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MASSIVE CHANGES IN TRADITIOnal land use IN oil consession block 5A

 

- The oil fields of Sudan / Southern Sudan -

 

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A multi-temporal study of the oil concession block 5A revealed massive changes in the traditional farming pattern since the introduction of oil industry.

 

 

 

Particular in the period between 1999-02 massive changes was shown from Landsat data which was recorded at the time of the year when the traditional farming are concentrated around the homesteads. The active farming from primarily harvesting activity and high livestock concentrations left a footprint that are known from range land farming systems. This natural resource utilization leaves a high reflectance which can be linked to the presence of people.  The pattern of change closely collaborated with massive reporting's of fighting and displacement of people in the area. 

 

 

Change in land use in the period 1999-04. During 2002 almost no land use activity (farming activity) could be observed around Nhialdiu. An increased in farming activity could, however be observed around Touc and south of. 

 

 

 

Change in farming activity in Block 5A from 1999 to spring 2003, during this period most observed farming activity was moved away from areas of the expanding oil industry.

 

 

Change in farming activity in the period 2000-2 and plot of village attacks (recorded from literature). Brown refers to areas which were observed as farming areas in 2000 but not in late 2002. Pale green observed farming areas in late 2002 which co-responded with reported refugee areas. 

 

 

Summery from peer-reviewed scientific publication in International Journal of Remote Sensing.

 Covering land use changes in two periods 1999-2002 and 2014-2015 Prins (2018):

 

During 2013-15 the 5a area was exposed to more humanitarian suffering. This was apparently watched by international organisations as a huge amount VHR data was available on Google Earth. This allowed a more comprehensive evaluation of the Landsat potential for mapping the footprint and changes in the agro-pastoral way of life.

Very High Resolution (VHR) satellite data was used to evaluate the prediction of farming activity, which was characterized like the description as in the Prins 2009 report (Table 1; Prins 2018). However, the lower bound of farming activity was set to 10% of a Landsat pixel, as the target was settlement areas and close surroundings and below this threshold interpretation of VHR data was considered to be linked with unacceptable uncertainties. 

 Table 1. Farming activity class definition and means to verification by VHR imagery.

The key Landsat data (table 2) was again chosen from early dry / harvest period where farming activity are concentrated around the homesteads - thus, if present, will show a footprint like monitoring the pioshere in rangeland management.

 

Table 2. Landsat data used in International Journal of Remote Sensing publication (Prins 2018) included two essential images from the Prins 2009 report. The images are requested in early dry season that is best for rangeland monitoring and in this case are when people have been gathered at homesteads for crop harvest.

The five Landsat images were calibrated to ToA and intercalibrated using invariant points, with a few exceptions reached a remarkably close intercalibration that allow multitemporal monitoring. Surface Reflectance (SR) that is a product that have under development over the past decade showed a similar result. SR is useful as it corrects for uneven atmosphere and can now be acquired pre-processed for free. The uneven atmosphere is typically affecting visible blue band, but Blue did not show channelling to calibrate for the time series analysis. This was found both in Prins (2018) (see table 3 below. table 4 in Prins 2018) and in the imagery shown in Stockholm 2013 presentation that were calibrated independently.

Table 3. Mean difference between spectral bands after inter-calibration (from Prins 2018).

The most successful farming activity predictor was the machine learning algorithm MaxEnt, which is traditionally used in ecology for species prediction (Phillips et al 2006). However, so far, the remote sensing community has only given it limited attention. In this case it outperformed all other traditional remote sensing classifiers. MaxEnt showed outstanding predictability and consistency across all years - apart from the NiR band. This could be expected as farming areas in this environment could include a range of factors that include bare areas, differences in soils, green vegetation, and moisture, which NiR is sensitive to.

 

Figure 1. AUC values [1]of individual Landsat bands produced by MaxEnt for each year and mean of all years including SD - apart from NiR band it shows remarkably high consistency and thus high reliability for multitemporal prediction and mapping.

