledge of past drought events. Finally, we answer the question, which methods and indicators are suitable to identify changes in the vegetation condition preceding a drought and during drought phases considering the methods and indicators as above plus simple precipitation-based drought indicators (e.g. standardized precipitation index, SPI) and enhanced drought indicators applying multiple indicators theirselves (e.g. combined drought indicator, CDI). The study is imbedded in the SaWaM project (Seasonal Water Management for semi-arid areas) and contributes to improved water management in the project regions by the integrated analysis of remote sensing and ecosystem modelling results that are made available to regional stakeholders tasked with water management in an online tool .</p>

Sign in / Sign up

Export Citation Format

Share Document