A comprehensive analysis of classifications algorithms as well as spectral bands and indices was preformed from both the 2014 and 2015 image. The predictability of farming activity from MaxEnt showed very strong across indices.

The strongest response collaborated findings from recent years remote sensing research in rangeland management (fig 2). SWIR band 2 and furthermore indices of the Normalized Difference Tillage Index (NDTI), Normalized Difference Infrared Index 7 (NDII7) as well as the Soil Adjusted Total Vegetation Index (SATVI) showed strongest – they are considered state of art indices for rangeland management. However, Blue band and SWIR 2 showed for both test years to independently produce among the highest overall mapping accuracy > 97% (see Prins 2018 paper or indicated by the debated Kappa coefficient of approximately 0.85). MaxEnt using all bands and Tassel caps outperformed all other methods with overall mapping accuracy > 98.5% (Kappa 0.88-0.92). The ISO classifier produced overall mapping accuracy > 97.2% (Kappa 0.81-0.86) that outperformed most categorical classifiers. However, it should be noted that the uncertainties with ISO classifier was linked with commission errors. Or more specially it made the farming areas larger which indicates it grabbed farming activity below the 10% threshold used in the evaluation by interpreting VHR images -see below.

 

Figure 2. AUC values produced by MaxEnt for individual Landsat bands and indices show very high explaining ability, particular for indices and bands that have found useful for Rangeland management.

When the farming prediction from MaxEnt was put together as a standardized difference image (2014–2015), depopulated or change areas could be identified at both settlement and state levels. As shown in Figure 3 (b), there has been a significant decrease in farming activity along the main roads as well as in the east central part of the study area. This includes the Ngop area that has been documented by UNOSAT (2015) as burnt and the larger Boaw area that was reported to be severely terrorized and destroyed in 2015 (Aljazeera 2015; HRW 2015; UNOSAT 2016). Interpreting a no comprehensive mapping from Boaw area (Figure 3b) in Google Open Street Maps (2015) shows approximately 1500 housing structures and with average of 5 persons in a household (personal communication Nils Carstensen, Christian Aid), this can bring up realistic estimates of 10,000 people being driven off their land in the Boaw area in 2015. The Landsat change product shows a similar overall pattern as the UNOSAT (2016) VHR-based product of destroyed housing structures. However, it covers a much larger area, and allows identification of specific destroyed settlements (Figure 4). In addition to the massive disturbance east of Ngop and Boaw, the Landsat change analysis showed large scale increased farming intensity in and around major towns of Bentiu, Koch, and Leer. According to ground reports (HRW 2015; UNHRC 2016) , these areas have received high amount of displaced people as well as cattle in 2015. This is difficult to observe or derive from VHR imagery. UNOSAT (2016) uses direct observation of cattle which only represents a snapshot of the situation, compared to the Landsat analysis that captures the intensity of the farming activity that had occurred.

Therefore, Landsat data can be a better choice for capturing piosphere or anthropogenic activity than the use of VHR imagery. This has analogy to evaluation of burnt areas where interpretation of Landsat data can be a better choice than VHR imagery (Sparks et al. 2015). Overall, these results suggest that Landsat data not only can be an effective supplement to VHR imagery but a more effective choice to produce regional overviews that allow evaluation down to the settlement level. However, it can also retrieve essential information that is not captured or cannot be interpreted by using VHR imagery.

Producing a similar MaxEnt standardized difference image for the 1999–2002 data (see Figure (a)) uncovers a massive change in farming activity that closely collaborated Prins earlier work and ground reports (e.g., HRW 2003; de Guzman 2002 - see also hand drawn map herein; Christian Aid 2002) of massive human abuses that depopulated the Nhialdiu area in February 2002. Inserted in Figure 3 (a) are also attacked villages and refugee campsites that were geo-located from the reports. Furthermore, in transparent, the extent of the 2002 attack interpreted from reports and hand drawn maps (de Guzman 2002; Christian Aid 2002) that were reported depopulated in late 2002. This could again be shown by the MaxEnt change detection product (e.g., SD < −2.5) that inferred the decrease in farming activity for the entire area. Again, using Google Open Street maps un-comprehensive housing structure assessment from ap proximately 2013 (HOT 2016) that covered approximately two-thirds of the area and 14.450 housing structures. This indicates that numbers of approximately 100,000 people have been driven off their land for that area. Most people found refuge in the south (in green) that Christian Aid (2002) have assessed to receive approximately 50,000 people by late 2002. Inserted in Figure 3a is a zoom up along the all-weather road from where specific villages have been reported attacked and show no longer farming activities in 2002. 

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 Figure 3. Change detection (z-scores) of MaxEnt derived farming activity on a large scale (from Prins 2018), its detail and collaborate reports of massive displacement of people from two different periods and events (1999–2002 (a) and 2014–2014 (b)).

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Clip of farming change in the Ngop village area 2014-2015. Prins 2018 left (figure 3 b) and right VHR image from UNOSAT 2015 counting 250 destroyed structures – see more  https://unosat-maps.web.cern.ch/SS/CE20131218SSD/Ngop_UNOSAT_20150518.pdf

Figure 4. A typical settlement area recorded by VHR imagery in late 2014 (a), cattle’s, most white, can be seen in upper right part of the image. The same area recorded 7 months later into the wet season. The settlement was destroyed and rapidly being overgrown (b). The Landsat 2014–15 difference image (z-scores) of farming activity from MaxEnt shows a strong response to the change at settlement level. Photo credit (Google Earth).

 

Figure 5. Change detection of farming activity after harvest in 1999 and 2002. Based upon ISO classification of calibrated Landsat data (Prins 2018) recorded the 27. Nov. 1999 and 3. Nov. 2002. The ISO classification was performed with standard setting in Erdas Imagine - like the Prins 2009 report. The categorical classifier immediately grabs more of the homestead surroundings than using the supervised approach with a 10% minimum threshold. This suggest that it captures farming activity well below the 10% threshold set out in the VHR verification. In other words, the ISO classifier grabs the effect of farming activity that are difficult to account for in VHR images interpretations. This sensitivity is a plausible explanation for why it has for decades been considered as a stronger classifier than supervised classification to trace the effect of pastoral farming systems.

VHR imagery has a clear advantage over Landsat data of being able to check up the status of individual building structures at any time of the year if the cloud cover permits it. However, it can be difficult to access if people are present in an area from just the visual interpretation of VHR imagery using the blue, green and red-light area. In this case, the spatial and multispectral properties of Landsat data could show to traced farming activity beyond what could be clearly interpreted from VHR imagery. This can explain the commission errors (show to much) by the ISODATA classification that appeared to be rooted in farming activity below the 10% farming activity threshold used in this work. This assumption is supported by the categorical classification simply produces larger homestead areas than those predicted from the highly effective MaxEnt algorithm that has been entirely trained with higher levels of farming activity. I all cases the work confirms that ISODATA classification is a useful application for rapid tracing farming activity in range land environment – if it is used wisely. As with multispectral crop monitoring,  the limitation of the current method is a time window of few months from where it may work with high accuracy. Dryness will typically produce increased commission errors further into the dry season and ‘greening’ will produce omission error in the wet season. On the other hand, it has been noted (HRW 2003) that during the wet period, mobility in the area is decreased which limits the humanitarian abuses.


[1] In general, an AUC of 0.5 suggests no discrimination (i.e., ability to predict farming activity), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding.

 

 

 

Supplements:

 

PRINS KEY-NOTE SPEAKER AT THE SWEDISH SPACE DAYS 2013: Global reporting interpretation of speach

 

  pdf  PRESENTAION OF THE 5A CASE AND ANALYSIS - INVITED TALK REMOTE SENSING DAYS STOCKHOLM 8 APRIL 2013

pdf  REPORT TO ECOS 2009: Satellite mapping of land cover and use in relation to Oil exploitation in concession 5A

 

Client of the 2009 report: The European Coalition on Oil in Sudan (ECOS)

 

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